chapter 1

COMBINING FARMER DECISION MAKING WITH SYSTEMS MODELS FOR RESTORING MULTI-FUNCTIONAL ECOHYDROLOGICAL SYSTEMS IN DEGRADED CATCHMENTS

Justin Ryan (BA Hons I)

School of Geography, Planning and Architecture The University of Queensland Degree of Doctor of Philosophy

23rd November 2007

Ryan, J.G. (2007) PhD Thesis

This is a statement of the originality of the conceptual ideas, research methods, modelling, and findings contained in this thesis, are to the best of the candidates knowledge, original and the candidates own work, except where acknowledged in the text as co-authored publications, and the material has not been submitted, either in part of whole, for purposes related to a degree or award at this or any other university or research institution.

Justin Ryan (23rd November 2007)

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Acknowledgements I firstly wish to thank my funding body Land and Water Australia (LWA) for making this PhD research a reality, and particularly Dr Nick Schofield for he’s encouragement and support concerning complex systems science. Thanks also to UQ’s Post-Graduate School and Assoc Prof Stuart Phinn (Centre for Remote Sensing and Spatial Information Science) for their academic and financial support. A very warm thank you to the farmers of the Crows Nest, Emu Creek, Ravensbourne, and Rosalie North Landcare Groups for their time and assistance with the GLAMS activities. I especially wish to thank Roger Foxton, Frank Burgess, and Peter Hunter for their hospitality and friendship during my field visits, and Bruce Lord and Dr David Manning of the Western Catchments NRM Group for logistical support and perspectives on the Western Catchments. Thanks also to Crows Nest Shire Council for data support. Thank you Ron and Sue Watkins of Payneham Vale (Western Australia) for your hospitality and views on the ecosystems farming approach, and to the anonymous farmers who participated in my interviews. Thanks also to Dr Carl Smith for advice on Bayesian statistics, and to Jurgen Overheu and Alan Victor for their efforts to keep my IT environment from imploding. I would also like to thank DHI for the loan of MIKE SHE software, and particularly Graeme Cox (Gold Coast) for the ‘hands on’ learning experience and the numerous emails and phone calls concerning the world of hydrological modelling. A very special thankyou to Dr John Ludwig and Dr Clive McAlpine, my advisors, for their never ending support, friendship, and pertinent advice on so many aspects of my research and journal manuscripts. I wish to also thank Christine (Feisty) Fyfe for her enduring friendship and gentle, caring, honest and altruistic outlook. I wish we could all share at least some of your simple (yet complex) joy of ‘dirt’ and it’s creatures great yet small.

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Abstract In agricultural landscapes, native ecosystems are largely replaced by non-native crop and pasture species, with the extent of modification dependent on land use history and the management practices of current farmers. The result is greater complexity in some parts of the landscape, while other areas become more simplified. Of critical importance, however, is that many landscapes become ‘leaky’ due to the impaired feedback mechanisms between the types of vegetation present (i.e. land cover) and their ability to efficiently cycle water, sediment, nutrients and carbon between the lower atmosphere, vegetation, soil, and hydrological systems, causing landscapes to become ecohydrologically dysfunctional. In many mixed cropping-grazing landscapes of Australia, ecohydrologically dysfunctional landscapes exhibit declines in soil condition, water quality and quantity, rising salinity levels, higher wind speeds and temperatures, reduced rainfall, and a loss of biodiversity and production capacity. Ecohydrologically dysfunctional landscapes also are less resilient to climatic variations, which during extended dry periods, can result in water shortages with potentially dire socioeconomic consequences. This Thesis focusses on how landscape designs comprised of particular types and locations of land covers can improve and restore the ecohydrological functioning of mixed cropping-grazing landscapes. The specific aim of this Thesis is to show ‘proof of concept’ for the – development of a new modelling approach which automatically calculates the effects that land cover changes at specific locations across hillslopes have on ecohydrological systems functioning, and to use this information to delineate landscape designs that enhance water retention within the landscape while minimising sediment and nutrient export to the catchment. To support this aim, the Thesis has three major objectives: 1) develop a complex adaptive systems conceptual model of ecohydrological systems functioning within landscapes; 2) develop a participatory survey method to capture the expert knowledge of farmers in terms of preferences for a given land use and changes in these preferences during seasonal variations in rainfall; and 3) demonstrate ‘proof of concept’ that a combination of expert systems and hydrological process models can be employed to automatically delineate landscape designs which restore ecohydrological functioning within mixed cropping-grazing landscapes in sub-tropical Australia. The development of a rigorous theoretical basis of ecohydrological systems function within the landscape in terms of adaptations to environmental flux in climate and human forcings such as land cover change, was achieved by integrating the concepts of both complex adaptive systems and landscape ecology theories. The resulting framework, termed ‘Complex Adaptive Landscapes’ (CAL), derived six core tenets which described the system dynamics of a landscape: 1) a continuum of scales; 2) open systems; 3) non-linear feedback mechanisms; 4) aggregation of components; 5) self-organisation; and 6) multiple meta-stable states. The participatory survey method resulted in the development of the ‘Graphical Landscape Map Survey’ (GLAMS). This process applied three-dimensional representations of the landscape (i.e. Graphical Landscape Maps) in combination with Bayesian Belief Networks (BBNs) to capture farmer’s expert knowledge. GLAMS generated probability estimates (P) that highlighted the importance of ecohydrological functioning to farmers and the locations and change in land use through time in both average and extended dry seasons. Achievement of the last objective resulted in the ‘Landscape Ecohydrological Attenuation Configuration System (LEACS). This system utilised farmer decision rules as probability estimates (P) for a given land cover within the STELLA systems software, and then revised these P estimates depending on the magnitude of runoff from a hillslope following an intense thunderstorm event as calculated by a distributed hydrological process model (MIKE SHE). The fundamental dynamics of the LECAS model was based on iterative feedback between the outputs of water at iv


the end of the catchment and changing the spatial locations of particular land covers within the catchment over time. Together the three objectives highlighted the following major implications for natural resource and catchment management: i)

the CAL framework may be used to design both sampling and monitoring strategies in natural resource and catchment management. CAL suggested that monitoring timeframes should be decadal in time-frame, and the feedback mechanisms of landscape must be accounted for if the longer-term sustainability of human-modified landscapes is to be achieved. A basis to such complexity is likely to form around aggregated components such as native vegetation patches, and these in turn, are important for self-organisation of a desirable landscape state to be maintained through increased resilience to disturbances and climatic fluxes;

ii) participatory survey methods, such as GLAMS, are an excellent means to capture farmer expert knowledge in a manner that is intuitive to the farmers. The GLAMS approach accounted for any desired set of management actions put forward by a Landcare group or catchment body, differentiated between property sizes, and incorporated landscape heterogeneity in time and space. The framework helped to prioritise the level of support for a given set of NRM actions by farmers, including where activities were best placed. The approach also aided in identifying future landscape states based on a priori conditions and farmer preferences; and iii) the LEACS model demonstrated an approach capable of providing estimates of where it would be best to locate land cover changes (e.g. tree belts) to aid in the restoration of ecohydrological functioning in the landscape. A secondary outcome for simulations which tested the effects of tree belts in specific configurations was that these designs were an effective ecohydrological restoration technique that reduced water velocities and increased infiltration across steep hillslopes in the Maronghi Creek catchment, Southeast Queensland.

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Publications Ryan, J. G. and McAlpine, C. A. (2005). Simulating the feedback between land cover configuration and ecohydrological functioning in complex adaptive landscapes. In: Proceedings of the MODSIM 2005 International Congress on Modelling and Simulation: Advances and Applications for Management and Decision Making, Melbourne. Modelling and Simulation Society of Australia and New Zealand: 758-764. http://www.mssanz.org.au/modsim05/papers/ryan.pdf Ryan, J. G., McAlpine, C. A. and Ludwig, J. A. (2007). GLAMS: a graphical method for capturing land and water management practices in agroecosystems. Ecosystems 10 (3): 432-447. Ryan, J. G., Ludwig, J. A. and McAlpine, C. A. (2007). Complex adaptive landscapes (CAL): a conceptual framework of multi-functional, non-linear ecohydrological feedback systems. Ecological Complexity 4 (3): 113-127.

Statement of Contributions I witness that Justin Ryan having been advised by me over the entire period of the PhD candidacy has contributed the majority of conceptual material found in each of the publications contained within this thesis, and has researched, written, and edited all materials contained within those publications. They are original works developed by Justin Ryan for the satisfaction of the award of Doctor of Philosophy.

Signed, Dr Clive McAlpine 23rd November 2007

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Table of Contents CHAPTER 1 - INTRODUCTION ....................................................................................................... 1 1.1

Background to the Problem .......................................................................................... 1

1.1.1 What are Ecohydrological Systems? ................................................................................................. 1 1.1.2 What are the main Drivers and Functions of Ecohydrological Systems? ......................................... 2 1.1.3 How Land Use affects Ecohydrological Systems ............................................................................. 3

1.2

Research Problem Statement ........................................................................................ 5

1.3

Research Aims .............................................................................................................. 6

1.4

Summary of Research Objectives ................................................................................ 7

1.5

Detailed Research Objectives ....................................................................................... 7

1.5.1 How Might Ecohydrological Systems Functioning be Restored in Modified Landscapes? ............. 7 1.5.2 Objective 1 – Developing a Conceptual Model of Ecohydrological Systems Functioning Based on a Complex Adaptive Systems Framework.................................................................................... 8 1.5.3 Objective 2 – Developing a Participatory Survey to Capture Farmers Land Use Management Behaviours (Expert Knowledge) .......................................................................................... 10 1.5.4 Objective 3 - Combining Expert Knowledge with Process Models to Develop Ecohydrological Restoration Designs ........................................................................................................ 11

1.6

Thesis Chapter Outline ............................................................................................... 12

CHAPTER 2 - COMPLEX ADAPTIVE LANDSCAPES (CAL): A FRAMEWORK AND EXAMPLES BASED ON MULTI-FUNCTIONAL, NON-LINEAR ECOHYDROLOGICAL FEEDBACK SYSTEMS .................. 13

2.1

Introduction ................................................................................................................ 14

2.2

Complex Adaptive Landscapes – A Framework ........................................................ 16

2.2.1 Examples of Landscape Ecological and CAS Research Applications ............................................ 16 2.2.2 The CAL Framework ...................................................................................................................... 17 2.2.3 Core Tenets of the CAL Framework ............................................................................................... 21 2.2.3.1

A Continuum of Scales ........................................................................................................................ 22

2.2.3.2

Open Gradients .................................................................................................................................... 24

2.2.3.3

Multiple Component Types .................................................................................................................. 26

2.2.3.4

Interactions and Non-linear Feedback Mechanisms ............................................................................. 28

2.2.3.5

Aggregation and Self-Organisation ...................................................................................................... 31

2.2.3.6

Multiple States and Transitions ............................................................................................................ 34

2.3

A Synthesis of CAL Tenets for Ecohydrological Management ................................. 37

2.4

Acknowledgements .................................................................................................... 39

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CHAPTER 3 - GLAMS - A GRAPHICAL METHOD FOR CAPTURING LAND AND WATER MANAGEMENT PRACTICES IN AGROECOSYSTEMS ................................................................... 41 3.1

Introduction ................................................................................................................ 42

3.2

Method........................................................................................................................ 45

3.2.1 Case Study Area .............................................................................................................................. 45 3.2.2 Survey Participants ......................................................................................................................... 46 3.2.3 The GLAMS Method ...................................................................................................................... 47 3.2.4 Post-Survey Data Analyses ............................................................................................................. 51

3.3

Results ........................................................................................................................ 53

3.3.1 Land Use Positions within the Landscape ...................................................................................... 53 3.3.2 Land Use Changes and Timeframes ............................................................................................... 55 3.3.3 Ecohydrological Risks .................................................................................................................... 59

3.4

Discussion .................................................................................................................. 62

3.4.1 Benefits and Limitations of GLAMS .............................................................................................. 62 3.4.2 Specific Findings from GLAMS ..................................................................................................... 63

3.5

Conclusions ................................................................................................................ 67

3.6

Acknowledgements .................................................................................................... 68

CHAPTER 4 - FORMULATION & TESTING OF A LANDSCAPE ECOHYDROLOGICAL ATTENUATION CONFIGURATION SYSTEM (LEACS) ........................................................................................ 69 4.1

Introduction ................................................................................................................ 69

4.1.1 Why Ecohydrological Restoration? ................................................................................................ 69 4.1.2 The Effects of Plant Functional Type on Ecohydrological Systems ............................................... 70 4.1.3 Hydrological Responses to Changes in Native Vegetation Cover .................................................. 71 4.1.4 Issues with Restoring Ecohydrological Functioning Using Native Vegetation .............................. 72 4.1.5 Existing Modelling Approaches and their Limitations ................................................................... 74 4.1.6 An Automated Approach to Redesigning Leaky Landscapes - LEACS ......................................... 76

4.2

Method........................................................................................................................ 78

4.2.1 Case Study Area: Maronghi Creek Catchment ............................................................................... 78 4.2.1.1

Physiography and Climate ................................................................................................................... 78

4.2.1.2

Geology, Lithology and Soils ............................................................................................................... 79

4.2.1.3

Vegetation and Land Use ..................................................................................................................... 80

4.2.2 Phase I: Development of the LEACS Model .................................................................................. 84 4.2.2.1

Definition and Logic of LEACS .......................................................................................................... 84

4.2.2.2

Development and Testing of LEACS Equations and Logical Statements ............................................ 86

4.2.3 Phase II: Generating Hydrological Simulation Model Outputs ...................................................... 88 4.2.3.1

Selection of Water Balance Model ....................................................................................................... 88

4.2.3.2

Basis of MIKE SHE Algorithms .......................................................................................................... 89

4.2.3.3

ArcGIS Data Preparation ..................................................................................................................... 92

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4.2.3.4

4.3

Simulation Parameters.......................................................................................................................... 93

Results ........................................................................................................................ 96

4.3.1 Phase I: LEACS as a Stella Model.................................................................................................. 96 4.3.1.1

General Response of P Values to Performance Measure Error (Pme) .................................................. 96

4.3.1.2

Sensitivity of P Value to Changes in Pm Values .................................................................................. 97

4.3.1.3

Sensitivity of P Value to Changes in the Tuner .................................................................................... 97

4.3.1.4

Sensitivity of P Value to a Change in RecPm ...................................................................................... 97

4.3.2 Phase II – Tree Belt Simulations ................................................................................................... 100

4.4

4.3.2.1

Overview ............................................................................................................................................ 100

4.3.2.2

Water Balance .................................................................................................................................... 100

4.3.2.3

Depth of Overland Flow ..................................................................................................................... 100

4.3.2.4

Water Velocity ................................................................................................................................... 104

4.3.2.5

Infiltration to the Unsaturated Zone (UZ) .......................................................................................... 105

4.3.2.6

Evapotranspiration Rates.................................................................................................................... 106

Discussion ................................................................................................................ 107

4.4.1 Phase I: LEACS STELLA Model ................................................................................................. 107 4.4.1.1

General Performance .......................................................................................................................... 107

4.4.1.2

Potential for Linking LEACS Phase I and II ...................................................................................... 108

4.4.1.3

Suggested Improvements to Stella Model .......................................................................................... 109

4.4.2 Phase II: Simulated Affects of Tree Belts on Ecohydrological Functioning of Hillslopes ........... 110

4.5

4.4.2.1

Overall Scope ..................................................................................................................................... 110

4.4.2.2

Implications from Phase II ................................................................................................................. 110

4.4.2.3

Water Balance .................................................................................................................................... 111

4.4.2.4

Water Velocity and Erosivity ............................................................................................................. 113

4.4.2.5

Changes in Infiltration........................................................................................................................ 115

4.4.2.6

Evapotranspiration ............................................................................................................................. 116

Conclusion ................................................................................................................ 117

CHAPTER 5 - DISCUSSION AND CONCLUSION ........................................................................... 119 5.1

Chapter Overview..................................................................................................... 119

5.2

Objective 1 – Complex Adaptive Systems Framework for Ecohydrological

Systems .............................................................................................................................. 119 5.2.1 Achievement and Major Outcomes ............................................................................................... 119 5.2.2 Implications for Natural Resource and Catchment Management.................................................. 121 5.2.2.1

A Continuum of Scales ...................................................................................................................... 121

5.2.2.2

Open Gradients .................................................................................................................................. 122

5.2.2.3

Multiple and Diverse Sets of Components ......................................................................................... 123

5.2.2.4

Interactions and Non-linear Feedback Mechanisms ........................................................................... 123

5.2.2.5

Aggregation and Self-Organisation .................................................................................................... 125

5.2.2.6

Multiple States and Transitions .......................................................................................................... 126

5.2.3 Summary of Future Research ........................................................................................................ 127

5.3

Objective 2 - GLAMS .............................................................................................. 127 ix


5.3.1 Achievement and Major Outcomes ............................................................................................... 127 5.3.2 Major Implications for NRM and Catchment Management ......................................................... 129 5.3.3 Summary of Future Research ........................................................................................................ 130

5.4

Objective 3 - The LEACS Model ............................................................................. 131

5.4.1 Achievement and Major Outcomes ............................................................................................... 131 5.4.1.1

Phase I – LEACS STELLA ................................................................................................................ 131

5.4.1.2

Phase II – LEACS MIKE SHE........................................................................................................... 133

5.4.2 Implications for Natural Resource and Catchment Management.................................................. 135 5.4.3 Summary of Future Research ........................................................................................................ 137

5.5

Conclusion ................................................................................................................ 137

REFERENCES ............................................................................................................................ 139

APPENDIX A: OUTPUTS FROM A SERIES OF LEACS STELLA RUNS....................................... 181

APPENDIX B: SUMMARY OF HYDROLOGICAL SIMULATION MODEL PARAMETER ESTIMATES .... 189

APPENDIX C. AN EXAMPLE OF THE TYPE OF THUNDERSTORM SIMULATED FOR THE MARONGHI CREEK CATCHMENT .............................................................................................................. 190

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List of Figures Figure 1.1. The major ecohydrological functions operating in production landscapes Figure 2.1. The complex adaptive landscape (CAL) framework as a simple state and transition diagram Figure 2.2. CAL as a schematic diagram illustrating landscape complexity as multiple levels Figure 3.1. The study area boundaries within the Western Catchments of the Upper Brisbane River Figure 3.2. An example of the GLAMS physical template showing local lithology Figure 3.3. An example of the GLAMS physical template showing native ecosystems Figure 3.4. Probabilities for different land uses in specific landscape positions Figure 3.5. Probabilities for a land use change from cropping Figure 3.6. Probabilities for a land use change from grazing in a native forest Figure 3.7. The difference between perspectives on the importance of ecohydrological risks Figure 3.8. The general relationship between property size and land use management Figure 4.1. The location of the Maronghi Creek catchment Figure 4.2. The physiography of the Maronghi Creek catchment Figure 4.3. Current remnant vegetation and tree belt configuration for the Maronghi Creek sub-catchment Figure 4.4. A native patch of grassy open eucalypt woodland found in the study area Figure 4.5. Steep hillslopes of granitic soils cleared for grazing on improved pastures Figure 4.6. A combination of a sand slug with high nutrient loads in Maronghi Creek Figure 4.7. Creek bank erosion and mass failure Figure 4.8. Schematic of the LEACS model with MIKE SHE simulation and GIS data integration Box 4.1. LEACS model performance measure error and land cover change equations Figure 4.9. The STELLA systems model of LEACS Figure 4.10. A simplified conceptual model of water flow and partitioning in MIKE SHE Figure 4.11. Rain rate (mm/hr) for Cressbrook Dam Jan-Feb 1999 Figure 4.12. The response of LEACS to an array of performance measure values Figure 4.13. The response of LEACS to changes to Pm values Figure 4.14. The sensitivity of LEACS to changes in the Tuner value Figure 4.15. Changes in LEACS model behaviour where the EOC target value is altered Figure 4.16. The sub-catchment water balance for the current pasture based simulation run Figure 4.17. The sub-catchment water balance for the tree belt simulation run Figure 4.18. Time-series of stormwater depth for pasture based simulation run Figure 4.19. Time-series of stormwater depth for tree belt simulation run Figure 4.20. Maximum flow velocities in pasture based simulation runs Figure 4.21. Maximum flow velocities in tree belt based simulation runs Figure 4.22. The difference between soil profile recharge rates Figure 4.23. The difference between evapotranspiration rates

2 19 20 45 47 48 54 57 58 60 65 78 79 81 82 82 83 83 85 86 87 92 95 98 98 99 99 101 101 102 103 104 104 105 106

List of Tables Table 2.1. The six core tenets of the CAL framework Table 3.1. The set of questions which participants used to complete GLAMS Table 3.2. Land use and ecohydrological codes used in GLAMS Table 4.1. Climate statistics for Crows Nest and Cressbrook Dam weather stations Table 4.2. Parameter estimates (initial) used for the Maronghi Creek simulation by MIKE SHE

21 49 50 79 94

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List of Main Abbreviations

BBN – Bayesian Belief Network CAS – complex adaptive systems CAL – complex adaptive landscapes EOC – end of catchment target GLAMS – graphical landscape map survey LEACS – landscape ecohydrological attenuation configuration system NRM – natural resource management PFT – plant functional type SPAC – soil-plant-atmosphere continuum

We are still square farming in a round world (Ron Watkins pers comm. 2004).

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Ryan, J.G. (2007) PhD Thesis - Chapter 1, Introduction

CHAPTER 1 INTRODUCTION

1.1 Background to the Problem 1.1.1 What are Ecohydrological Systems? Ecohydrology has been defined as many things depending on the objectives of the research. In this thesis, ecohydrological systems are defined as –

the reciprocal non-linear feedback mechanisms between specific vegetation assemblages and their configuration in the landscape and the interception, partitioning and distribution of water in atmospheric, soil, and fluvial systems.

This definition is based on studies by researchers from fields of terrestrial and aquatic ecology and hydrology conducted between catchment and hillslope scales. Wassen and Grootjans (1996), for example, defined ecohydrology as the hydrological aspects of ecology with a particular emphasis on the development of wetlands, which was further expanded by Baird (1999) to include how hydrological processes affect plant growth in any terrestrial system. Zalewski (2000) suggests that ecohydrology is the study of the functional interrelations between hydrology and biota at the catchment scale, while Eamus et al. (2006) state that it concerns the linkages and reciprocal exchange between the structure and function of vegetation and the movement and storage of water in the environment. The major functions of ecohydrological systems found between hillslope and catchment scales that are referred to in this Thesis are illustrated in Figure 1.1.

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partitioning of solar insolation flux to latent heat/sensible heat

evapotranspiration

shading from radiation

interception of rainfall

enhancing convection and cumulus formation

lowering wind speed

enhanced water infiltration capacity

slope stabilisation

slowing run-off

prevention of soil erosion

water harvesting

filtering sediment/ nutrients contour agroforestry

water quality & quantity for farm dams prevention of dryland salinity

Figure 1.1. The major ecohydrological functions operating in production landscapes.

1.1.2 What are the Main Drivers and Functions of Ecohydrological Systems? In structurally intact landscapes comprised of native vegetation assemblages, interactions between climate, geomorphology and hydrology are the primary influences on the structural and functional heterogeneity of the landscape (Moore et al. 1991; Cleland et al. 1994; Catterall et al. 2001). Within these landscapes the composition, structure and location of native vegetation, in turn, display adaptive functional mechanisms to hydro-geomorphic processes through non-linear feedback mechanisms which regulate the flux, interception, flows, and storage of water, sediments, nutrients and carbon at differing rates and within differing timeframes (DeAngelis et al. 1989; Florinsky and Kuryakova 1996; Wondzell et al.

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1996; Wilby and Schimel 1999; Carpenter et al. 2001; Dirnbock et al. 2002; Gibon 2005; Ludwig et al. 2005). These feedback mechanisms are dependent on the type of vegetation species within the landscape as well as their spatial configuration, leading to heterogeneous ecohydrological functioning in terms of the interception, filtering, uptake, processing, storage and subsequent transport of water, sediments and nutrients (Ludwig et al. 1999a; Ludwig et al. 1999b). Landscape composition and spatial configuration also influence wind directions and speeds, humidity, air temperatures, localised turbulence and convection, and the formation of cumulus (Lyons et al. 1996; Hayden 1998; Saunders et al. 1998; Ray et al. 2003; Baldocchi et al. 2004). The interactions between vegetative, edaphic, hydrological and climatic systems at hill-slope scales, results in complex patterns in soil development and moisture status and subsequently the emergence of heterogeneity in native vegetation across the landscape. While natural exogenous disturbance regimes modify these interactions over time, seemingly redundant species fulfil important functions when environmental conditions change following disturbances (Williams and Saunders 2005).

1.1.3 How Land Use affects Ecohydrological Systems While exogenous disturbance is incorporated as a normal part of functioning in intact landscapes, within human-modified landscapes, there are often large departures from historical functioning (Baker 1995; McIntyre and Hobbs 1999; Ernoult et al. 2003). In modified landscapes, many native vegetation assemblages are replaced with non-native species that are not entirely suited to the influx of energy and material into these new agroecosystems (Freebairn and King 2003; Williams and Saunders 2005). As land use modifies and fragments native vegetation, concurrent structural and functional changes in turn affect resource flows and partitioning across the landscape (Saunders et al. 1991; Hobbs 1993; McAlpine and Loyn 2000; Foley et al. 2005). Modified landscape structure, such as the

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matrix of remnant native vegetation patches following land clearing, may give rise to new and dysfunctional landscape states (Noy-Meir 1973; Westoby et al. 1989). In other words, human land use causes structural and functional heterogeneity to be markedly different from that found in structurally intact landscapes, and often much less resilient to environmental flux. The occurrence of dysfunctional ecohydrological systems implies a mismatch between landscapes structure and functioning, such as the type, physiology, percentage cover and location of different vegetation within the catchment, and the reciprocal effects on water, sediment, nutrient and carbon storage and recycling mechanisms. Symptoms of ecohydrologically dysfunctional landscapes include: altered (often reduced) precipitation budgets in terms of distribution and magnitude from microclimate to regional scales (Charney 1975; Mylne and Rowntree 1992; Eltahir and Bras 1996; Boulter et al. 2000; Ray et al. 2003; Walker 2003; Pitman et al. 2004; McAlpine et al. 2007); modified surface hydrological regimes (Ludwig et al. 1999a; Loch 2000; Ludwig and Tongway 2002), groundwater recharge and dryland salinity (Wilson et al. 2000); changes to soil macro-invertebrate species composition and nutrient cycling (Fragoso et al. 1997; Rossi and Blanchart 2005); soil structural degradation and erosion (Freebairn 1998; Main 2003); as well as the subsequent altered habitat conditions for vertebrate fauna (Bennett 1999; Ernoult et al. 2003). The spatial configuration of land use can amplify this mismatch and cause new landscape states (large departures from historical conditions) to emerge that reflect disaggregation and a net loss of connectedness, resulting in ecohydrologically ‘leaky’ systems (DeAngelis et al. 1989; Ludwig and Tongway 2002; Williams and Gascoigne 2003). These same modifications also have the ability to alter atmospheric circulation and precipitation budgets in terms of distribution and magnitude, which may explain some of the regional-scale declines in rainfall across many parts of Australia (Charney 1975; Mylne and Rowntree 1992; Eltahir and Bras 1996; Ray et al. 2003; Walker 2003; McAlpine et al. 2007).

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While it is important to understand the complex interactions of hydrological and biogeochemical cycles with respect to the anthropogenic impact on the environment (Zalewski et al. 1997), the complex functions that native vegetation provides through physiological interactions (feedbacks) to the soil-plant-hydrology-atmosphere continuum is often not fully appreciated. The complexity and non-linearity involved between structure and functioning is often manifested as ‘time-lags’ between management actions (cause) and landscape dysfunction (response) (Patten and Odum 1981; Wu and Loucks 1995; Hartvigsen et al. 1998). This is an important perceptional challenge for natural resource management (NRM) to acknowledge - the time-frames and spatial scales of human land use change seem very small to be able to affect ecohydrological and climatic systems, so when changes do occur years later, how do we know what caused them? The progressive and cumulative impact of land cover changes over the Australian continent for the past 150 years is suggested to have had a long-term feedback to the climate which has resulted in increased temperatures and reduced rainfall for many regions (McAlpine et al. 2007). If we ignore these long-term feedbacks between land cover and climate change, addressing resilience of agricultural at a local scales is not meaningful. In other words, focusing on single issues over short management timeframes may lead to an incremental erosion of the resilience of landscape systems, which in turn, reduces future management options (Anderies et al. 2006).

1.2 Research Problem Statement Ecohydrologically dysfunctional landscapes are less resilient to climatic variations, have lower stores, flows and quality of water, and suffer land degradation which consequently results in lower productive capacity. An important question, therefore, is how to improve the ecohydrological functioning of landscapes that are shaped by human land use/cover change while being economically viable for farmers to implement. As only small proportions of the 5


catchment are likely to contribute to most of the sediment and nutrient loads (Prosser et al. 2003), a pertinent NRM question is where to locate land cover changes on a hillslope that will subsequently result in the greatest ratio of sediment/nutrient reduction to the catchment for each patch of a land cover changed. While the ability to trace back sources of stormwater runoff, sediment or nutrient loads to specific hillslopes is difficult with many regional scale or ‘lumped’ models, an alternative class of distributed parameter models can solve physical equations for a set of hillslope-scale ‘grid-based’ variables (Aral and Gunduz 2006). Testing the hydrological response of any number of possible alternative land cover configurations, however, still relies on user generated design options and appropriate changes to a land cover variable (grid) before each simulation run. When testing numerous possible alternative land cover configurations this quickly becomes intractable. This problem, theoretically, may be addressed by a constrained optimisation process where outputs from a hydrological simulation modify a set of land use preferences based on the magnitude of ‘leakiness’ for a single patch of land cover (i.e. grid cell). These land cover changes are then used in the next simulation iteration. A process where farmer land use preferences are incorporated into the development of ecohydrological restoration designs, may enable more realistic constraints to be applied to designs derived purely from physical hydrological simulations of hillslope responses.

1.3 Research Aims The primary aim of this Thesis is: i) to show ‘proof of concept’ of the potential to frame the problem of ecohydrologically dysfunctional landscapes in terms of a complex adaptive systems framework, and ii) to test whether ‘feedback’ between simulated land use changes in a distributed parameter hydrological model can be used to update farmer preferences stored in a separate systems model, in order to automate the process of changing the land cover from pasture to trees at locations (100 m2 cell) which are excessively ‘leaky’. A 6


secondary aim is to clarify the ecohydrological restoration potential of tree belts to reduce stormwater velocities, increase infiltration, and redistribute moisture across grazed hillslopes in the Maronghi Creek sub-catchment, Southeast Queensland.

1.4 Summary of Research Objectives i)

Develop a conceptual model of ecohydrological systems framed in the context of a Complex Adaptive System, with humans being included explicitly as an agent of change which cause various stochastic disturbances through time;

ii)

Develop a participatory survey approach to capture farmers decision making preferences for allocating land use during average seasons and extended dry periods to spatially and temporally explicit probability estimates;

iii)

Show proof of concept for: a. benefits of establishing a link between a systems model of land use preferences with the outputs from a distributed parameter hydrological simulation model; b. the ability of tree belts to reduce the depths and velocities of stormwater runoff through the redistribution of flows across hillslopes.

1.5 Detailed Research Objectives 1.5.1 How Might Ecohydrological Systems Functioning be Restored in Modified Landscapes? Due to the potential of native vegetation to provide multiple ecohydrological functions within modified landscapes, and the ability of many native species to regenerate after grazing or cropping pressures are removed (i.e. regrowth), native vegetation becomes one of the most economical management options we have at our disposal to enhance the functioning of ecohydrological systems (Farrington and Salama 1996). Specific examples include measures that buffer the impacts of extended dry periods (i.e. drought), reduce wind speeds and 7


temperatures, and attenuate the effects of intense thunderstorms by reducing the velocity (m/s1

) and magnitudes (peak discharge) of stormwater runoff to prevent sediment/nutrient

transport (see Figure 4.6) and stream bank erosion and collapse (see Figure 4.7). Catchment boards and NRM groups throughout Australia need to determine what type of landscape structures should be restored, what these may entail in terms of types of vegetation species to use, where to locate them, and how to manage them through time (Cullen 2004; SEQWCG 2004). This is a markedly difficult task due to the scaling nature of landscapes, non-linearity in variables that manifest as time-lags between cause and response, and possibility of multiple-meta stable landscape states (Gunderson et al. 2002; Wu and Li 2006). The following objectives aim to capture and simplify the complexity involved in ecohydrological systems, including disturbances and changes applied through human interactions, and utilise this knowledge to develop simulations of how land cover at hillslope scales may be changed to reduce ‘leakiness’ following heavy thunderstorms.

1.5.2 Objective 1 – Developing a Conceptual Model of Ecohydrological Systems Functioning Based on a Complex Adaptive Systems Framework Many of the NRM and catchment management problems that we currently face stem from a similar basis – a failure to recognise or actively manage for the inherent feedbacks that arise from the complex links between landscape structure and functioning across multiple spatial and temporal scales (Williams and Gascoigne 2003). When we do recognise that these systems are shaped and modified by both their own and other system’s behaviours, and that the interactions change in both space and time, we are often overwhelmed by the sheer complexity that confronts us. There are, however, approaches that are ideally suited to frame these type of systems. These are related to the Complex Adaptive Systems (CAS) paradigm (Holland 1992; Hartvigsen et al. 1998; Levin 1998), and are particularly useful for the

8


development of frameworks for modelling complex, non-linear functions that operate through feedback mechanisms across multiple spatial and temporal scales within the landscape. Complexity, according to Standish (2001), is a ubiquitous property of the universe that the field of CAS attempts to define. There is a growing body of evidence that many natural and human modified systems are organised as an open, yet complex, web of interconnecting components in space and time (Itami 1994; Simon 1996; Goldspink and Kay 2003). CAS are often described as bottom-up distributed non-linear systems comprised of a high number of components which interact within imposed top-down constraints, to provide the conditions necessary for the emergence of unique and often surprising phenomena at other times or locations (Holling 1986; Holland 1998). The notion of ‘teleconnections’ between local changes in land cover on the Earth and the subsequent changes in atmospheric processes at regional or synoptic scales, highlights an example of CAS dynamics (Rind 1999; Gedney and Valdes 2000; Avissar et al. 2006). A first step toward generating specific designs to address dysfunctional ecohydrological systems in modified landscapes, is to capture these complex interactions and non-linear feedback mechanisms in a simple but sound conceptual framework. The first objective of this Thesis is to summarise an extensive literature review on the principles and mechanisms associated with ‘complex adaptive systems’ to a set of six core tenets, and apply these tenets to landscape and ecohydrological systems structure and functioning. This is termed the Complex Adaptive Landscapes (CAL) framework. The CAL framework allows the ‘mapping’ of interactions at finer-scale hillslope levels to broader landscape levels and outcomes, such as how modifying land cover (i.e. plant functional types) in differing parts of the landscape causes ecohydrologically dysfunctional states to arise at other places or times.

9


1.5.3 Objective 2 – Developing a Participatory Survey to Capture Farmers Land Use Management Behaviours (Expert Knowledge) A fundamental precursor to achieve better ecohydrological management, is to generate knowledge at scales ranging between hillslopes to catchments or regions (Cullen 2004). As ecohydrologically dysfunctional landscapes often arise from either a direct or indirect result of land cover change, the people who manage land use change, the farmers, must also form part of the generation of knowledge. The term ‘expert knowledge’ conveys the ways in which people understand and manage a particular landscape (Kropff et al. 2001; Heathwaite 2003), while participatory processes are a means to actively capture farmers needs and motivations in ways which may then be translated to practical management solutions at the farm scale (Carberry 2001; Ridley 2004). In recent decades, both farmers and research/extension organisations have increased the adoption of farming systems approaches that integrate ecological, economical, and social objectives for improving short and long-term prospects for achieving sustainable land use (Gibon et al. 1999; Kropff et al. 2001; Gibon 2005). Objective 2 of this Thesis is to capture the accumulated experience from successive generations of farmers (i.e. expert knowledge) through a participatory survey, and to use this information to generate probability estimates (P) of land cover being applied at specific locations within the landscape and changes in these land covers at specific times as rainfall become increasingly scarce (i.e. extended dry periods). An new approach based on the Graphical Landscape Map Survey (GLAMS) is developed in a two-stage process: 1) a graphic survey method to capture land use applications across different sized farms at specific times and places; and 2) a analytical method based on Bayesian Belief Networks (BBNs) to derive probability estimates from the raw data obtained from the graphical surveys.

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1.5.4 Objective 3 - Combining Expert Knowledge with Process Models to Develop Ecohydrological Restoration Designs It is reasonably well understood that changes in land cover also modifies the water balance of landscapes (Gordon et al. 2003). Non-native plant species often fail to perform the ecohydrological functions associated with native vegetation (Williams and Saunders 2005), which have typically evolved as a result from adaptations to low nutrient soils and regular intervals of well below average rainfall (Cullen 2005). The need to find landscape designs that restore ecohydrological functioning has been highlighted by many researchers and land and water resource managers over the past few decades (Brizga and Finlayson 2000b; Williams and Saunders 2005). While the focus must be on areas where restoration can have the greatest benefit (Prosser et al. 2003), a pertinent question is then how to approach ecohydrological restoration within the economic constraints of agricultural production. This reflects a trade-off between land use preferences and values of ecohydrological functioning. Objective 3 of this Thesis is twofold: 1) to show proof of concept that farmer land use preferences can be used to constrain the land cover changes within each 100 m2 cell in a simulated grid, based on ‘feedback’ such as water velocity outputs estimated by a hydrological simulation model; and 2) to simulate the effects of tree belts on stormwater velocity, depths, infiltration and spatial redistribution. The entire process is termed the Landscape Ecohydrological Attenuation Configuration System (LEACS), but it is based on two distinct Phases: i)

Phase I – a prototype systems model for modifying farmer values (i.e. P values) for a given land cover in response to a hypothetical set of sediment transport data;

ii)

Phase II – a hydrological simulation of the ecohydrological effects of tree belts on stormwater depth, velocity, infiltration and evapotranspiration across steep hillslopes comprised of grazing land uses in the Maronghi Creek catchment near Crows Nest, Queensland.

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Phase I - A systems model is developed using STELLA software developed by ISEE Systems Inc. (ISEE 2005) is used to calculate the change in farmer preference values (P) for two land covers (pasture and tree belts) depending on the magnitude of outputs relative to a hypothesised ‘end of catchment’ (EOC) target for maximum sediments/nutrients loads (tn/Ha/Yr-1). To test the patterns of change in farmer preference values, hypothetical data arrays are used and responses graphed as a performance indicator. LEACS Phase II uses the MIKE SHE model (DHI 2005) to assess the effects of tree belts on hillslope water velocity, infiltration and redistribution using a distributed parameter hydrological simulation model.

1.6 Thesis Chapter Outline i)

Chapter 2 – Complex Adaptive Landscapes (CAL): a framework and examples based on multi-functional, non-linear ecohydrological feedback systems.

ii)

Chapter 3 – GLAMS: A graphical method for capturing land and water management practices in agroecosystems.

iii)

Chapter 4 – Development of the Landscape Ecohydrological Attenuation Configuration System (LEACS).

iv)

Chapter 5 – Discussion and Conclusion.

v)

References.

vi)

Appendix A. – LEACS Stella outputs.

vii)

Appendix B. - Summary of hydrological simulation model parameter estimates.

viii)

Appendix C. – Example of a severe thunderstorm over the Maronghi Creek catchment.

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Ryan, J.G. (2007) PhD Thesis - Chapter 2, CAL

CHAPTER 2 COMPLEX ADAPTIVE LANDSCAPES (CAL): A FRAMEWORK AND EXAMPLES BASED ON MULTI-FUNCTIONAL, NON-LINEAR ECOHYDROLOGICAL FEEDBACK SYSTEMS

This chapter is a paper published in the international journal Ecological Complexity. The content is original, and as lead author, is predominantly written by myself. The following should be used as the appropriate reference: Ryan, J. G., Ludwig, J. A. and McAlpine, C. A. (2007). Complex adaptive landscapes (CAL): a conceptual framework of multi-functional, non-linear ecohydrological feedback systems. Ecological Complexity 4 (3): 113-127.

Abstract Landscape ecology and complex adaptive systems (CAS) research provide numerous examples of systems with complex non-linear feedbacks, but our understanding of these systems is severely limited by a lack of conceptual frameworks built on these foundations. Here, we develop a conceptual framework by combining CAS organisation with landscape structure, functioning and change. The resulting framework, ‘Complex Adaptive Landscapes’ (CAL), explicitly captures the reciprocal feedbacks and non-linear nature of interactions between components within and between system levels, and the consequent possibility of multiple functional states (alternate systems functioning). The CAL framework highlights six core tenets that describe landscape complexity and dynamics. CAL provides examples of how the complex ecohydrological interactions at finerscale hillslope levels manifest changes to broader landscape levels, as well as multi-temporal feedbacks and change (days to decades). Understanding the specific feedback and non-linear responses of different components of the landscape, such as plant functional types, are of paramount importance for adequately designing monitoring and analytical frameworks for adaptive natural resource management. The CAL framework allows us to better understand the scale of ecohydrological functions within the landscape, and how substituted component types and their spatial and temporal configuration may cause dysfunctional states to arise as a result of human land use.

Keywords landscape complexity; ecohydrological functioning; complex adaptive systems; nonlinear feedbacks; aggregation; self-organisation; multiple landscape states.

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2.1

Introduction Formal definitions of landscapes are often based around structure, function and

change. Patterns in observable components lead to spatial structures based on non-linear interactions between physical gradients and organisms (including humans) (Forman and Godron 1986; Begon et al. 1996; Shugart 1998). Landscape function, defined as the flow of energy, material and species, is dependent on landscape structure such that changes in landscape structure have reciprocal effects on landscape function (Forman and Godron 1986; McIntyre et al. 1996). While spatial heterogeneity is a fundamental property of all landscapes that includes patchiness observable at multiple spatial and temporal scales, there is a tendency toward context-specific scales related to the dynamics that shape landscape structure and function (van der Maarel 1988; Wu and Loucks 1995). Natural ecosystems are intact vegetation assemblages that exhibit compositional, structural and functional heterogeneity based upon underlying physical gradients of climate and geological substrate (Moore et al. 1991; Cleland et al. 1994; Catterall et al. 2001). These gradients change over time through non-linear feedbacks with native vegetation and biodiversity, and disturbance regimes such as diseases, pests and pathogens (Landsberg and Gower 1997; Loreau et al. 2002). While exogenous disturbance is incorporated as a normal part of functioning in intact landscapes, within human-modified landscapes, there are often large departures from historical functioning (Baker 1995; McIntyre and Hobbs 1999; Ernoult et al. 2003). As land use modifies and fragments native vegetation, concurrent structural and functional changes in turn affect resource flows and partitioning across the landscape (Saunders et al. 1991; Hobbs 1993; McAlpine and Loyn 2000; Foley et al. 2005). Modifying landscape structure, such as through the type and configuration of components, changes the interconnections and functions of the landscape, which may result in new dysfunctional landscape states (Noy-Meir 1973; Westoby et al. 1989). Symptoms of landscapes dysfunction include: altered (often reduced) precipitation budgets in terms of 14


distribution and magnitude from microclimate to regional scales (Charney 1975; Mylne and Rowntree 1992; Eltahir and Bras 1996; Boulter et al. 2000; Ray et al. 2003; Walker 2003; Pitman et al. 2004; McAlpine et al. 2007); modified surface hydrological regimes (Ludwig et al. 1999a; Loch 2000; Ludwig and Tongway 2002), groundwater recharge and dryland salinity (Wilson et al. 2000; Ellis et al. 2005a); changes to soil macro-invertebrate species composition and nutrient cycling (Fragoso et al. 1997; Rossi and Blanchart 2005); soil structural degradation and erosion (Freebairn 1998; Main 2003); as well as the subsequent altered habitat conditions for vertebrate fauna (Bennett 1999; Ernoult et al. 2003). Due to the potential for multiple landscapes states and non-linear dynamics between states, the use of linear and reductionist mathematical functions (differential equations) to model complex system behaviour becomes intractable (Holland 1995; Gunderson et al. 2002). The challenge of understanding non-linear interactions between natural systems and human land use across multiple spatial and temporal scales, must be addressed if we are to have confidence in predictions as to the likely response of landscape functions to a given set of landscape modifications (Barrett et al. 2001). Landscape ecology and complex adaptive systems (CAS) are two disciplinary areas that offer analytical methods that aid in understanding landscape complexity (Milne 1998). Together, these disciplines can be synthesised into a unifying framework that addresses recurrent issues of multiple scales, spatial configuration, and dynamics of system components. The aim of this paper is to present a new conceptual framework, Complex Adaptive Landscapes (CAL), that unifies research in CAS and landscape ecology on system properties, features and dynamics of natural and human modified systems. We provide a set of six core tenets that define the inherent structures, functions and mechanisms operating within CAL. We also provide applied examples for each tenet, specifically on the ecohydrological functions of semi-arid and sub-tropical landscapes at hillslope (100s ha) to landscape (10000s ha) and regional (millions ha) scales.

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These examples provide a starting point for improving ecohydrological functioning and natural resource management (NRM) in human modified landscapes. The principle message for managers of natural resources is the need to recognise that the observed structures and functioning within landscapes are part of a multiple scale system. The level or scale of our observations may uncover particular patterns or dynamics, but these patterns may not arise purely due to processes operating at that scale, hence, the need for a CAL framework. Specifically, the configuration of specific structural components affects the interactions and feedbacks between them and the landscape, which are important determinants for the formation of aggregates and the emergence of landscape functions that sustain the resource base.

2.2

Complex Adaptive Landscapes – A Framework

2.2.1 Examples of Landscape Ecological and CAS Research Applications Complexity, according to Standish (2001), is a ubiquitous property of the universe that the field of CAS attempts to define. There is a growing body of evidence that many natural and human modified systems are organised as an open, yet complex, web of interconnecting components in space and time (Itami 1994; Simon 1996; Goldspink and Kay 2003). Examples can be found in everyday life from the internet, economies, urban systems, physical and chemical systems, ecological food webs, swarms of insects, the immune system, developing embryos, the brain, and even in the emergent behaviour of social groups (Holland 1992; Perry 1995; Schweitzer 1997; Levin 1998; Wu and Marceau 2002; Jeong 2003; Torrens 2003; Pascual and Dunne 2006). In the fields specific to CAS, there have been a number of researchers who have contributed to the development of concepts and applications. These include the universe and life per se (Holland 1998; Kauffman 2000), Earth (Lovelock 2000; Lenton and van Oijen 2002), the economy (Ramos-Martin 2003), the human brain and immune system (Holland 16


1995), human-ecosystem dynamics (Folke et al. 2002), information processing and networks (Holland 1992; Railsback 2001), and the emergent properties of systems in general (Standish 2001). Landscape ecologists have also recognised that CAS is useful for understanding and defining principles for the patterns and dynamics that are observed in many landscapes (Allen 1998). Some of the applications include ecosystems (Levin 1998), vegetation complexity (Pignatti 1996), soil landscapes (Buol 1994), hillslope hydrology (Sidle et al. 2001), and the presence of critical habitat loss thresholds for the persistence of wildlife populations (McAlpine et al. 2002; With and King 2004). Important contributions on specific aspects of landscapes that relate to CAS include hierarchy theory (Allen and Star 1982; Wu and Loucks 1995; Wu and Li 2006), non-linearity and feedback mechanisms in general (DeAngelis and Waterhouse 1987; O'Neill 2001; Wu and Marceau 2002), theories related to the resilience and resistance of meta-stable states (Holling 1973; Pimm 1979; Pimm 1984; Neubert and Caswell 1997; Gunderson et al. 2002), natural resource aggregation (Ludwig and Tongway 1995; Ludwig et al. 1999a; McIvor and McIntyre 2002), multiple meta-stable states in woodlands (Noy-Meir 1973; Westoby 1980; Laycock 1991; George et al. 1992), landscape memory (Peterson 2002), and the effects of biodiversity of ecosystem functioning (Tilman 1996; Loreau et al. 2002). Specific examples are not presented in detail here, rather, they are introduced as supporting material that highlight a particular feature or dynamic for each core tenet of CAL.

2.2.2 The CAL Framework A simple state and transition diagram is used to illustrate the basic premise of CAL (Figure 2.1). A set of four landscape states are shown, each a response to either landscape evolution or human land use over time. From an intact landscape comprised of native vegetation assemblages (State I) two choices may be made: 1) a uniform thinning of the woodland for the establishment of light grazing on native pastures (State II), or 2) conversion

17


of the woodland to cropping of non-native species (State III). The landscape in State II may exhibit some loss in complexity and stores of natural resources, while State III may lose substantial complexity and a substantial loss of natural resources (although these resources can be artificially boosted by fertilizers and irrigation). If major exogenous disturbances occur during State III, such as floods or drought, there may be a large net loss of vital natural resources such as key nutrients and organic matter. This is particularly the case where there are few components available with the appropriate interconnections between them to buffer, capture and redistribute the influx of resources to the system (such as rainfall). This may result in a ‘leaky’ system. Where the resource base has not become severely degraded, State III may return to State II over time though natural adaptive recovery processes. Alternatively, by strategically recreating vegetation aggregates within a matrix of land use appropriately targeted to match landscape functioning, the natural resource base may begin to recover over time to reflect a complex production landscape (State IV). While Figure 2.1 is useful, the state and transition model is unable to show the full complexity of CAL. A more detailed schematic is presented in Figure 2.2, representing similar states and transitions as described for Figure 2.1. Figure 2.2 shows landscape complexity as multiple levels (z axis), with a high number of components interacting within each level, feedback mechanisms between components and resource gradients leading to aggregation and self-organisation (inset), and the ability of aggregates in each level to intercept, store and redistribute resources and buffer against disturbances (y axis). These features are similar to ‘panarchy’ as described by Gunderson et al. (2002). As a consequence of human land use, different landscape components (e.g. soil, hydrology, vegetation, biological diversity) are modified through time (x axis), causing system structures (e.g. vegetation cover, habitat) and functions (e.g. soil retention) to change across areas, and thereby potentially causing future changes in landscape states. These features again reflect the state-and-transition framework such as those presented by Noy-Meir (1973) and Westoby et

18


al. (1989), but add the effects of feedbacks between multiple components, their relationship to state transitions, and impacts upon the resilience of the system through time (e.g. Carpenter et al. 2001). Figure 2.2 also has similarities to an inverted version of the ‘ball and cup’ model developed by DeAngelis and Waterhouse (1987).

Figure 2.1 The complex adaptive landscape (CAL) framework as a simple state and transition diagram representing the change in complexity as landscapes enters different states in response to minor and major disturbances and available natural resources.

19

Figure 2.2 CAL as a schematic diagram illustrating landscape complexity as multiple levels (z axis) with a high number of components interacting within each level. The bottom vector shows that landscape states change through time (x axis) due to the thinning or removal of vegetation aggregates through land use, while the top reflects the intact native ecosystem. Feedback mechanisms operate between components and resource gradients leading to aggregation and selforganisation at specific scales (inset). Flows are intercepted by components, which are then able to store and redistribute resources and buffer against disturbances (y axis). Note that when vegetation structure is altered, gradients in other levels are concurrently modified, albeit with a slight time-lag.

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2.2.3 Core Tenets of the CAL Framework To explain the basic tenets of the CAL framework in more detail, we need to first conceptualise the complexity of landscapes across a scale continuum. This requires a clear definition of the scope of the system being modelled, and a meaningful translation of such complexity by capturing the appropriate components and their dynamic interactions (Pickett and Cadenasso 2002). CAS have a high level of redundancy with many components providing similar functions, and we can use this redundancy to greatly simplify their description (Simon 1996). The key is to recognise pervasive characteristics of the system from the large number of idiosyncratic features (Holland 1995). The CAL framework can be simplified to six core tenets (Table 2.1).

Table 2.1 The six core tenets of the CAL framework Tenet

Core concept

Scale continuum

Landscapes are comprised of components in levels that are distributed across a continuum of scales in space and time

Open gradients

Energy and resource gradients in landscapes are thermodynamically open

Diversity of Components

High numbers of components within each level A diversity of types to cope with alternative system dynamics Humans are included explicitly

Interactions and Non- Landscapes comprise multiple interactions between components and resource gradients linear Feedback Positive feedbacks are self-reinforcing functions for component consolidisation, Mechanisms negative feedback are constraints imposed by the system and other components Non-linear responses arise from time-lags associated with resource buffering and partitioning Aggregation and Self- Feedback mechanisms instigate the formation of aggregates and lead to Organisation self-organisation among the components Self-organisation underpins emergent patterns/behaviours at other levels Multiple States and Transitions

Landscapes have the potential for multiple meta-stable states Transitions may be difficult to reverse when resource gradients are altered Resilience and resistance are measures of the stability of a given landscape state

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2.2.3.1

A Continuum of Scales Issues of scale are one of the greatest challenges facing contemporary ecological and

geographical studies of spatially arranged phenomena (Levin 1992; Wiens 1992; Bissonette 1997). While the term scale in general refers to the spatial or temporal dimension of a phenomenon, Wu and Li (2006) suggest that scale is comprised of dimensions, components and kinds. They suggest that dimensions include time, space, and an organisational level such as in hierarchies. Components of scale are related to sampling and cartographic frameworks, that include resolution (grain), extent, and coverage and spacing of samples. The third tier, kinds of scale, is the scale at which we perceive some phenomena. This leads to perceptions of a ‘characteristic scale’ for some phenomena or process, which implies an interaction between the observer and the inherent scale of the phenomenon (Wu and Li 2006). The scales we perceive, sample, analyse, and further classify as being a characteristic scale related to some phenomenon or process, are based on a particular reference point positioned along a scale continuum. Such a point is often determined by our objectives, or the technologies we have at our disposal to observe or measure the phenomenon or process. A continuum of scale implies that patches, boundaries and functional heterogeneity in landscapes are all sensitive to the scales across which they are defined (Kershaw 1973; Kolasa and Rollo 1991; Wiens 1992). Choosing a particular scale over some other range of scales therefore, can have considerable bearing on what we determine as either components (variables) or processes (parameters) when describing and modelling the physical or biophysical system, as well as the number and choice of variables selected to characterise an aggregate system (Carpenter et al. 2001; Beisner et al. 2003). As there is no ‘correct scale’ at which to view, describe, or model a landscape or ecosystem (Levin 1992), our analyses and findings may be subject to ‘scale effects’, or the artifacts of measuring at a given spatial and temporal resolution and extent (Wu and Li 2006). This sampling of just a small sub-set of the scale continuum may then lead us to question the

22


reliability of our explanation where the detection of system patterns and dynamics are more an artifact of the particular scale of observation than the actual system dynamics and patterns per se (Wiens 1989; Wu and Loucks 1995; Bissonette 1997; Wilhelm and Bruggemann 2000). For example, if we measure some pattern in a landscape at a scale larger than the scale of the process, the results may show noise within the data, or inversely, if the scale of observation is smaller than the scale of the process, the results may exhibit trends within the data (Wu and Li 2006). For temporal regimes, we may make observations on what appears to be the typical dynamics at some given time, when it is instead, only one particular state or the ‘transient response’ of a landscape undergoing a change between states (Neubert and Caswell 1997; Bass et al. 1998). Scale effects related to spatial heterogeneity, multiple temporal dynamics and feedbacks from various landscape components in response to human land use, are often ignored within land use management and NRM policy arenas. For example, at the hillslope scale most agricultural production still operates within square paddock boundaries that do not account for the spatial heterogeneity of soil properties or variations in ecohydrological functioning across the hillslope (Saunders and Briggs 2002). At the catchment scale, waterresource management plans developed for the semi-arid Condamine-Balonne River Basin of Queensland treat six spatial hydrological zones as functionally homogeneous, despite differences in land use on adjacent hillslopes and the spatial and temporal variation in hydrologic flows within each zone (Thoms and Parsons 2003). In the temporal scale, there are numerous examples of non-linearity in response to inputs. Annual precipitation, for example, often displays highly variable temporal regimes that fluctuate widely (e.g. El Niño-Southern Oscillation) (Bowman et al. 2001; Franks 2004), resulting in rivers such as the Cooper and the Diamantina of western Queensland displaying the most variable flow regimes on Earth (Puckridge et al. 1998; Arthington and Pusey 2003). As streams and rivers in semi-arid and sub-tropical woodlands are highly dynamic, it is vital

23


to consider the temporal lag between human disturbances and system responses (Brooks and Brierley 2000). Although such large scale dynamics affect land use and environmental flows for decades, recognition of a river’s larger scale hydrological characteristics are often not considered (Thoms and Parsons 2003). Other time-lags are human induced, such as modified landscape structure and function following land use change (Landsberg et al. 1998; Brooks and Brierley 2000; Foster et al. 2003; Williams and Saunders 2005; Peters and Havstad 2006). Land clearing falls into this category (Figure 1 and 2), along with numerous other examples of human land use altering the functioning of Earth’s ecosystems from patch to planetary scales (Vitousek et al. 1997; Foley et al. 2005). Some of these examples include: the change in hydrological functioning of a stream network following erosion and sedimentation (Ladson and White 2000; Rutherford 2000; Brizga and Finlayson 2000a); slow declines in soil structure and fertility (Freebairn 1998; Ahuja et al. 1999; Hatton and Nulsen 1999; Ray et al. 2003; Sparrow et al. 2003); the emergence of dryland salinity decades after landclearing (Eberbach 2003); and changes in regional rainfall and temperatures (Charney 1975; Mylne and Rowntree 1992; Eltahir and Bras 1996; Ray et al. 2003; Walker 2003; McAlpine et al. 2007).

2.2.3.2

Open Gradients Landscape patterns and dynamics involve resource inputs, outflows, and disturbance

events which occur both within and external to the landscape system. This is consistent with CAS, which are not ‘isolated’ systems, but thermodynamically open and in constant feedback with their environment (Ilachinski 2001). This also appears to be true for many natural and anthropogenic systems (Itami 1994; Simon 1996; Goldspink and Kay 2003). In landscape systems, order and thermodynamic efficiency (i.e. lower entropy) is achieved through mechanisms which intercept, transform and store solar energy (Odum 1971; DeAngelis et al. 1989; Clayton and Radcliffe 1996; Forman 2001; Wu and Marceau 2002).

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The perception of how gradients affect landscape structure has also shaped our views on spatial patterns in ecological phenomena (Packham et al. 1992). For example, there has been a long running debate in vegetation science concerning whether species are individualistically distributed along a continuum (open gradients), or grouped together in synergistic ‘communities’ (Kershaw 1973; Scott 1974; Austin 1985; Krebs 1985). The waterenergy dynamics of landscapes are clearly continuous or open gradients, which often determine the types and richness of tree and shrub species (O'Brien et al. 2000). Vegetation patterns in semi-arid and sub-tropical woodlands reflect open gradients driven primarily by interactions between climate and geology, and the redistribution of water and sediments in the fluvial system. These variables dictate the flow of water which shapes landforms, the orientation of watersheds, topography, the frequency and spatial pattern of disturbances such as wind and fire, the development of soil, distribution of native vegetation, movement of organisms, and inevitably provide the template for human land use (Robinson 1972; Moore et al. 1991; Cleland et al. 1994; Hollander et al. 1994; Landsberg and Gillieson 1995). The physiological processes that govern species distributions and growth therefore, are driven by the interactions between radiation, temperature, humidity and wind and a species’ inherent physiological factors of nutrient requirements, canopy architecture, leaf longevity, growth phases and life cycle (Landsberg and Gower 1997). While open gradients may give rise to heterogeneity across landscapes (Forman 2001), the cumulative effect of all environmental gradients at one point in space and time results in vegetation assemblages of different composition and structure. Eagleson (2002) suggests that natural selection favors increased productivity, such that each plant functional type (PFT) exhibits the most efficient capture, use and recycling of local resources. For example, there is a plant functional type which represents the optimal form-behavioral strategy for carbon gain within each niche (Mooney 1974). In this context, dysfunctional landscapes may reflect a mismatch between system dynamics (e.g. influx and disturbance) and the ability of non-native

25


PFTs to capture available resources. This highlights the importance of the structural and functional capabilities of landscape components for the maintenance of ecohydrological functioning, especially in human modified landscapes.

2.2.3.3

Multiple Component Types Components are generally conceived as being able to perform at least one

thermodynamic work cycle, that, in turn, propagates and proliferates diversifying organisation of the system (Kauffman 2000). Landscapes themselves may house sets of dynamic assemblages of interacting components that are ‘semi-closed’ (Klijn and Udo de Haes 1994). These components are self-organised into patterns of interaction on multiple scales of space and time distinguishable from one another by dissimilarities in their structure and function and by the scale at which they appear most dynamic (Cleland et al. 1994; Levin 1999). It is feasible, therefore, that components may be discerned by the differences between rates of processes that organise or modify system dynamics over time (O'Neill et al. 1986). Most components in CAL disrupt flows, albeit temporarily, through their structural or functional effects (Figure 2, y axis). Flows are usually buffered or filtered through boundaries, which are maintained through the allocation of a proportional expenditure of captured energy and material (Wilhelm and Bruggemann 2000). The abstract notion of auto-poietic systems also conveys the same description of self-replicating components maintaining a semipermeable chemical boundary to allow them to actively select what enters their structure (Maturana and Varela 1980; Luisi 2003). Components also need to respond adequately and appropriately to disturbances to resource flows. For any component, finding a set of strategies that allow an appropriate response for a potential large number of flux/disturbance regimes would require an enormous set of strategies stored as chemical information. This seemingly insurmountable issue is partly overcome by using ‘building blocks’ (Holland 1992; Holland 1998).

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Building blocks in CAS represent a system’s grouping of similar components and processes of the environment into general sets of patterns that often repeat (Holland 1995). Although landscapes may be spatially separated and dissimilar in appearance, there are often striking similarities in their function (Peterson et al. 1998), with building blocks a possible explanation. In biology, the ‘law of requisite’ variety explains the same principle (Clayton and Radcliffe 1996). For building blocks to be useful in a landscape open to constant change, structural or functional strategies must allow a component to ‘predict’ (overt) or ‘anticipate’ (tacit) the near future states of the system. Holland (1995) introduces the term ‘tags’ to describe mechanisms that facilitate emergent behaviour or phenomena which persists through time, despite the fact that individual components are continually changing. Tags facilitate the parsing (grouping of similar components and processes) of environmental conditions or constraints into general sets based upon the patterns of previously successful models (Holland 1995). As no component can possess all possible strategies for functioning in multiple landscape states, or directly interact with all other component types, only those that have mechanism to facilitate such interactions are successful within a given landscape state. In other words, components with useful tags that promote system stability spread, while those that do not fail, and subsequently lose their resources to other more useful components (Holland 1995). These factors partly explain the existence of (or the need for) a diversity of component types within CAS and hence, CAL. For example, in landscapes that show marked variation in flux and disturbance, components that appear redundant at certain times fulfill important functions under different environmental conditions (Williams and Saunders 2005). The more regulated a system, therefore, the more likely there are the correct types and amounts of freely available supported tags that mediate useful exchanges (Holland 1995). These principles have marked ramifications for the ecohydrological functioning of human-modified landscapes. For example, if native ecosystems are comprised of structurally

27


and functionally adapted components (e.g. PFTs) each with its tags for interacting with other components and the broader landscape, then leaky landscapes reflect a dysfunctional ecohydrological system due to the lack of corresponding tags belonging to the introduced PFTs such as exotic grasses and crops. The important tags for PFTs in terms of maintaining the ecohydrological functioning of modified landscapes, may be related to the seasonality and life cycles of each species for determining respiration and photosynthetic timing and capacity, plant morphology through interception and shading, biomass through carbon assimilation, nutrient uptake and root depth for water and nutrient cycling, and the production of organic matter through ground cover and organic matter.

2.2.3.4

Interactions and Non-linear Feedback Mechanisms The correspondence of tags, suggests Holland (1995), facilitates complimentary

interactions that reinforces a systems functioning. These interactions constitute feedback mechanisms between system components. Feedback in general, is a cybernetics term for ‘stimulus-response’ behaviour of some phenomena (Robinson 1998). Odum (1971) describes positive feedbacks as ‘deviation accelerating’ and negative feedback as ‘deviation counteracting’, although they may in general be regarded as signal amplification or signal attenuation mechanisms, respectively (Walters 1971; Warner 2004). In a CAS, a steady state and low entropy (order) may be achieved through contrasting feedback mechanisms between components and the system they are embedded within (Clayton and Radcliffe 1996). Li (2000) provides a definition of a system as being an irreducible complex of elements and subsystems in which the parts are interacting. Landscape systems are suggested to result from multiple negative and positive feedback mechanisms, which form interconnected networks such that no species or process exists in isolation from other species or processes (Tilman 1988; Pahl-Wostl 1995; Chapin et al. 1996; VanLeeuwen et al. 1999). In CAL, we are particularly interested in the connections and potential loops through which a

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system may eventually effect itself (Clayton and Radcliffe 1996). Different subsystems within the soil-plant-atmosphere continuum (SPAC) for example, are connected by non-linear feedback loops that interact in a process of self-organisation (Pignatti 1996), the functions of which are to regulate water and biogeochemical cycles (Zalewski et al. 2003). There are complex organisational factors that underpin the interactions between hillslopes, vegetation and hydrology, such as through patterns of erosion and deposition (Wainwright et al. 1999). We are only beginning to understand that these multiple reciprocal non-linear responses between geomorphology, hydrology and ecology operate over very long time-frames (Brizga and Finlayson 2000b). In human-modified landscapes, these feedbacks loops have often been impaired, resulting in a dysfunctional ecohydrological system. Clearing native vegetation over large areas is a primary cause for the altered water balance and hydrology of the soil-plant-atmosphere continuum (Eamus et al. 2006). In agricultural systems, alternative PFTs are not entirely suited to the influx of energy and material entering these modified landscapes (Freebairn and King 2003; Williams and Saunders 2005). As a consequence, these landscapes often exhibit a loss of connectedness, lower soil fertility despite high fertilser application levels, and leak water, nutrients, and carbon (Williams and Gascoigne 2003). The spatial and temporal configuration of differing PFTs is linked through feedbacks to precipitation, temperature, the interception, infiltration, runoff and storage of water, soil condition, fuel loads for fire, riparian functions, and the fluvial system (Tongway and Ludwig 1997; Brooks and Brierley 2000; HilleRisLambers et al. 2001; van de Koppel et al. 2001; van Langevelde et al. 2003; Caylor and Rodriguez-Iturbe 2004). For example, the physical structure of many native trees is efficient for rainfall interception and directing water along the main trunk, which then infiltrates to considerable depths at the base of the tree where it may be stored for later use (Hatton and Nulsen 1999). Stored soil water may then be recycled through hydraulic pumping of deep groundwater by trees and some shrubs, which is often

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released into surface soils at night (Hodgkinson and Freudenberger 1997). In turn, when this pumping is impaired as a result of large tracts of native vegetation being converted to pastures on old salty soils, like those typical of much of semi-arid Australia, this has often resulted in the widespread appearance of dryland salinity (Eberbach 2003). One of the most important feedbacks of PFTs is the recycling of precipitation and soil moisture through evapotranspiration, which may be a particularly important mechanism for the generation of rainfall (Pal and Eltahir 2001). It has been suggested that the convection of transpired moisture may contribute up to a third of the water in the atmosphere over land masses (Brubaker et al. 1993; Trenberth 1999). Clouds, for example, tend to form sooner over tree canopies due to their higher sensible heat flux, as well as higher relative humidity at the top of the canopy boundary layer (Lyons 2004). Another factor, although subtle, relates to the release of biogenic gases (e.g. H2O, CH4, O2) that form from the decomposition of organic matter, which can promote the condensation of ice nuclei in clouds (Hayden 1998). These factors have rather important ramifications where land cover has been modified over entire regions, as the feedbacks, although displaying decadal time-lags, have often altered atmospheric circulation and precipitation budgets in terms of distribution and magnitude (usually declines) (Charney 1975; Mylne and Rowntree 1992; Eltahir and Bras 1996; Ray et al. 2003; Walker 2003; McAlpine et al. 2007). In the Sahel region of Africa land clearing has resulted in an increase in surface albedo, a sinking motion of air masses to maintain thermal equilibrium, additional drying, and enhanced aridity (Charney 1975; Savenije 1995; Hogg et al. 2000). Land clearing patterns in combination with a concurrent switch to an alternative macro-scale rainfall regime, is also believed to have resulted in a sudden decline of about 20% of annual precipitation during the mid 1970’s in the Western Australian wheatbelt (IOCI 2002; Pitman et al. 2004). In the subtropical regions of southeastern Queensland and northeastern New South Wales, the strongest temperature anomalies have coincided with areas of largest land cover change since European

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settlement, with a regional mean annual rainfall decline of 4% to 12% attributed to land clearing (McAlpine et al. 2007).

2.2.3.5

Aggregation and Self-Organisation The previous section highlights that feedback mechanisms are particularly important

for the maintenance of ecohydrological functioning within a landscape. Reciprocal feedback mechanisms also reinforce the clustering of components at particular levels across the scale continuum, and therefore become important precursors for the formation of aggregates (inset, Figure 2). As each component’s interactions with its environment alters that environment, the tension between positive and negative feedbacks causes CAS to be a poised state at the edge of chaos (Kauffman 2000). Holland (1995) suggests that this is where the complex regime is most effective for generating the conditions necessary for emergent behaviour and adaptations. The emergence of complex patterns of interactions at a given scale, however, begins with only a small number of rules or laws at the local level. It is through feedback mechanisms that the generation of complex aggregate patterns at other levels begin to emerge, although the rules for which are not part of the system’s initial specifications (Standish 2001; Torrens 2003). A system that is greater than the sum of the components that it encompasses is a trademark feature of all CAS. These processes also operate on a separate fundamental physical level than those of natural selection (Holland 1995; Kauffman 2000). That is, the emergence of spontaneous order occurs despite (not due to) concurrent selection dynamics for a given fitness function. For example, Kauffman (1993) suggests that selection is the mechanism that achieves a CAS that is capable of adaptation, and this results in an environmental context in which perpetual novelty is promoted (Holland 1995). Perpetual novelty in CAS suggests there are always better ways of recycling resources, including the sharing of information.

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While aggregation is a common property of nature (Forman 2001), patterns may emerge at particular spatial and temporal scales (Parker et al. 2003). Components that are closer together spatially or topologically, typically interact more strongly as they share more connections (Simon 1996; Jeong 2003). If interactions between separate components are too weak, they adapt and evolve independently of each other in a more chaotic manner, or if too large or strong, they either mimic each others behaviour or are overwhelmed by random noise (Ilachinski 2001; Parker et al. 2003). Where interactions fall between these extremes, selforganisation of larger scale phenomena based on smaller scale interactions begins to emerge (Holland 1995). At the hillslope scale in landscapes, there are complex feedbacks between the root systems of grasses, shrubs and trees, and their symbiotic relationship with soil fungi and macrofauna, providing a vast network for recycling and redirecting water and nutrients (Williams and Saunders 2005). For example, there is a positive feedback from macropores consisting of decayed and live roots, subsurface erosion, surface bedrock fractures and animal burrows, which together give rise to the self-organisation of larger preferential flow systems as sites become progressively wetter (Sidle et al. 2001). These macro-pores, in turn, lead to the deep cycling of moisture and organic matter. The interactions between soil organic matter and mineralisation processes lead to aggregations of clay particles and organic materials, which stabilises both soil structure and the carbon compounds within the aggregates (Oades 1988). With the modification of these complex feedback systems due to changes in PFTs, there may be less buffering of precipitation, a slow decline in soil structure and fertility, less cycling of soil moisture, and more extreme runoff events (Elliot and Ward 1995; MartinezMena et al. 2000; Magdoff and Weil 2004; Williams and Saunders 2005). At the landscape scale, aggregation in the form of vegetation ‘clumping’ is a fundamental aspect of most resource limited semi-arid woodlands (Ludwig and Tongway 1995; Ludwig et al. 1999b; McIvor and McIntyre 2002). Feedbacks between patch structure

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and resource flows, for example, causes vegetation aggregates to form around accumulated resources, and vice versa. The resulting heterogeneity sustains a higher species richness, NPP, and biomass across the landscape than would be the case if resources were spread homogeneously (Noy-Meir 1973; Noble 1997). It appears that the size and configuration of vegetative elements (aggregates) at larger scales are also important to maintain resource condition in semi-arid woodlands (Loch 2000). Simulation modelling of a semi-arid bandedmulga landscape by Ludwig et al. (1994), for example, suggests that between 40% and 60% (high and low infiltration respectively) of the landscape must function as a water and nutrient ‘reserve’ or sink in order to maximise water and nutrient retention when rainfall is less than 160 mm. The spatial configuration of vegetation aggregates at the landscape scale also affects atmospheric processes. The heterogeneity of cover types has been shown to affect the mesoscale patterns of the fluxes of mass, energy and momentum from the biosphere to the atmosphere (Brown and Arnold 1998; Hayden 1998). The processes modify convective weather systems, and thereby play a significant role in regional-scale climate change. The spatial structure of the surface heating as influenced by landscape patterning, such as often arises along land-cover-type and soil-order boundaries, can generate relatively strong atmospheric circulations and produce focused regions for deep cumulonimbus convection (Garrett 1982; Avissar and Pielke 1991; Pielke 2001). Mahfouf et al. (1987), for example, found that the juxtaposition of a well-transpiring vegetated area with a dry bare land can generate circulations as strong as sea breezes. Anthes (1984) also documented that larger vegetation bands or patches (50 km to 100 km wide) in semi-arid landscapes, may increase convective precipitation more than areas of uniform vegetation.

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2.2.3.6

Multiple States and Transitions The tendency of landscape systems to maintain themselves over a limited range of

conditions is termed meta-stability (O’Neill et al. 1989). Within a constantly changing system, components must remain viable (meta-stable) based upon the maintenance of operations within the limited range of conditions and constraints imposed by the fundamental properties of their environment (O'Neill et al. 1989; Bossel 2001). The formation of aggregates is fundamental to such persistence, which is itself dependent on reciprocal feedbacks over long temporal periods (inset, Figure 2). The presence of meta-stability does not imply a ‘balance in nature’, rather a system far from equilibrium that alternates between periods of relative meta-stability and dramatic change through stochastic perturbations (Holling 1973; Levin 1999). At the scale of the biome, the global climatic system is a highly non-linear multiple meta-stable equilibria system, which often undergoes both episodic and abrupt changes in state (Rial et al. 2004). It is now widely accepted that many ecosystems may exhibit multiple meta-stable states (Gunderson et al. 2002). There are two views, however, of meta-stable states in landscapes. One view focuses upon components such as species and populations as they grow, reproduce and perish (biotic centric perspective), whereas an alternative view focuses on systems dynamics such as flux, variable energy capture, nutrient retention and rate regulation (i.e. a functional centric perspective) (Holling 1973; O'Neill et al. 1986). In either case, a meta-stable equilibrium is maintained through the dampening of disturbances through contrasting feedback mechanisms (Walters 1971). Perturbations to the system, however, may result in new patterns emerging depending on the scale, intensity and timing of the disturbance (O'Neill et al. 1986; Cleland et al. 1994; Shugart 1998). The timing of a disturbance is particularly important because it affects the future trajectory and state of the system (Gunderson et al. 2002). For many systems, the particular meta-stable state it reaches depends upon the initial conditions of the components (Parker et

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al. 2003). This ‘sensitivity to initial conditions’ was also purported by Lorenz (1963) to help explain highly divergent atmospheric conditions. The presence of ‘strong attractors’ represented by dominant variables in the system may also determine the future state of the system (Scheffer et al. 2001). Some researchers have referred to this phenomena as ‘landscape memory’ or ‘historical artifacts’ (Peterson 2002). In other words, the history of landscape disturbance influences the current state of the system, and the future likelihood of a process or component (e.g. a species) being maintained (Schrott et al. 2005). The maximum propagation rate of a perturbation through a system, the amplification or magnitude of the perturbation, and the time at which the amplification occurs, should all be included in system measurements (Neubert and Caswell 1997). They stress that ignoring transient growth parameters may result in misleading information of a system’s response to perturbations. The attenuation or the growth rate of a particular perturbation in a system, however, also depends on the amplitude and frequency of oscillations in inputs of energy and materials (flux and disturbance), as well as the resilience, persistence and resistance of the system (Holling 1973; Pimm 1984; Neubert and Caswell 1997). Resilience is a feature of stability, which is normally associated with the rate of decay of a perturbation within a system, and the return time of components to their original state (DeAngelis et al. 1989; Pahl-Wostl 1995; Neubert and Caswell 1997). In general, slower oscillating variables control faster oscillating variables (Gunderson et al. 2002). In landscapes, small triggers may be propagated as matter or waves such that they produce much greater high energy effects. These changes must be appropriated by components (e.g. PFTs) within a timeframe much less than the time it takes for a change in organisation in response to that information (Engelberg and Boyarsky 1979). If systems change faster than components can adapt their structure or functions, then the component may cease to exist, which may potentially undermine the resilience of the entire system.

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At the hillslope scale, vegetation species are dependent upon the relation between scale of environmental variation (e.g. seconds, days, years, centuries) and the time scale of organism response (Woodward 1987; Hollander et al. 1994). At the landscape scale, the time that nutrients are intercepted, stored and processed within detritus makes a good approximation for the resilience and return time of an ecosystem following perturbations (DeAngelis et al. 1989). In landscapes comprised of native ecosystems, each vegetation component displays differences in their resistance and resilience to displacement from levels of environmental gradients they have adjusted to over time and the speed of recovery to the new levels, respectively (Grime et al. 1997). Due to the unpredictable nature of disturbances, native ecosystems often have a high diversity of components to cope with stress and changing conditions, and buffer accumulated resources against loss (Williams and Saunders 2005). In dysfunctional landscapes, the rates of energy and material export may surpass the assimilative rate of the organisms (e.g. PFTs) (Odum 1971). This, in turn, results in a build-up of high entropy wastes and a reduction in system resilience. As most landscapes can display multiple meta-stable states, a challenging problem exists in defining what may be a ‘normal’ range within biophysical structures and biogeochemical functions of landscapes (Belaoussoff and Kevan 1998; Campbell 2000). Feedbacks, in particular, often cause time-lags due to the buffering and partitioning of resources, resulting in non-linear responses to flux and disturbance and the rise of heterogeneity of process across the landscape (Patten and Odum 1981; Wu and Loucks 1995; Hartvigsen et al. 1998). This non-linear behavior, in turn, causes numerous unpredictable connections, patterns, and behaviours through time (Patten and Odum 1981; Veldkamp et al. 2001). As a result, it is much more difficult to predict at which point human land use surpasses the inherent resilience of landscape systems, and forces a landscape to enter a dysfunctional state.

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It is perhaps easier and more meaningful to suggest which functions we wish to maintain in modified landscapes. For example, in a functional semi-arid woodland landscape, the coexistence of trees and grasses may lead to one of many stable states, which is often cited as resulting from differing partitioning of water and nutrients (Noble 1997; Ludwig et al. 1999b; Caylor and Rodriguez-Iturbe 2004; D'Odorico et al. 2005). Dysfunctional landscapes, on the other hand, often display significantly lower soil organic carbon and nitrogen, infiltration capacities and soil biological activity, which can ultimately lead to less water in the reserve and lower plant production pulses (Freudenberger et al. 1997; Ludwig and Tongway 1997). Dysfunctional landscapes also present fewer surface obstructions and consist of larger inter-patch distances that allow flows (e.g. water and wind) to gain more energy and volume, thereby accelerating erosion and losses form the local system (Ludwig and Tongway 1997). In other words, dysfunctional landscapes reflect states of disaggregation and a net loss of connectedness, resulting in ecohydrologically ‘leaky’ systems (DeAngelis et al. 1989; Ludwig and Tongway 2002; Williams and Gascoigne 2003).

2.3

A Synthesis of CAL Tenets for Ecohydrological Management The complexity of landscapes makes it difficult to understand and predict landscape

dynamics across scales with confidence or certainty (Peters et al. 2006). There are a number of methods that may be applied to overcome these potential scale-related drawbacks of sampling, analyses and the management of sub-sets of the scale continuum. The CAS paradigm is useful for simplifying such complexity (Milne 1998), and providing a rigorous framework to understand the multiple levels of interactions across a continuum of scales. It is not necessary to understand how every component or sub-system is structured, or how it functions, to predict its typical behaviour (Walters 1971). The key is to determine what components are preserved and which are lost as one moves from one scale to another (Levin 1992). For example, components may be distinguishable from one another by dissimilarities 37


in their structural and functional heterogeneity and the scale at which they appear the most dynamic (Cleland et al. 1994; Klijn and Udo de Haes 1994; Farina 1998; Shugart 1998; Levin 1999). The appearance of spatial heterogeneity at a given sub-set of scales may help us discern the scale of the dominant biophysical processes and rates (O'Neill et al. 1986; Meentemeyer 1989; Turner et al. 1989). We have highlighted the importance of feedbacks for both CAS and CAL to advance our understanding of system dynamics by focusing on its components and the linkages between them. One approach may include the placement of the most strongly interacting elements (usually high spatial propinquity) within the topmost of the hierarchy, while associating weaker interactions with progressively lower levels (Ilachinski 2001). However, it will require decades to properly understand how differing feedbacks may result from the types of components (PFTs) utilised in human-modified landscapes and their spatial and temporal configuration. Recognising and managing for the potential multiple meta-stable states in landscapes presents a major challenge. Part of the problem lies in what is deemed as ‘desirable’ in terms of landscape components and what functions we wish to maintain. Attempting to define these factors, however, is fundamental to the development of sustainable NRM practices and policy. We have shown that in modified landscapes, many of the PFTs that facilitate ecohydrological functioning, from climate to soil moisture, are replaced with exotic crops and pastures which cannot perform the required resource buffering, storage and recycling functions. Given that the future state of a CAS will depend upon a suite of factors such as prior conditions, the timing of disturbance in relation to the systems trajectory, the presence of strong attractors, and particularly the types and configuration of components within the system, historical precedence may not be indicative of future states. In other words, dysfunctional landscapes may not necessarily return to their previous states without significant human manipulation, or

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not at all where macro-scale dynamics enter alternative domains of attraction (e.g. climate change). Specific replications of differing types, configurations and areas of PFTs over decadal timeframes, in combination with monitoring of climate, soil and hydrological systems functioning, may provide some insight into the feedbacks necessary to maintain a functional landscape. For sustainable land use to occur, we may need to focus upon creating selforganisation in landscapes through hillslope scale feedbacks that lead to local aggregation of key resources. Aggregation at this scale, in turn, may be the precursor to the emergence of landscape or regional scale processes that reinforce lower-level ecohydrological functions.

2.4

Acknowledgements The authors wish to thank Land and Water Australia (LWA) and the Centre for

Remote Sensing and Spatial Information Science (CRSSIS) for their funding support during the preparation of this paper.

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40

Ryan, J.G. (2007) PhD Thesis - Chapter 3, GLAMS

CHAPTER 3 GLAMS - A GRAPHICAL METHOD FOR CAPTURING LAND AND WATER MANAGEMENT PRACTICES IN AGROECOSYSTEMS

This chapter is a paper published in the international journal Ecosystems. The content is original, and as lead author, is predominantly written by myself. The following should be used as the appropriate reference: Ryan, J. G., McAlpine, C. A. and Ludwig, J. A. (2007). GLAMS: a graphical method for capturing land and water management practices in agroecosystems. Ecosystems 10 (3): 432-447.

Abstract Modification of native ecosystems through land use can affect the biophysical functioning of agroecosystems, with spatial arrangement (configuration) through time often determining the degree to which landscapes experience dysfunctional states. An improved understanding is needed of how spatial and temporal patterns in land use affect ecohydrological dysfunctions, such as how landscapes leak or fail to retain water and soil, at scales relevant to farm management. We develop and apply a Graphical Landscape Map Survey method, or ‘GLAMS’, for measuring changes in landscape function based upon a 3D graphic of a hypothetical subcatchment. GLAMS was applied within four Landcare Groups comprised of farmers from the Western Catchments of Southeast Queensland, Australia. The aim was to capture the behaviors of farmers who manage land use under natural variations in precipitation, especially extended dry periods, and with the associated risks from ecohydrologically dysfunctional or ‘leaky’ landscapes. GLAMS provided variable spatial and temporal resolution which allowed quantification of the land use responses for three different property sizes: 1) small < 100 ha; 2) medium 100 – 500 ha; and 3) large > 500 ha. Responses were quantified using Bayesian Belief Networks (BBNs) to provide probability estimates of the likelihood of a given action taking place within a particular part of the landscape, considering both climatic and ecohydrological risks. The findings indicated that GLAMS was more intuitive to farmers than traditional question-based surveys, resulting in a low cost technique that is rapid to implement while providing spatially-explicit information relevant to farm and catchment management.

Keywords: graphical landscape map; land use management; ecohydrology; expert knowledge; Bayesian Belief Networks

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3.1 Introduction When considering the human dimension of land management, participatory processes are needed that actively seek to understand farmers needs and motivations in ways which translate their knowledge into practical management solutions at the farm scale (Carberry 2001; Ridley 2004). The adoption of farming systems approaches that integrate ecological, economical, and social objectives for improving short and long-term prospects for sustainability have increased over recent decades, both by farmers themselves and by research and extension organisations (Gibon et al. 1999; Kropff et al. 2001; Gibon 2005). The success of farming systems approaches will hinge upon a high level of farmer participation while recognising that their decisions are based on social activities as well as economic factors, and that these factors vary broadly between differing groups (Petheram and Clark 1998; Vanclay 2004). Although farming systems research is often a scientist-farmer partnership focused upon hillslope scales (1 – 10s ha) (Ridley 2005), it must also relate to the complexity, conflict, and multiple values of people and natural resources within the context of the broader landscape (10s – 1000s ha) (Seymour and Ridley 2005). In structurally intact landscapes, the interaction of geomorphology and ecohydrology influences the composition, structure and location of native ecosystems, with functional mechanisms often emerging that efficiently regulate the influx, distribution, and export of water, nutrients, and carbon (Florinsky and Kuryakova 1996; Wondzell et al. 1996; Dirnbock et al. 2002; Gibon 2005; Ludwig et al. 2005). Ecohydrological functions, in turn, impact on geomorphological processes through feedback mechanisms and interactions such as between vegetation cover and erosion and deposition (Wondzell et al. 1996; Wilby and Schimel 1999). These feedback mechanisms link the spatial configuration of vegetation within native forests and grasslands to ecohydrological functions of interception, filtering, uptake, processing, storage as well the subsequent transport of water, sediments and nutrients (Ludwig et al. 1999a; Ludwig et al. 1999b). Ecosystem composition, structure and spatial configuration also 42


influence wind directions and speeds, humidity, air temperatures, localised turbulence and convection, and the formation of cumulus (Lyons et al. 1996; Hayden 1998; Saunders et al. 1998; Ray et al. 2003; Baldocchi et al. 2004). In agroecosystems, native forests and grasslands are often modified or replaced with non-native species that are not entirely suited to the influx of energy and material into these new agro-ecosystems (Freebairn and King 2003; Williams and Saunders 2005). The spatial configuration of land use can amplify this mismatch and cause new landscape states (large departures from historical conditions) to emerge that reflect disaggregation and a net loss of connectedness, resulting in ecohydrologically ‘leaky’ systems (DeAngelis et al. 1989; Ludwig and Tongway 2002; Williams and Gascoigne 2003). These same modifications also have the ability to alter atmospheric circulation and precipitation budgets in terms of distribution and magnitude (Charney 1975; Mylne and Rowntree 1992; Eltahir and Bras 1996; Ray et al. 2003; Walker 2003; McAlpine et al. 2007), often the bane for farmers. The spatial configurations of both woody and herbaceous ground cover appears to be particularly important for capturing both water and nutrients in overland flows at patch (0.001 ha – 0.1 ha) to hillslope (1 ha – 100s ha) scales (Ludwig et al. 1999a; Loch 2000; Ludwig and Tongway 2002). In heterogeneous native ecosystems, the specific functions performed by numerous species result in complex systems comprised of self-organising subsystems that are connected by non-linear feedback loops (Pignatti 1996; Veldkamp et al. 2001). Our understanding of these dynamics which determine the links and non-linear feedbacks between land surface cover at hillslope scales and the ecohydrological functioning of an entire subcatchment (10000s ha) is, however, somewhat limited (Wainwright et al. 1999; Sivapalan 2003). To address this issue requires moving our current focus of generating information at scales related to entire river basins (Bartley et al. 2003; Lu et al. 2003), to finer spatial scales that are inclusive of the social and economic drivers associated with land management decisions and activities undertaken by farmers.

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One approach to gain a better understanding of the non-linearity in feedbacks between social and biophysical systems relative to land cover change (farming), such as the time-lags between management actions and landscape responses (and vice versa) is to utilise and build on the knowledge of the very people that manage the landscape (Kropff et al. 2001; Heathwaite 2003). Farmers form markedly differing perspectives and values of their landscapes, depending upon factors such as land use history, property size, family traditions, age, education, finance, and social networks. While it is acknowledged that many of the idiosyncrasies of a particular landscape and its functions may yet be learned from indigenous peoples (Chisholm 1994; Ross and Pickering 2002), there is also a great wealth of accumulated experience of successive generations of farmers experimenting with their management systems in order to improve the performance and sustainability of their agroecosystems (Sumberg and Okali 1997). In this paper, we present an innovative 3D graphic survey technique, referred to as the Graphical Landscape Map Survey (GLAMS), which captures the expert knowledge of farmers at hillslope scales. The aim of GLAMS was to provide information on the values and behaviours of farmers who manage land use under marked seasonal variations in precipitation (extended dry periods) and with the risks associated with ecohydrologically dysfunctional or ‘leaky’ landscapes. GLAMS used a variable spatial resolution to account for possible differences in perspectives of land use and ecohydrological management among three property sizes: small (< 100 ha), medium (100 – 500 ha), and large (> 500 ha) farms. It explicitly incorporates farmer’s experiences in managing the complex feedback systems that arise between climate, topography, ecohydrological functioning, and the land uses that are applied on this biophysical template. Some of the results and implications from the survey are critiqued and discussed, and suggestions for improving the GLAMS method provided.

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3.2 Method 3.2.1 Case Study Area The survey focussed on an area (27º00´S, 152º00´E) within the Western Catchments Natural Resource Management (NRM) region of the Upper Brisbane River catchment, Southeast Queensland, Australia (Figure 3.1). The elevation ranges from approximately 100 m in the east to approximately 900 m in the north-west. The climate is sub-tropical with average annual precipitation ranging from 724 mm at Cooyar, 842 mm at Crows Nest to 1158 mm at Ravensbourne, occurring predominantly (~70%) in the summer months (November to March). Like most of northeast Australia, the El Niño-Southern Oscillation (ENSO) causes highly variable annual rainfall, as demonstrated by comparing the highest total for February at Crows Nest of 769 mm in 1883 with just 12.2 mm recorded in 1919 (QDPI 2005).

Figure 3.1. The study area boundaries (dash-dot line) within the Western Catchments of the Upper Brisbane River (27º00´S, 152º00´E), Southeast Queensland, Australia. Shading represents the 200 m, 500 m, 750 m and 1000 m contours.

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Land use for the larger properties is comprised mainly of cattle grazing on native pastures in cleared or thinned native forests. Pasture management involves the control of native tree regrowth and weeds, and balancing the effects of fire and grazing pressure to improve the response of palatable pasture species such as black spear grass (Heteropogon contortus) (McIvor et al. 2005). The removal of competitive vegetation species, use of fertilizers, tapping of ground-water, and in-stream impoundments, all increase as grazing practices intensify (McIntyre and Martin 2002). On the smaller property sizes within the study area, hobby or ‘lifestyle’ farmers undertake a variety of alternate land uses such as fruit crops, agroforestry, olives, cut flowers/foliage production, and conservation. Recent land restoration works across all property sizes include alterations to local topography such as contour bunds and grassed waterways on steeper slopes, as well as off-stream impoundments and watering points, and to a lesser extent fenced regeneration areas along riparian corridors.

3.2.2 Survey Participants GLAMS was applied in a series of workshops with farmers from four Landcare Groups (Crows Nest, Emu Creek, Ravensbourne, Rosalie North), with 27 farmers participating (n = 27) across the four groups. The farmers represented different land use management approaches based upon property size and biophysical systems (e.g. precipitation, elevation, lithology, soil type, slope, topographic position). It is acknowledged, however, that all groups were comprised of farmers that are active members of Landcare groups throughout the Western Catchments, a factor which could introduce a bias toward more sustainable land management practices. The findings of the first workshop were further clarified by presenting the results to the participating farmers in a second round of workshops, and recording their comments on the results of the initial survey.

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3.2.3 The GLAMS Method The GLAMS method was based upon a set of ‘Graphical Landscape Models’, which are illustrations in three dimensions that represented local sub-catchments familiar to the survey participants. The graphical landscape models illustrated a number of physical attributes including topographic sequences, aspect and slope combinations, drainage patterns, lithological substrates (Figure 3.2), and native ecosystems depicting different vegetation assemblages (Figure 3.3). The models were draped with a fine scale grid to assist farmers in accounting for paddock and property size, while allowing a variable grid resolution to ensure the use of the full extent of the map: 

Small farms of < 100 ha = 25 m grid resolution;



Medium farms of > 100 to < 500 ha = 50 m grid resolution; and



Large farms of > 500 ha = 100 m grid resolution.

Figure 3.2. An example of the GLAMS physical template showing local lithology (inferring soil type), slope, drainage, topography.

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Figure 3.3. An example of the GLAMS physical template showing native ecosystems (i.e. vegetation assemblages) that were likely to be present before clearing.

Although the landscape map showing the physical attributes (Figure 3.2) reflected five generalised lithology types, many participants chose to ignore these variations and focus solely on vegetation assemblages (Figure 3.3). Table 3.1 shows the three sets of questions used in the survey, with each question relating to a specific landscape map. Participants used specific codes (Table 3.2) representing land use types and management timeframes (Questions 1 and 2), as well as unique codes for ecohydrological risks (Questions 3i and 3ii). By drawing boundaries on the models where they would likely place a given land use, with lines effectively representing paddock fences, the codes were then placed within these areas to reflect the particular activity being undertaken at that time. Within each land use area, a land use code and management timeframe code indicated the typical land use applied in either an average rainfall season (Question 1) or during an extended dry period (Question 2). The term ‘extended dry period’ was used instead of drought as a sequence of wetter or dryer years is 48


deemed to be the normal, if somewhat chaotic, climatic pattern within areas affected by the ENSO phenomenon (Bowman et al. 2001; Franks 2004). In addition, terms such as drought can be a value judgement of an individual or group with major differences of what is actually being implied (McMahon and Finlayson 2003).

Table 3.1 The set of questions which participants used to complete GLAMS. Questions 1 and 2 were focused on the land uses that may be applied by the participant. Question 1 related to an average season where precipitation would be close to normal for that area, while Question 2 related to an extended dry period (i.e. perceived time of drought). Question 3 requested participants to consider the potential threat to their land use from one or more ecohydrological risks. Question 3i required the ranking of the list of ecohydrological risks provided (Table 3.2), while Question 3ii required the participants to ‘map’ where in the landscape these risks may then occur using specified symbols (Table 3.2).Question 3iii requested the participants to suggest the necessary spatial area that would need to be addressed if these risks were then targeted for restoration works. Question 1. i)

Consider the type of soil, available water, and hazards such as steeper slopes and creeks in the model, as well as your likely need for a return on that land use as applied over some minimal area. ii) Outline how you would develop land use across this landscape using your ‘ideal’ scenario. You may apply any set of land uses from the seven provided, over whatever areas you wish. You may apply one or more symbols per area. iii) Within each of these areas that you have marked with your choices for land use, also show how often you would likely re-consider this land use (months or years symbols). Question 2. i)

The land uses you picked have now been in use for a period of 20 years. The current situation is critical however, in that this is the fourth consecutive year of less than 400mm annual rainfall (extended dry period), while the current season outlook is for very much above average rainfall. There is very sparse ground cover, and most creeks and dams are dry. ii) outline what land uses you may change in this situation in order to protect your future economic return and natural resource condition. iii) mark the period before you would review the land use and/or change to other land use. Question 3. i)

Please rank from 1st to last, the following NRM issues in the order that you consider the greatest risk to your land use. You may rank one or more as being of equal importance. ii) Place a symbol showing the location of where you would likely apply remediation techniques. Consider the effect that remedial works may have upon water retention on slopes, as well as water storages, runoff, and downstream transport of sediments and nutrients. iii) Please consider how large an area you would wish to address within one year? You may answer by placing an ‘x’ in the appropriate circle under the column that suits your answer best (cells/Ha) (NB: remember your grid resolution).

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Table 3.2. Land use and ecohydrological codes as used by the participants to complete each question within GLAMS. Participants used the appropriate code by placing it within an area they had previously drawn upon the Graphical Landscape Maps. The land use code represented their choice of land use at that location within the landscape, while a second code indicated the change in land use over time during either an average season or an extended dry period. A set of codes for ecohydrological risks were also used by the participants to indicate their relative importance, where these risks may occur in the landscape, and how large an area would be needed to address the risk. Land Use Codes Type of land use

Timeframe before land use changes

Cro Frt Agf Gip Gnp Gnf Ley Con

1 - 2 m = one to two months 2 - 3 m = two to three months 3 - 6 m = three to six months 6 - 12 yr = six motnhs to a year 1 - 2 yr = one to two years 2 - 5 yr = two to five years 5 - 10 yr = five to ten years > 10 yr = more than 10 years x = permananent

= dryland cropping = fruit = agroforestry = grazed improved pastures = grazed native pastures = grazed native forest = ley grazing (light grazing) = conservation area

Ecohydrological Risk Codes Type of ecohydrological risk

Importance rating

Area (ha) of remediation

Wav Wql Sre Gul Cbs Rip Evf

1 2 3 4 5 6 7

1 2 3 4

= water availaibility = water quality = sheet and rill erosion = gully erosion = creek bank stability = riparian condition = envitonmental flows

= high = = = med = = = low

= < 10 = 10 - 25 = 25 - 100 = > 100

Overlay Template (all Q’s) Landscape position Alv Rip Losl Misl Upsl Bcap Any

= alluvial = riparian = lower slope = mid slope = upper slope = basalt cap = any landscape position

Question 3 focussed on the perceived impacts of applying the previously defined land uses on ecohydrological functions within the landscape, particularly from a perspective of the likelihood of an ecohydrological risk such as soil loss affecting production. Ecohydrological

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risks relate to the management effort that would be potentially needed to address symptoms of dysfunction between vegetation cover, agricultural land uses, and flows of water across the hillslope and entering streams. Symptoms may be expressed as sheet and rill erosion (e.g. sediment and nutrient transport from hillslopes to streams), gullying (i.e. head-ward erosion of slope depressions on hillslopes), water availability (i.e. lack of), water quality (e.g. sediment and nutrient loads), stream bank stability, riparian condition (e.g. vegetation cover and health), and environmental flows (e.g. low or no flows). In Question 3i (Table 3.1), participants were firstly requested to rank in order from highest to lowest priority (1 – 7 respectively), which of these ecohydrological factors they deemed as being a risk to production. Question 3ii focussed on what locations in the landscape these risks may occur at, while Question 3iii requested participants to consider how large an area would be needed to address these issues effectively (i.e. remediation).

3.2.4 Post-Survey Data Analyses First, the survey responses from the four groups were converted to quantitative measures based on hectares for each land use, their location(s), changes between average or extended dry periods, and variations due to adverse ecohydrological functioning. This was achieved using the grid cell size proportional to the property size class of that particular farmer, multiplied by the number of cells occupied by a land use type. The locations of each land use were derived using an overlay template of landscape positions, and the number of cells for a given land use class within each landscape position recorded. Lithological variations were not included in subsequent analyses as very few respondents indicated the soil type of their respective landscape. Second, once in tabulated format within a digital database, the data for each respondent and each question were imported into Netica (Norsys Software Corp 1998) to create a Bayesian Belief Network (BBN). A BBN represents relationships between propositions or

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variables, and includes data uncertainty and unpredictability or imprecision in the model. Because the BBN is a network, the effects of changing variables or values are transmitted throughout the model according to conditional probabilities tables (Cain 2001). This capability, as well as the long-run frequency property of BBNs, provides an opportunity to incorporate additional data from prior cases, such as monitoring and measurements, to improve estimates of risk factors (Ellison 1996; Leonard and Hsu 1999; Cain 2001). The probability (P) of a factor (A) occurring (e.g. land use type, ecohydrological risk) under a given set of conditions (B) (e.g. average, extended dry seasons) gives P(A|B) [0:1]. A P estimate was derived for each combination of eight land use types, seven ecohydrological risks, seven landscape positions, and nine temporal periods ranging from two months to very long term. Every time period for each application of a given land use, whether it changed or not, was treated as a unique case. In this way, decisions involving no change in land use were also seen as vital information, resulting in a much greater number of cases (> 1,000) being included in each analysis than just the number of respondents. Finally, the cases were entered in an iterative manner (looped) approximately ten million times to allow the P estimates to take into account a priori conditions. That is, former cases weight the conditional P value according to how many times they have occurred as well as their respective values. Data analyses focused on: 1) size of land use areas; 2) farmer decisions on which land cover to apply in what part of the landscape; 3) when the land uses are likely to be applied in average years; 4) when the land uses are likely to change during extended dry periods; 5) values and importance attached to ecohydrological functions that support production and the natural resource base; 6) location of ecohydrological risk areas; and 7) the approximate area over which restoration works would need to be undertaken in any one year if the problem was to be addressed. Major findings are presented for all properties combined, as well as for how the three property size classes affected perspectives and decision making.

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3.3 Results 3.3.1 Land Use Positions within the Landscape Across all property sizes, a consistent perspective of farmers was that riparian areas should be used for conservation (P = 0.75). Conservation, however, did not necessarily equate to no other land use, but rather these areas were managed more selectively than adjacent cropping and grazing paddocks. The results indicate that the smaller and larger property sizes managed riparian areas for differing reasons; nonetheless, all three property size groups indicated that riparian areas are very important and required sensitive management. There was a consistent preference for the smaller farms (< 100 ha) to locate fruit crops on more moist and fertile lower slopes (P = 1.00; Figure 3.4a), whereas medium-sized properties (100 – 500 ha) preferred basalt caps for their fruit crops (P = 0.50; Figure 3.4b) as much as lower slopes (P = 0.50). The likelihood of the large properties (> 500 ha) applying a fruit crop in any part of their landscape was low (P = 0.14; Figure 3.4c). In contrast, grazing native pastures had a high likelihood of being applied anywhere in the landscape for both large (P = 0.63) and small farms (P = 1.00), although very low for medium-sized farms (P = 0.13). Cropping, usually irrigated pasture, had an even chance of being applied on alluvial flats or lower slopes within large properties (P = 0.50), although small farms were unlikely (P = 0.14) to apply this land use at any time. The smaller farms showed a strong probability of grazing improved pastures on alluvial flats (P = 1.00) and to a lesser extent agroforestry on lower slopes (P = 0.50) or basalt caps (P = 0.50). Applications of ley grazing, a period of light or no grazing pressure, was always (P = 1.00) applied on upper slopes by the medium sized farms, while grazing in native forests was undertaken upon the basalt caps (P = 0.50). Ley grazing was often applied on basalt caps (P = 0.50) and in riparian areas (P = 0.50) by the larger properties, while this group also showed a moderate preference for grazing improved pastures in any part of the landscape (P = 0.43), and grazing in native forests on upper slopes (P = 0.40). 53

Figure 3.4. Probabilities for different land uses in specific landscape positions for (a) small < 100 ha; (b) medium 100 – 500 ha; and (c) large > 500 ha farms. (see Table 3.2 for definitions of abbreviations).

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3.3.2 Land Use Changes and Timeframes During the course of any season, year, or decade, climatic variations are certain to play a major role in determining farmer’s land use decisions. GLAMS indicated that the greatest likelihood of change () for the farmers surveyed, were for land uses based upon cropping (Figure 3.5) or grazing in native forests (Figure 3.6) in either average years or extended dry periods. The alternative land uses that were substituted during extended dry times showed different responses for the three property size groups. For cropping (usually irrigated pasture), the smaller farms exhibited a low likelihood of applying this land use at any time, irrespective of the seasonal conditions (P = 0.13; Figure 3.5a and 3.5b). The greatest  between average and extended dry seasons for cropping was reflected in the P estimates for the medium-sized farms (P = 0.38; Figure 3.5c). Of note, however, was that under average conditions some farmers (P = 0.25) would not undertake this land use in any case. For those farmers that did undertake cropping, a small number indicated a possible change to either grazed native pasture (P = 0.25) or ley grazing (P = 0.25). These changes were likely to occur after three to six months of ongoing dry conditions, compared to six to 12 months during average seasons (Figure 3.5d). A different response was observed for the larger properties (Figure 3.5e and 3.5f), where the same likelihood for a change to grazed native pasture was observed for average or extended dry seasons (P = 0.17), although the timing of this change was approximately six months earlier during drier periods. The changes from a land use based on grazing in native forests were most prominent for the smaller farms, which indicated that a total replacement of this land use (P = 1.00) occurred when the dry conditions persisted longer than approximately two years (Figure 3.6a). This compared with a small proportion who otherwise changed during average seasons (P = 0.20). Changes were predominantly to ley grazing (P = 0.80) beginning after only two dry months, whereas a small proportion changed to conservation (P = 0.20) when dry periods extended beyond two years (Figure 3.6b). For both the medium-sized farms (Figure 3.6c) and 55


larger properties (Figure 3.6e), only a small proportion changed from grazed native forests during extended dry conditions compared to average conditions (P = 0.12 and P = 0.20 respectively). These two groups indicated that ley grazing would be substituted instead of grazing in their native forests, although the medium-sized farms changed land use approximately 12 months earlier than the larger properties (Figure 3.6d and Figure 3.6f). Results were not as clear for the other land uses indicated by the farmers, except to suggest that once applied, uses such as agroforestry, fruit crops or conservation, become more permanent.

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Figure 3.5. Probabilities for a land use change from cropping for properties groups < 100 ha (a, b); 100 – 500 ha (c, d); and > 500 ha (e, f). The left column shows the magnitude of change (Δ) between average (○) and extended dry seasons (×). The greatest Δ observed was for the 100 – 500 ha group (ΔP = 0.38), less for the > 500 ha group (ΔP = 0.17), while the < 100 ha group are unlikely to apply cropping at any time (P = 0.13). The temporal period for a change in land use during extended dry seasons was six months earlier for the 100 – 500 ha group, while approximately 12 months earlier for the > 500 ha group. The right column shows the land use that was substituted during an extended dry season. The 100 – 500 ha group were equally as likely (P = 0.25) to change from cropping (●) to either grazed native pasture (+) or ley grazing (□) after six months, while the > 500 ha group showed only a low probability of changing (P = 0.17) to grazed improved pasture after 12 months, the remainder continued with a cropping land use. (note: the temporal scale is not linear).

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Figure 3.6. Probabilities for a land use change from grazing in a native forest for properties groups < 100 ha (a, b); 100 – 500 ha (c, d); and > 500 ha (e, f). The left column shows the largest change (Δ) between average (○) and extended dry seasons (×) was for the < 100 ha group (ΔP = 1.00), considerably less for the > 500 ha group (ΔP = 0.29), while the > 500 ha group showed only a low probability of change was likely (ΔP = 0.20). The right column shows that the < 100 ha group changed from grazing in native forests (◊) to ley grazing (□) progressively from two (P = 0.20) to approximately 12 months (P = 0.80), with some even considering conservation (Δ) after two years (P = 0.20). A small proportion of both the 100 – 500 ha and > 500 ha groups changed to ley grazing (ΔP = 0.15 and ΔP = 0.20 respectively) but at different times (six and 12 months respectively). (note: the temporal scale is not linear).

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3.3.3 Ecohydrological Risks Question 3 focussed on the importance of ecohydrological risks, their locations within the landscape, and an indicator of how large an area farmers considered necessary to address them through remedial works such as controlling gully erosion with contour banks. Considerable variation was found in the ranking of ecohydrological risk within each group as well as between groups. Many participants selected mid-values for a risk, which indicated that the factor is neither the most or least important concern toward sustaining production and landscape functioning. For Question 3i, there was consistent agreement for the smaller farms that water quality (P = 0.80) and water availability (P = 0.60) were of significant importance (Figure 3.7a). Both riparian condition and environmental flows showed some consistency in being rated important (P = 0.40), but to a lesser extent sheet and rill erosion (P = 0.25). In contrast, a small proportion of this group viewed water availability (P = 0.20) and environmental flows (P = 0.20) as being of low importance. The results for the medium-sized farms also varied. There was some agreement that sheet and rill erosion (P = 0.38), gullying (P = 0.30), and water availability (P = 0.27) were important, but there was low agreement for creek bank stability (P = 0.18) and environmental flows (P = 0.14) (Figure 3.7c). The importance of water availability (P = 0.71) was consistently ranked as high for the larger properties, while sheet and rill erosion (P = 0.50), water quality (P = 0.33) and gullying (P = 0.29) found moderate consistency in being ranked as important (Figure 3.7e). The likelihood of a low rating for environmental flows (P = 0.27) and riparian condition (P = 0.25) was surprising, however, as these areas directly contribute toward water availability and water quality.

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Figure 3.7. The difference between perspectives on the importance of ecohydrological risks (left column) and where these are most likely to occur within the landscape (right column) for property sizes < 100 ha (top row); 100 – 500 ha (middle row); and > 500 ha (bottom row). Using the left hand column for each property size group to find which ecohydrological risks were most important for that group, the right hand column may then be referenced to find where these risks may occur at particular locations within the landscape. Across all groups, the most consistently ranked risks of highest importance and their locations were water availability in riparian areas or on upper slopes, and water quality in riparian areas. (see Table 3.2 for definitions of abbreviations). 60


Question 3ii provided information on where in the landscape ecohydrological risks were most likely to occur. For the small farms, riparian condition, water quality and environmental flows were seen equally as being of high importance in riparian areas (P = 0.75), whereas creek bank stability (P = 0.67) and water availability (P = 0.60) were also important in riparian areas (Figure 3.7b). Sheet and rill erosion was indicated to occur on basalt caps (P = 0.50), with the remainder of the risks not showing significant consistency as to where they may arise, other then ‘at any point in the landscape’ (P = 0.33 – 0.25). The medium-sized farms shared a consistent view that water quality and environmental flows were of equal importance (P = 0.75) in riparian areas (Figure 3.7d). On these farms, riparian areas are often used for production purposes, such as providing feed and water for grazing cattle. Water availability and sheet and rill erosion were also of high importance (P = 0.60), but these risks occurred on upper slopes. For the larger properties, there was a consistent view that riparian areas were of very high importance for a number of ecohydrological functions (P = 1.00; Figure 3.7f). Water availability, however, was just as important on upper slopes (P = 0.40) as in riparian areas (P = 0.40), or more so if mid-slopes (P = 0.20) were included as ‘slopes’ per se. Sheet and rill erosion was also consistently perceived as a risk on mid slopes (P = 1.00), whereas there was low consistency in the view that gullying was a concern on mid slopes (P = 0.25). The response to Question 3iii, that is, how large an area did farmers consider necessary to effectively address an ecohydrological risk, suggested a low level of consistency in perspectives among and between farm-size groups. For the smaller farms, sheet and rill erosion (P = 0.50) needed an area of 10 – 25 ha, gullying (P = 0.50) 10 – 100 ha, and creek bank stability (P = 0.50) between 25 – 100 ha. Water quality and environmental flows (P = 0.50) both needed > 100 ha to be addressed effectively. The medium-sized farm considered that water quality, creek bank stability, and gullying (P = 0.50), would only require < 10 ha to

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be addressed effectively, whereas riparian regeneration and water availability (P = 0.50) would need an area of between 10 – 25 ha. The larger properties indicated that regeneration of riparian areas (P = 1.00), gullying (P = 0.67), creek bank stability (P = 0.60), and water availability (P = 0.57) needed to be addressed over 10 – 25 ha. For improving water quality, however, the findings suggested that anywhere between 10 ha (P = 0.50) and over 100 ha (P = 0.50) would need to be undertaken to address this issue. Finally, there was very high consistency (P = 1.00) in the view that environmental flows needed to be addressed over areas > 100 ha for the problem to be addressed effectively.

3.4 Discussion 3.4.1 Benefits and Limitations of GLAMS For natural resource and catchment management to be truly effective, the local community must play a key role in the formulation of plans, targets, and management actions (Ewing et al. 2000). Although farmer participation is generally a good thing (Vanclay 2004), farmers are busy people so they are often perturbed by questionnaires that desire their participation in ways that are not intuitive to them or bombard them with numerous questions regarding their personal choices and values on land use or ecohydrological management. Unlike other methods based upon survey questions analysed through a geographic information system or spreadsheet (Smith and McNeill 2001; Hall et al. 2004), GLAMS is based on 3D graphical models and therefore takes advantage of the old adage – a picture is worth a thousand words. Based upon a second round of workshops with farmers from the Western Catchments, GLAMS was said to be reflecting the general trend of land use across the region. This was despite the high diversity of land use types and management approaches that are found within these agroecosystems. GLAMS 3D graphical landscape models were easy for participants to 62


interpret, therefore rapid to implement, cost efficient, and relevant to the spatial and temporal scales of farming activities. While GLAMS depicted the local topographic and ecosystem characteristics of the surveyed area sufficiently well, for other landscapes (e.g. floodplains), the aim would be to simplify the complexity of the landscape to illustrate only those dynamic features that are likely to affect a farmers decisions toward the application of a given set of land uses. For future applications of GLAMS, it is anticipated that an electronic version could be used that is based on A3 sized digitizing tablets. In this way, farmers still draw on the 3D landscape models while the lines and codes used would be automatically stored in a computer database, along with the appropriate spatial transformation to account for differences in property size and area of land use, while a BBN automatically receives the input from the database and generates probability statistics that may be viewed concurrently. While these type of computer aided questionnaires which use graphic models are good for conveying complex questions to participants (De Vaus 2002), GLAMS should be viewed more as a tool to facilitate effective communication between farmers and the research or extension organisations that represent their interests. As farmers themselves were able to define what was happening in the landscape and where and when, GLAMS may be termed a ‘demand pull’ participatory research extension procedure that is more aligned with farmers actual information needs (Marsh and Pannell 2000), a factor which has often been ignored by conventional research approaches (Tripp 1989).

3.4.2 Specific Findings from GLAMS We found that landscape position strongly influenced decisions on what land use was applied where and when. This is partly a consequence of the different biophysical processes that take place in certain locations within the landscape. Sensitive locations within the landscape are overwhelmingly riparian areas, as well as upper slopes and basalt caps. The effect of property size was also a major factor influencing land use decisions for different

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parts of the landscape, as well as the intentions of subsequent management. For example, all groups’ value riparian areas for conservation, but larger farms would almost certainly utilise riparian areas for light grazing and movement of cattle between paddocks, whereas farmers on small farms would more likely exclude production from riparian areas to maintain their natural values. In general, the more intensive land uses occurred at locations in the landscape where soil moisture and fertility are more likely to lead to higher yields, such as lower slopes and run-on areas (Halvorson and Doll 1991; Noble 1997; Veldkamp et al. 2001). The likelihood of precipitation and subsequent water availability during critical plant growth phases directly affects farmers decisions about what crop to plant and when (Jakeman and Cuddy 2003). Whereas there are certainly differences among the property size groups as to what would be deemed an ‘average season’ compared to that of an ‘extended dry period’, all groups indicated some degree of change when affected by a prolonged period of lower than expected precipitation. In ongoing dry seasons, changes in land use tend toward being less intensive and more focussed on the maintenance of good resource condition. The change in land use during an extended dry period also occurred much sooner compared to the average season. Of particular importance, however, were the timings and magnitudes of change among the farm size groups. Our findings indicate a general relationship where as property size increased the proportion of farmers changing their land use over time was lower (Figure 3.8). In addition, the temporal threshold before a change in land use became evident was progressively lagged as property size increased. Previous studies suggest that the influence of property size significantly affected farmer perspectives and flexibility for management of native vegetation (Hamilton et al. 1999), the over-utilisation of resources or adoption of new sustainable land practices (Cary et al. 2002), as well as membership with Landcare organisations and the preparation of property management plans (Curtis et al. 2000). The trend between property size and management preferences, however, may differ between regions depending upon the

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relative profitability and appropriateness of a management action for a given location (Barr and Cary 2000; Cary et al. 2001).

Figure 3.8. The general relationship between property size and the responsiveness and flexibility of land use management.

With the advent of a new wave of ‘lifestyle farmers’ there is a blurred boundary between the small farms that rely only on-farm income compared to those who source offfarm income, which allows them to adapt rapidly to changed circumstances (Curtis et al. 2000; Avery and Coster 2003). This may include the ability to apply no land use at all during extended dry periods, or fence-off areas exclusively for nature conservation. While the same ideologies may be shared with farmers who manage larger scale properties, it is more likely that their economic situation may not allow the flexibility to do so. Farmers who may be preoccupied with short-term survival are limited in their ability to implement more

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sustainable practices (Campbell 1995) or, in other words, ‘it’s hard to be green when you’re in the red’ (Vanclay 2004). It has been suggested that as grazing intensity increases, key nutrients are likely to be lost at the landscape scale rather than just being redistributed (Sparrow et al. 2003), which perhaps underlies the fact that water quality in the streams of the study area have continued to receive low ratings over recent years (MBWCP 2005). Findings for the Liverpool Plains region of Australia show that grazing on steep forested lands contributed nine times more sediment than pastures on gentler slopes (cropping was 84 times greater), while the largest contribution at the catchment outlet was sourced from subsoil erosion of gullies and channels within and between the different landuse areas (Wallbrink et al. 2003). In contrast, observations in tropical woodlands of northeast Queensland show that most of the sediments generated from sheet and rill erosion on hillslopes may not actually enter the stream system (Bartley et al. 2003). While the application of contour bunds in association with good herbaceous cover (e.g. 20-30%), may dramatically reduce by 80-90% the erosion and redistribution of sediment and nutrients across hillslopes used for cropping (Freebairn and Wockner 1986a), farm dams are still likely to capture mobilised sediments and nutrients from fertilizers and cattle effluent (Lewis 2002). The sediments that are captured by farm dams may reduce their capacity over time, while water quality may also be impaired due to incoming cattle effluent, pesticides and herbicides, as well as in-situ algal blooms. Following intense storms that result in localised flooding, these dams may contribute toward adverse downstream impacts once their storage capacity is exceeded. While the results from GLAMS showed that water availability and quality along with sheet and rill erosion were the most important ecohydrological risks to farmers, it was surprising to find that water quality in riparian areas were deemed to be of greater importance than for water running off upper and mid slopes into farm dams. This

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leads to a poignant question: why do many farmers value water quality in riparian areas more highly than for farm dams? The ecohydrological findings from GLAMS could be further validated using a hydrological process model (Kinsey-Henderson et al. 2005). Although such models tend to be sensitive to small changes in their parameters (Newham et al. 2003), they are nonetheless useful for exploring how changes in land use at different spatial and temporal scales affect the ecohydrological system. For example, outputs from a hydrological process model have been combined with expert knowledge rules to simulate potential climate and land cover changes on sediment and nutrient transport (Ryan and McAlpine 2005). As this type of modelling enables the examination of numerous alternative land use operations and the response of landscape systems under a range of conditions, they will be an increasingly important tool of discovery for NRM (Veldkamp and Lambin 2001; Williams and Saunders 2005).

3.5 Conclusions 

We found that GLAMS was a simple, rapid and low cost technique that allowed farmers to succinctly convey spatially and temporally relevant information on their preferred land uses under variable climatic conditions while managing ecohydrological risks.



The findings indicated that property size strongly influences farmer perceptions, where as farm size increases the proportion of farmers changing their land use over time decreased, while the temporal threshold is progressively lagged before a change in land use becomes evident.



There is an apparent disparity between ecohydrological systems and their management, particularly for the availability and quality of water captured and stored in farm dams, and subsequent effects on environmental flows and water quality for the subcatchment. 67


3.6 Acknowledgements The authors wish to thank the farmers and graziers of the Crows Nest, Emu Creek, Ravensbourne, and Rosalie North Landcare Groups for their time and assistance with the survey and acknowledge Bruce Lord of the Western Catchments NRM Group for logistical support. We also thank Dr Carl Smith for advice on Bayesian statistics and anonymous reviewers for suggesting manuscript improvements.

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Ryan, J.G. (2007) PhD Thesis - Chapter 4, LEACS

CHAPTER 4 FORMULATION & TESTING OF A LANDSCAPE ECOHYDROLOGICAL ATTENUATION CONFIGURATION SYSTEM (LEACS)

4.1

Introduction

4.1.1 Why Ecohydrological Restoration? Sub-tropical thunderstorms can rapidly generate large amounts of runoff with high velocities and turbulence across hillslopes. Depending upon the soil type and its condition, management practices, vegetation cover and catchment characteristics, significant amounts of sediment with high concentrations of organic acids and other residual agrochemicals can be entrained in stormwater runoff (Gippel 1989; NRW 2004b). The size and mass of particles are highly spatially variable, but for a particular soil and individual sediment type turbidity and mass of suspended matter may be consistent (Duchrow and Everhart 1971). This relationship allows sediment sources to be tracked and an economical evaluation of costs for restoring source areas in the catchment to be carried out. Reducing sediment delivery to streams is necessary to arrest the movement of sediment moving down a stream bed (Rutherford 2000), and also to manage turbidity which increases raw water supply treatment costs (Weber 2005). Many natural resource management (NRM) and catchment groups in Australia are currently establishing designs to achieve various end of catchment (EOC) targets for regulating outputs of water, salt, nutrients and other pollutants (Cullen 2004). Reductions of sediment and nutrients entering streams (and inevitably to Lake Wivenhoe) are stated as a high priority in strategic NRM plans for the Western Catchments of the Upper Brisbane River (SEQWCG 2004; SEQWCG 2005). Setting realistic EOC targets without appropriate monitoring and evaluation strategies, however, raises concerns over their applicability across a range of spatial and temporal scales (Bartley et al. 2004; Cullen 2004).

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4.1.2 The Effects of Plant Functional Type on Ecohydrological Systems It has been suggested that natural selection favours form-behavioural strategies that maximise productivity and carbon gain for a given niche (Mooney 1974; Eagleson 2002). The physiological adaptations and change in morphology over time of a given plant functional type (PFT) in its native ecosystem, are very important factors for understanding how land cover change may affect the future behaviour of a catchment. This is particularly true for long-lived plants such as eucalypts. These changes include leaf area index (LAI), stomatal conductance, foliar nutrient status (nitrogen), interception of rainfall, evapotranspiration, rooting depth, partitioning of carbon into biomass and the production of organic matter (Eastham et al. 1994; Landsberg 1999; Eamus 2003). Pastures have lower LAI than native vegetation (trees), so their interception and rates of evapotranspiration are also much lower (Eastham et al. 1994; Zhang et al. 1999). Typical LAI values for pastures are in the range of 0.5 -1.0 while for native eucalypt woodlands, allowing for variations for leaf angle and canopy structure, values range from approximately 0.5 to 1.5 (Montagu et al. 2003; Leuning et al. 2005). Photosynthetic rate, LAI, stomatal conductance, net primary productivity and the accumulation of biomass are related to long-term rainfall, atmospheric CO2 levels and soil nutrient and moisture status. For native vegetation, increasing aridity and radiation reduce stomatal conductance and LAI, while nitrogen content per unit leaf area increases (Farquhar et al. 2002; Pitman et al. 2004). In many Australian soils nitrogen is limited, so native species need to continually balance this factor against limited precipitation. Zeppel et al. (2006) suggests that vegetation species will exhibit similar LAI and rates of water use where sites have similar climate. Stape et al. (2004), for example, found that the LAI of eucalypts increased by 0.3 for each 100 mm increase in annual rainfall near Entre-Rios, Brazil. LAI will also depend on a species growing environment, such as the effect that topographic plan and profile curvature has on available soil moisture (Montagu et al. 2003; Eamus et al. 2006).

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The basal area of a patch, foliage projective cover (FPC) and LAI all have a significant impact on evapotranspiration, and therefore, are very important for ecohydrological functioning within the landscape (Eamus et al. 2006). As soil organic carbon affects the stability of soil aggregates, infiltration and soil water holding capacity, where native vegetation is cleared soil condition often declines while susceptibility to erosion increases (Feller and Beare 1997; Valentin et al. 2004; Zhao et al. 2007). Native vegetation also provides essential functions to precipitation recycling during dry periods by tapping into deep groundwater during dry periods (Hutley et al. 2000; Pal and Eltahir 2001), while also forming symbiotic relationships with soil biota which are responsible for water and nutrient cycling in soils (Williams and Saunders 2005). In addition, without functional diversity of soil biota nitrogen may not be recycled into forms available to plants, but instead, lost as a greenhouse gas (N2O) to the atmosphere (Weier and Macrae 1992).

4.1.3 Hydrological Responses to Changes in Native Vegetation Cover Clearing native vegetation alters water dynamics from patch to hill-slope scales which results in greater water yields at the landscape and catchment scales (Zhang et al. 1999; Ludwig and Tongway 2002). Where native vegetation is modified or removed through land cover change, this can profoundly affect sediment supplies and runoff regimes up to several orders of magnitude (Ward and Elliot 1995; Brooks and Brierley 2000). These altered hydrodynamics have resulted in many streams increasing their width, depth, and rates of bank erosion over the last 150 years in Australia (Rutherford 2000). As the morphology of a stream is based upon relationships to the velocity, width, and depth of water flows, changes in sediment loads can also change the hydrodynamics of the entire river basin (Curry 1971). Once the system is thrown out of balance, self-reinforcing ‘positive feedbacks’ can continue to modify the system into the future (Brizga and Finlayson 2000a).

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While it is acknowledged that much of the sediment generation in streams comes from bank and bed erosion in the streams themselves (Wallbrink et al. 2003; Hateley et al. 2005), it is suggested that the marked increase in run-off volumes and the on-set of peak discharge and water velocities following major storm events, produce the conditions necessary for these erosional episodes to continue. Such concentrations of run-off cause soil erosion, gullying, and mass failure (i.e. slumping) on hillslopes, failure of stream banks, and results in sediment slugs forming on the bottom of streambeds (e.g. Figure 4.6 and 4.7). It has been estimated that approximately 60% of the Murray-Darling Basin (MDB) has sediment and nutrient loads in excess of 20 times the natural load, and in some cases up to 100 times historical loads (Prosser et al. 2003). Most of the problem areas are where hillslope erosion rates are combined with higher hillslope sediment delivery ratios, which mostly occur at higher elevations in the Eastern section of the MDB, resulting in approximately 20% of the catchments delivering 80% of the materials. That is, only small areas of a catchment are likely to contribute most of the sediment and nutrient loads (Prosser et al. 2003).

4.1.4 Issues with Restoring Ecohydrological Functioning Using Native Vegetation In the shorter term, it is uneconomical to remediate an entire landscape, so ecohydrological restoration needs to be focussed on areas where it can have most benefit (Prosser et al. 2003). These might be locations across the landscape which, if restored, will markedly affect the water balance of hillslopes, run-off regimes, and sediment and nutrient transport capacities to the stream network and catchment. O'Loughlin and Nambiar (2001) consider that with the re-establishment of native vegetation across hillslopes, local streams may readjust to rainfall/run-off regimes similar to a more native state. The effects of restoring native vegetation on a catchments hydrological response, however, may involve timeframes of decades or more (Dowling et al. 2004).

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While the general runoff response in catchments is non-linear (Willgoose and Kuczera 1995), the actual water depths and velocities that lead to sediment erosion and transport depends upon the type, size and configuration of vegetative patches across the hillslope (Loch 2000; Ludwig et al. 2007). It also depends on the physical and chemical composition and state of the soil which affects rates of infiltration, storage, drainage, run-off and erosion (Becker et al. 2004; NRW 2004b). Humans further modify the land surface through land use, making feedbacks between land cover and catchment response more complex. The spatial heterogeneity and temporal variability within states of vegetation (i.e. phenotype) and percentage cover, organic matter and surface properties, and soil structure and biological activity, masks the response of a catchment to changes in vegetative cover at a given location. That is, the complex reciprocal links between land cover change and catchment response are not immediate or obvious (Brooks and Brierley 2000; Ladson and White 2000). At present there is an absence of objective methods for ecohydrological restoration designs. Considering the high temporal variability of climate and the pending onset of global climate change, trials must span many decades. Long-term field trials across a catchment at high spatial resolution would be immensely beneficial to accurately estimate how hillslopes or a catchment will respond to adding a particular type of vegetation at a particular place, but such data are rare. In addition, it is not always feasible to wait decades before providing details on possible landscape restoration designs as dysfunctional ecohydrological systems will lead to the degradation of natural resources. Parameterising and running ‘what if’ scenarios in a simulation model, however, is on means through which estimates of the hydrological response of hillslopes and catchments to restoration designs using native vegetation can be assessed.

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4.1.5 Existing Modelling Approaches and their Limitations Existing approaches to simulating the hydrological functioning of catchments, and experiments to determine how these catchments may respond to changes in land cover, have often been at regional scales. Some examples of models which have been used to estimate long-term annual sediment and nutrient budgets at the regional scale are SedNet (Wilkinson et al. 2004), Environmental Management Support System (EMSS) (Newham et al. 2005; Weber 2005) and the Soil and Water Assessment Tool (SWAT) (Arnold et al. 1998; Sun and Cornish 2006). SWAT has been used extensively in the United States for predicting the impact of land management practices on yields of water, sediment, and agricultural chemicals in large complex watersheds over long periods of time (Gassman et al. 2007). Likewise, SedNet has been used widely in Australia for providing regional estimates of long-term annual sediment budgets (Prosser et al. 2002; Bartley et al. 2003; Bartley et al. 2004; Hateley et al. 2005). A local application of SedNet is the study of sediment erosion and deposition patterns for the Brisbane River conducted by Prosser et al. (2003b). While these models provide reasonable results for regional scale modelling, they are not ideally suited to provide detailed spatial estimates for the generation of runoff, erosion and deposition of sediment or nutrient export at hillslope scales. The main reasons is that these types of models are commonly supported by a data structure known as ‘lumped’, in which an entire reach of a catchment (or hillslopes) are treated as single ‘units’ where underlying physical properties (such as soil type, land cover, slope) and rainfall patterns are regarded as being functionally homogenous (Vázquez et al. 2002; Newsha et al. 2004). That is, lumped models is solved for each unit without attempting to determine the precise spatial distribution of the processes within the unit (Chen et al. 2004). The advantage is that lumped models generally require few parameters (Beverly et al. 2005). The bane for lumped models, however, is that as the spatial scale of the catchment increases, the catchment tends to attenuate the complex, local patterns of runoff generation

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and water fluxes (Wood et al. 1988). Simulated outputs may also be problematic to transfer to other ungauged catchments (Beverly et al. 2005). By masking the heterogeneity in underlying physical variables which drive hydrological interactions at the hillslope scale, lumped models cannot accurately produce estimates of exactly which parts of individual hillslopes are the most ‘leaky’. This factor reduces their potential to provide potential designs for restoring ecohydrological functioning, such as testing the effects of changing land cover from crops to trees by farmers or catchment groups. An alternative class of models rely on solving physical equations based on a ‘distributed’ set of inputs, such as continuous variables that are commonly associated with raster or grid-based data formats (Aral and Gunduz 2006). These ‘distributed models’ are often used at finer hillslope or sub-catchment scales due to the very large data sets they generate to account for the spatial heterogeneity in soil type, rooting depth, slope, aspect, surface roughness as well as variable rainfall patterns (Mendoza et al. 2002). Some of the major shortcomings of these models is the high level of parameterisation required, the uncertainty in the estimation of each individual parameter, initial conditions, and often the inability to acquire parameters in ‘ungauged’ and data poor catchments (Koren et al. 2004; Carpenter and Georgakakos 2006; Newham and Drewry 2006). Some examples of distributed models include the Système Hydrologique Européen (SHE) (Abbott et al. 1986), Areal Nonpoint Source Watershed Environment Response Simulation (ANSWERS) (Bouraoui 1994), Limburg soil erosion model (LISEM), and the Catchment Scale Multiple-Landuse Atmosphere Soil Water and Solute Transport Model (CLASS) (Tuteja et al. 2004; Tuteja et al. 2005). ANSWERS has been used to model infiltration on cropping lands on the Darling Downs region of Southern Queensland (Connolly and Silburn 1995), while LISEM has been used to map spatial patterns and volumes of erosion and deposition near Tongeren, Belgium (Takken et al. 1999). A later variant of SHE - MIKE SHE, has been used mostly in Europe (Christiaens and Feyen 2001)

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but also in the United States for modelling in the Florida wetlands (SFWMD-BCB 2003) and in semi-arid shrublands of California (McMichael et al. 2006). While being developed in Australia the CLASS model is relatively new, although it has been trialed in the Little River catchment near Yeoval, NSW (Tuteja et al. 2005). A distributed parameter hydrological model that has been applied to test tree belts configuration is the study by Ticehurst et al. (2005) at a site near Holbrook, NSW. The authors suggested that while the HILLS model included variables such as rainfall intensity and duration, hydraulic conductivity and storage capacity of two soil layers, and topographic shape, it did not allow spatial variability in rooting depth and potential evapotranspiration. This restricted the model’s ability to describe water movement through two soil layers between pastures and tree-belts. A local application of the distributed model GLAMS (Groundwater Loading Effects of Agricultural Management Systems) model to test the effects of variable buffer widths in riparian areas for removing sediments/nutrients from runoff (Paul Lawrence pers comm. 2003). This approach was based on independent runs of the model to test a number of ‘what if’ scenarios. The proposition quickly becomes intractable where land cover data is manually changed in raster grid cells using GIS editing procedures when testing the effects of numerous possible configurations of alternate land covers. An alternative is to use the values, such as water velocity, generated from each simulation iteration, to constrain land cover changes based on the magnitude of reduce and increase infiltration and storage across hillslopes.

4.1.6 An Automated Approach to Redesigning Leaky Landscapes - LEACS This Chapter highlights the purpose for and development of the Landscape Ecohydrological Attenuation Configuration System (LEACS). The full purpose of developing LEACS is to assess the benefits of establishing a common data link between a systems simulation model that stores and updates farmer probability estimates (P values) for applying

76


a given land cover at a given location, and a distributed parameter hydrological simulation model to supply hydrological response data on land cover changes per iteration to allow the update of the P estimates. That is, the aim of LEACS is to show ‘proof of concept’ for the potential to automate a process where changes in land cover at discrete locations across a hillslope (i.e. 10 m grid cells) are assessed for their ability to reduce water velocity and depth to a catchment for meeting a specified end of catchment (EOC) targets set by a catchment or NRM group. LEACS is broken into two independent (but nested) tests: i)

Phase I - a systems model for testing farmer land cover preference values (i.e. P values) in response to data derived from;

ii)

Phase II – a hydrological simulation of the effects of tree belts compared to pastures alone on steep hillslopes.

Phases I is a mathematical/ logic model built in the systems simulation software STELLA (ISEE 2005), while Phase II is a hydrological simulation model built with the distributed parameter hydrological simulation software MIKE SHE (DHI 2005). The intended purpose of adding human decision making feedbacks in terms of land use change when a given location (cell) of a hillslope becomes ecohydrologically dysfunctional (leaky, eroded, gullying, etc.), is cells that have the highest outputs values slowly lose their preference for the existing land cover as a value of -0.5 P per cell is added for every iteration in excess of the nominated EOC target, until P < 0.50 when a simulated land cover change to tree belts would take place. These cells are converted to alternate land covers (such as tree belts), and then reassess the impacts of this land use change on subsequent outputs. In this developmental stage of LEACS, the focus is on providing data on the effects of tree belts on water flow paths, velocity and depth, although a hypothetical set of sediment transport data is also generated to test LEACS Phase I.

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4.2

Method

4.2.1 Case Study Area: Maronghi Creek Catchment 4.2.1.1

Physiography and Climate The Maronghi Creek Catchment forms part of the Western Catchments NRM region.

The creek flows into the Brisbane River upstream of Wivenhoe Dam (1,165,000 ML) (Figure 4.1). The catchment is approximately 330 – 440 m in elevation with generally steep topography (Figure 4.2). The average annual rainfall is approximately 709 mm based on the nearby Cressbrook Dam (~ 6 km S), with about 70% falling in the summer months (October to March) as intense thunderstorms. The average potential pan evapotranspiration (PET) is approximately 1500 mm, which is twice that of average rainfall received so the catchment water budget is in deficit most years (Table 4.1).

study area

study area

N

0

5 km

Toowoomba

Figure 4.1. The location of the Maronghi Creek sub-catchment (dark shade) north-east of Crows Nest in the Western Catchments NRM region.

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Table 4.1. Classification and summary climate statistics for Crows Nest and Cressbrook Dam*. BOM Station Years of (Station No.) Record

Lat/ Long

Cressbrook Dam (40808)

Mar 1995 Feb 2006

27.1 S 292 152.4 E

Crows Nest (040382)

Jan 1893 Dec 2004

27.1 S 540 152.1 E

Ave Rainfall (mm)

Ave Temp (0C)

low

mean

high

min

max

Ave PET (mm)

sub-tropical 439 cool winter/ hot summer

709

1049

-

-

1505

sub-tropical 332 cool winter/ hot summer

842

1769

Jan- 17 Jan- 28 Jul- 5 Jul- 17

Climatic MASL Zone

* Compiled from pluviograph records generated by the Bureau of Meteorology Brisbane Office and MetAccess software (BOM 1996).

4.2.1.2

Geology, Lithology and Soils A number of geological provinces occur in the region, but this area of the upper

Maronghi Creek catchment is predominantly underlain by igneous rocks such as Eskdale Granodiorite and Crows Nest Granite complexes from the Permian period (Geological Survey

79


of Queensland 1975). The soils derived from these rocks are classed as Basic/Silpanic/Orthic Tenosols in the Australian Soil Classification system (Jacquier et al. 2003). The Land Resource Area (LRA) mapping determines the area as steep granitic hills (9c) with the common soil type known as Banca (Harris et al. 1999). These are slightly acidic (pH 6.0-6.5) shallow to moderately deep (0.3 – 0.9 m) very dark grey to brown, gritty, structure-less sand amongst rock outcrops on granite hills. These soils can be either underlain by coloured subsoils or a natural shallow hardpan, and are highly permeable and prone to erosion where disturbed. They are likely to have higher nutrient export than for clay soils due to their lower adsorption capacity and higher infiltration rates (Slattery and Brown 2003). The recommended land use for these soil is light grazing, with slopes greater than 20% planted with tree crops in graded rows that are aligned with slope contours (Harris et al. 1999).

4.2.1.3

Vegetation and Land Use Remnant native vegetation of the study area (Figure 4.3 and 4.4) is coded 12.12.12 and

12.12.8 by the Queensland regional ecosystem mapping and are both listed as ‘of-concern’ by the Environmental Protection Agency’s Regional Ecosystem Mapping. This ecosystem is described as grassy Eucalypt woodlands on Mesozoic to Proterozoic igneous rocks. Species assemblages for 12.12.12 comprise Eucalyptus melanophloia (Silver Leaved Ironbark), E. crebra (Narrow Leaved Ironbark) and Corymbia erythrophloia (Gum-Topped Bloodwood), with sub-dominant species of Eucalyptus tereticornis (Queensland Blue Gum) and Eucalyptus exserta (Queensland Peppermint). Species assemblages for 12.12.8 are Eucalyptus tereticornis and E. crebra, with sub-dominant species including E. siderophloia (Grey Ironbark), E. melanophloia, Angophora subvelutina (Broad leaved Apple), A. leiocarpa (Smooth Barked Apple).

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Grazing of pastures of Heteropogon contortus (Black Speargrass) and other grasses occurs on steep hillslopes that are void of the deep rooted native vegetation which binds the unconsolidated granitic soils (Figure 4.5). This along with large volumes of water run-off following storm events has resulted in many local streams having considerable deposits of granitic sands in their beds (i.e. sand slugs) (Figure 4.6). In addition, the steep topography coupled with low vegetation cover to obstruct and slow stormwater run-off, results in large magnitude peak flows of short duration but high erosion potential on unprotected riparian areas which has in some cases resulted in creek bank collapse (Figure 4.7). The location of the simulated tree belts is also shown in Figure 4.3.

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Figure 4.4. A native patch of grassy open eucalypt woodland typical for the study area (source: Ryan 2005).

Figure 4.5. Shallow granitic soils support grazing on improved pastures (source: Ryan 2005).

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Figure 4.6. A combination of a ‘sand slug’ with high nutrient loads in Maronghi Creek. (source: Ryan 2005)

Figure 4.7. Creek bank erosion and mass failure due to a combination of high-energy peak flows, the removal of riparian vegetation and cattle trampling. (source: Ryan 2005)

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4.2.2 Phase I: Development of the LEACS Model 4.2.2.1

Definition and Logic of LEACS LEACS is based on a process which utilises the feedback between the outputs from a

hydrological simulation (or water balance) model and the effects of land cover change through time (e.g. pasture to native vegetation). Land cover changes reflect actual decision making of farmers as a set of probability values (P values) as derived from Chapter 3. In this case the transition from grazing to tree belts is modelled, with the starting P value set at 0.75 likelihood that grazing will continue unless the performance is continually poor. The desired level for retention of water, sediment and nutrients on hillslopes performance of a land cover is the trigger for land cover change, which is based upon the . This performance measure (Pm) is the returned value from the hydrological model, while the difference between the Pm and the stated EOC target value (Pmrec) in terms of maximum water velocity or sediment load (t/ha/yr-1), is termed the performance error (Pme). The aim of LEACS is to reduce the Pme by adjusting the P value per grid cell at the end of each simulation run, until a land cover change takes place within that cell which causes the subsequent Pm (e.g. sediment load) to reduce to below the Pmrec threshold. Specifically, LEACS is designed to be a simple and repeatable process for use in conjunction with distributed raster grid based distributed hydrological process models, to find optimal landscape configurations for where native vegetation (e.g. tree belts) are to be used to restore ecohydrological functioning. That is, LEACS is a constrained optimisation process that is applied to raster grid data to find a particular solution to spatio-temporal patterns in hydrological data values. In this developmental stage of LEACS a hypothetical array of data (i.e. Pm) is used to show ‘proof of concept’ and to validate the performance of the model. A conceptual model shows the model components and flow of data between them for a single raster grid cell in LEACS (Figure 4.8).

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ET ArcMap raster grids (10 m)

M DEM

‘P values’ grid t0

soil landcover state T0

1.00 1.00

1.00 0.75 0.75 0.75

1.00 0.75

Land Cover Script

0.75

classify

hydrological process model

input

MIKE SHE

output 0.04 0.06

0.06 0.12 0.16 0.22

0.06 0.16

0.10

Rec_Pm 0.08 update

‘P values’ grid t+n

Pm Script recommended performance measure landcover state T+1

1.00 1.00

1.00 0.60 0.55 0.45

1.00 0.55

0.65

Land Cover Script

classify

Figure 4.8. Schematic of the LEACS model with MIKE SHE simulation and GIS data integration. For each time-step, the water balance model (MIKE SHE) estimates the runoff based upon the incident rainfall, slope, soil type and land cover in each raster grid cell. The ecohydrological functioning between pastures and native vegetation reflects differences in rooting depth, LAI, Manning’s M (hydrological roughness) and evapotranspiration (ET). These factors result in an output of variable water flow estimates for each raster grid cell. Where these differences are greater than the allowed EOC outputs, the probability (P) for that land cover to be applied in the next simulation run is reduced. Where the P falls below 0.50, the alternate land cover is applied instead (i.e. a transition from pasture to tree belts). The simulation is run again, and the process repeated until the EOC target is met.

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4.2.2.2

Development and Testing of LEACS Equations and Logical Statements

4.2.2.2.1 Logic of Model Set-up A set of interconnected equations and logical statements (Box 4.1) formed the basis of the STELLA model (Figure 4.9). The LEACS model uses the simple Euler’s method for calculating values at each time-step. That is, the computed values for variables designated as ‘flows’ provide the estimate for the change (Δ) in corresponding ‘stocks’ over the interval time-step (Δt) (ISEE 2005). Two hypothetical data sets (arrays) of Pm estimates (Appendix A) were used to assess the performance of adjusting model parameters. The ‘Tuner’ in the STELLA model allowed the resistance of the existing P values in response to the magnitude of the Pme. This process accommodated a particular resistance to change expressed by a farmer (see Chapter 3). In other words, tolerance of poor performance of either pastures or native vegetation needs to be accounted for over a number of seasons to take into account chance events such as extended dry periods followed by intense thunderstorms.

e

Pm Pm Script

1/ n  n   rec _ Pm   log  ai     i  1   Pm  e

where

Pm

= performance measure error

1/ n

    ai   i 1 

= geometric mean

rec _ Pm

= recommended performance measure

n

Pm = performance measure

Rec_Pm 0.75

Land Cover Script

Recommended performance measure – a value stipulated by a catchment board which provides the end of catchment (EOC) target value for maximum allowable tonnes/ha/year of sediment/nutrients etc. as a result of land use in the catchment

Rule I IF LC = ‘nat_veg’ THEN ‘P’ = 1.00 ELSE ‘LC’ = ‘pasture’ AND ‘P” = 0.75

Rule II If ‘NewP’ < 0.5 THEN ‘LC’ = ‘nat_veg’ ELSE ‘LC’ = ‘pasture’

Box 4.1. The set of performance measure error and land cover change equations used in the LEACS model.

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RecPm

Definitions

Pm

~

Tuner = a numerical multiplier to adjust for different values entered in RecPm to a standard value which modifies the Transform function Transform = converts the Pme to a value which moderately affects the adjustment of the P value in any one time-step NewP Conversion = a function which adds the value received from ‘Transform’ converter to the existing P value

Pme ~ Tuner Pv alue

Transf orm

Limiter = limits the range of possible values between zero and 1 as to reflect the probability data type of the original P value NEW LCA Pvalue = this represents the new P actual value for the existing land cover, which in this case will be grazing on improved pasture

NewP Conv ersion

NEW LCA Pv alue

Limiter = this represents the new actual P value for the alternative land cover, which in this case is tree belts

Figure 4.9. The STELLA systems model of LEACS.

NEW LCB Pv alue

Limiter

4.2.2.2.2 Setting an EOC Target Value Bartley et al. (2004) consider that setting EOC targets may be difficult for a number of reasons such as heterogeneity in soil parent material (lithology) and soil condition (e.g. compaction, organic matter), vegetation condition and land use management, which leads to spatially variable hydrological functioning. In addition climatic factors can be highly variable in temporal and spatial domains, with rainfall rates and magnitudes varying considerably over a few hundred metres during intense thunderstorms, or between seasons where ENSO is a dominant climatic phenomena. No EOC target value exists for the study area, although the regional NRM body do suggest they will be developed in the near future (SEQWCG 2004). Hydrological modelling for the Daintree River catchment showed that the contribution of hillslope erosion for individual catchments yielded between 1.03 - 1.22 t/ha/yr-1, which is approximately 65% of the total sediment budget for the combined catchments (Bartley et al.

87


2004). This will not be relevant to local conditions due the difference in climatic regimes (i.e. intense cyclonic systems), soil types and land use. For Southeast Queensland, Prosser et al. (2003b) suggested that hillslope erosion on the steeper hillslopes that are used for grazing led to sediment loads of up to 5.0 t/ha/yr-1. A reference EOC can be generated from these estimates. For a given 10 m (100m2) raster grid cell this equate to approximately 0.015 t/100m2/yr-1 and 0.05 t/100m2/yr-1 respectively. The values for the SEQ region derived by Prosser et al. (2003) are for the upper maximum values, and setting a EOC target would almost certainly be well below this value if sediment and nutrient retention is to be achieved. A value of approximately 2 t/ha/yr-1 is therefore used, which equates to 0.02 t/100m2/yr-1. As the simulation can run at smaller timeframes than yearly estimates (e.g. hourly or daily), the reference EOC target values must be converted to an appropriate input value based around daily values. This results in a value of 0.00005 t/100m2/day-1 or 0.05 kg/cell/day-1. The Pm estimate that is subsequently used in the LEACS Stella model were based on this value.

4.2.3 Phase II: Generating Hydrological Simulation Model Outputs 4.2.3.1

Selection of Water Balance Model As discussed in s4.1.5, most hydrological or water balance models involve a certain

data structure, usually either ‘lumped’ or ‘distributed’. In the former case, large areas of a catchment where climate, land cover, soil, and terrain attributes may be somewhat spatially homogenous are classified as a single ‘unit’. The short-comings of these lumped models are in they way they route the flow of water directly to a stream node and then downstream through linkages of the stream network. At scales relative to farm management, and therefore land use change, distributed models appear more able to account for hillslope scale heterogeneity and variations in hydrological functioning. In addition, it is not uncommon that many stream

88


networks have no stream gauging station data available, so models must be physically based and rely on a minimal parameterisation to functional effectively. The physically based distributed parameter hydrological simulation model developed by the Danish Hydraulic Institute - MIKE SHE (DHI 2005), was selected as the process model due to excellent documentation and technical support available. The program is applicable to spatial scales ranging from a single soil profile (infiltration studies) to large regions that include several river catchments, and from minutes to decades in temporal scale. The MIKE SHE program includes numerical models for interception, evapotranspiration, overland flow, unsaturated and saturated flow, and solute transport for the entire terrestrial phase of the hydrological cycle (DHI 2005). In all models the flow calculation method was handled by the built in ‘two-layer’ function. In this version of MIKE SHE, however, no sediment erosion model was available so the focus was on accumulated flow, flow depth and flow velocity. While this placed a severe restriction on estimating the performance of LEACS Phase I (STELLA model), the modelling approach is not required to be an actual accurate ‘blueprint’ fro catchment design, rather, it is to show ‘proof of concept’ only.

4.2.3.2

Basis of MIKE SHE Algorithms All hydrological equations were based on the standard MIKE SHE numeric engines,

with the exception of water velocity which was calculated manually as a post-processing operation. Full descriptions of the differential equations used can be found in the MIKE SHE technical manual (DHI 2005). The model uses the two-dimensional diffusion wave approximation, which is a less computationally intensive form of the Saint Venant equations to describe the conservation of mass and momentum of shallow overland flow and vertical infiltration across a pervious plane (Tayfur et al. 1993; Deng et al. 2005; DHI 2005). The full Richards equation is not used, as this requires a functional relationship for both the moistureretention curve and the effective conductivity to be calculated.

89


Based on a formulation presented in Yan and Smith (1994), the ‘Two Layer UZ/ET method’ is used to model infiltration and evapotranspiration in the UZ instead of the more complex method developed by Kristensen and Jensen (1975). However, DHI note that the validity of the Two Layer UZ/ET method on very steep hill slopes with contrasting soil properties in the soil profile, can be problematic. Soil properties include variables for infiltration capacity, and soil moisture content at wilting point, field capacity and saturation. Vegetation is described in terms of LAI, rooting depth, and specific properties for root mass distribution (Aroot), partitioning between evaporation and evapotranspiration (C1, C2 and C3), as well as a measure of interception storage (Cint). The relationship between depth of water and water detention prior to equilibrium is given in Equation 1, while the routing of flow between adjacent grid cells is given by Equation 2. Equation 3 was developed as a post-processing operation to calculate flow velocity for a grid cell. For a given grid cell of location (x,y) and elevation (z) receives a net input of rainfall (Ix,y) minus infiltration (Infactual) is received at time to. Rainfall firstly fills the interception storage (Imax), which is dependent on the type and age of vegetation. If the interception storage is exceeded, the excess water is added to the amount of ponded water on the ground surface, doc, which is the height of surface ponding before infiltration (Infactual) is subtracted. Infiltration is limited both by the infiltration rate (Infk) and maximum infiltration volume (Infv). Part of the infiltrating water is evaporated from the upper part of the root zone or transpired by vegetation, while the remainder recharges the groundwater in the saturated zone or becomes available for overland flow. The amount of water received minus infiltration ponds to some given level before discharge (Q) to an adjacent cell occurs. The depth of water (h), slope (α), slope length (L) and surface roughness (M) are used to calculate flows based on Equation 2. All grid cells are calculated in order of descending elevation per iteration. Figure 4.10 illustrates these relationships in a simplified conceptual model of water flow and partitioning, with specific difference highlighted for pasture and tree belts.

90


h 

D L

3   1  3 / 5. D  m   De   

Equation 1

where h

=

depth of flow

D

=

detained surface storage prior to equilibrium

De

=

surface storage at equilibrium

L

=

slope length

D Q  M.  .  L

3   1  3 / 5. D     De   

5/3

m

2

/s



Equation 2

where Q

=

flow volume

D

=

detained surface storage prior to equilibrium

De

=

surface storage at equilibrium

h

=

depth of flow

L

=

slope length

α

=

slope gradient

M

=

Manning’s roughness coefficient

V  (

 h*L

)2  (

 h*L

)2

Equation 3

where V

=

flow velocity for a grid cell

u

=

flow velocity in x direction

v

=

flow velocity in y direction

h

=

depth of flow

L

=

slope length (i.e. cell width)

91


ET L LAI ET

Qn

LAI

Qe M

x,y,z

RD

h M Inf

RD Zwt Qw Qs u

v Δ elevation (m)

10 m 10 m

= pasture

u

= velocity in x direction

L

= slope length (cell)

v

= velocity in y direction

h

= depth of flow

Qs

= flow volume

Inf

= infiltration rate/capacity

ET = evapotranspiration

Zwt

= groundwater depth

RD = rooting depth

= tree-belt

LAI

= leaf area index

= overland flow direction/velocity = flow percolation in UZ M

= surface roughness (Manning’s M)

Figure 4.10 – A simplified conceptual model of water flow and partitioning across a set of nine grid cells, with the centre cell being processed within the typical three by three window. The Two Layer UZ/ET method includes the processes of interception, ponding, infiltration, evapotranspiration and ground water recharge. The aim is to simulate the ecohydrological differences between pastures and tree belts, to determine the spatial and temporal effects on stormwater runoff volumes, velocities and infiltration.

4.2.3.3

ArcGIS Data Preparation ArcGIS 9.1 is used to collate and prepare raster datasets for export to the water balance

model. Data in shapefile format were obtained for streams, roads, land cover, soil type (i.e. lithology) and contour data from Crows Nest Shire Council. These files were all projected to 92


the GDA 1994 MGA Zone 56 datum to ensure correct alignment. Features were compared to a set of ortho-rectified 1:5000 (~ 0.6 m resolution) aerial photographs obtained from Queensland EPA. Some spatial anomalies were observed between the land cover shapefile and the aerial photographs, which is not explained by any changes due to either land clearing or regrowth of native vegetation between these dates (August 2001 and December 2001 respectively), and more likely to result from classification error. An additional land cover layer was created based on contour planted trees in a small sub-catchment to enable a hypothetical case study to be run within the simulation. A 10 m DEM was created in ArcGIS from 5 m contour data, with sinks eliminated using a post-processing fill operation to provide a hydrological sound surface. Raster grid files (10 m) were created for evapotranspiration (ET) and Manning’s M (an index of surface roughness) based on the land cover shapefile to reflect the differences in physiological functioning between pasture and native vegetation. The raster grid files were transformed to an ASCII format to modify data headers and then imported into MIKE SHE.

4.2.3.4

Simulation Parameters One of the most problematic aspects of running hydrological models for rural

catchments is the paucity of data that is often available from hillslope to catchment scales. This includes the lateral and vertical distributions of soil types and their physical and chemical properties, land use mapping, land cover and the different physiological functioning of vegetation types (i.e. plant functional types), rainfall and temperature data, runoff and nutrient load estimates for local streams, as well as disturbance histories such as fire and grazing intensities. As such, parameter estimates used for running the MIKE SHE simulation were based upon a collection of locally available data (e.g. rainfall), estimates derived from the literature (e.g. soil properties) and smaller scale data (e.g. lithology) (Table 4.2). As the LEACS model is used for comparative purposes these shortcomings were tolerated.

93


Table 4.2. Parameter estimates (initial) used for the Maronghi Creek simulation by MIKE SHE. Parameter

Classes total pluviograph rainfall

Temporal

Parameter

Classes

Temporal

Spatial

PET

-

constant

uniform 4.12

Classes

Temporal

Spatial

soil type

Orthic Tenosol

constant

Parameter

Classes pasture

Parameter

land cover

time-series

Spatial

Resolution 6, 30, & 60 uniform mins

Range 01/03/03 03/02/06

Ave PETa mm/day

SWCSf,h SWFCf,h (%) (%)

SWWPf,h Infiltrationh (%) rate (mm/hr)

uniform ~ 600

0.16

0.08

Temporal

Spatial

LAIb,g

constant

variable 0.15 -

0.5 - 1

native constant vegetation

variable 0.15 - 0.6

1-2

2.6 mm/day

20 - 30

tree-belts

variable 0.15 - 0.45

2

2.6 mm/day

15 - 20

constant

Depthe (mm)

Depth (m)

0.14

40

Manning’s Md,I ETb,c (mm/day) (m(1/3)/s )2 1.6 30 - 45 mm/day

a) BOM (1996); b) Slavich et al. (1998); c) Best (2003); d) DHI (2005); e) Harris (1999); f) Graeme Cox pers comm. (DHI – Gold Coast); g) Eamus (2006); h) McKenzie (2004); i) Loch (1999)

A rainfall file for the simulation was generated from a full pluviograph record of the Cressbrook Dam weather station (BOM 40808) for a two month period covering January and February 1999 (Figure 4.11). This period was chosen due to it being a more typical ‘wet’ season, whereas exceptionally dry conditions have been experienced in Southeast Queensland in the last six years. During this period rain intensity rates approached 75 mm/hr-1 over a six minute interval, although over one hour this is reduced to 30.5 mm/hr-1 maximum. Intensities over 100 mm/hr-1 are common for this region, but usually last less than an hour. Two major storm events during this period are referred to as event A and event B. Temporal resolution of the pluviograph data was decreased from every six minutes to hourly, three hourly, six hourly and daily timeframes for comparison and to save processing time and reduce the size of data sets (e.g. < 5GB). It has been suggested that a six minute time-step be used to simulate hydrological fluxes on hillslopes (Ticehurst et al. 2005). Potential pan evaporation (PET) was derived from an annual average at Cressbrook Dam and converted to a daily rate of 4.12 mm.

94

Ryan, J.G. (2007) PhD Thesis - Chapter 4, LEACS [mm/day]

32 700 650 600

event A 550

event B 500

rain rate (mm/hr)

450 400

15

350 300 250 200 150 100 50

0

0

Date

00:00 1999-01-05

00:00 01-10

00:00 01-15

00:00 01-20

00:00 01-25

00:00 01-30

00:00 02-04

00:00 02-09

00:00 02-14

00:00 02-19

00:00 02-24

Figure 4.11. Rain rate (mm/hr) for Cressbrook Dam Jan-Feb 1999 derived from 6 min pluviograph records.

A granitic sandy loam soil derived from granodiorite was mapped as spatially homogeneous for the study area. Soil water saturation point (SWCS), field capacity (SWFC) and wilting point (SWWP) estimates reflect a Orthic Tenosol, assuming a shallow hard-pan of clay or rock between -0.15 to -0.6 m depth. The LAI, rooting depth and evapotranspiration exhibited the physiological differences between grasses and trees, although in the case of native vegetation there would be some mix of the two, and a higher LAI for young trees. Values for native vegetation were given as a constant to reflect mature trees LAI, rooting depth and evapotranspiration rates respectively. An account of differences in surface roughness between pasture, grazed native forests and tree belts were added through modified Manning’s M values. Manning’s M is the inverse of the more conventional Manning's n, where the value of n is typically in the range 0.01 (smooth channel) to 0.10 (thickly vegetated channel) while the corresponding values for M are from 100 to 10 (DHI 2005).

95


After each simulation was run the results were reviewed for their performance in terms of relative magnitudes for water flows and temporal duration of peak flows, as well as the pattern of wetting and drying across the steeper terrain. This process was repeated numerous (30-50) times until the peak and duration of overland flow responded comparatively to rainfall rates, while parameter estimates were still reflective of those estimates found in the literature. Once the model was deemed to be reasonably representative of real-world events (based on photographic evidence), the model was run at time-steps from 30 minutes, 1 hour, 3 hours, six hours and 24 hours. All rainfall time-series were derived from the six minute pluviograph record. The initial simulations run at daily timeframes over longer temporal periods (not discussed) were very poor, while the hourly rainfall interval provided a good compromise between hydrological performance, simulation run-time and data storage sizes (e.g. some runs > 14 GB). The results were based upon an hourly time-step with the parameters found in Table 4.2 and the rows labelled Test 3 and Test 4 in Appendix B.

4.3

Results

4.3.1 Phase I: LEACS as a Stella Model 4.3.1.1

General Response of P Values to Performance Measure Error (Pme) The response of LEACS to an array of values associated with the performance measure

(Pm) is provided in Figure 4.12. The recommended Pm (RecPm) and performance measure error (Pme) are also shown to highlight the relationship between these three functions in LEACS, and the affects they have on the new P value estimates. The three LCA lines reflect a updating of the P value after each simulation was run once, so that the new simulation used a P value as calculated from the mean of all returned New LCA values in the prior run.

96


4.3.1.2

Sensitivity of P Value to Changes in Pm Values Another two runs of the LEACS Stella model was completed to assess the response of

the model to changing the magnitude of the incoming Pm values (Figure 4.13). These new set of values (array) can be found in the tables for Run 4 and Run 5 in Appendix A. The same procedure applies as before with the new simulation using the mean of all returned New LCA values in the prior simulation run to set the new P value.

4.3.1.3

Sensitivity of P Value to Changes in the Tuner As the ‘tuner’ is a function which adjusts the magnitude of change on the P values

according to the incoming Pme, these results show how the magnitude of P value change can be increased or in this case decreased (Figure 4.14)

4.3.1.4

Sensitivity of P Value to a Change in RecPm If the recommended EOC is changed, as perhaps might be the case if LEACS Stella is

used in other catchments, the model is tested to see the response to a lower EOC target of 0.02 (Figure 4.15). As beforehand, the results from the first simulation run were used as inputs for the second run as to incorporate the effects of a changing P value.

97


0.701

0.08

0.07

0.601

0.06 0.501 0.05

0.04

Pm

P value

0.401

0.301 0.03 0.201 0.02

0.101

0.01

0.001

Run 1

0 3

5

7

9

11

13

New LCA - Run 1

15

17

19

21

23

New LCA - Run 2

25 27

29

31

33

35

New LCA - Run 3

37

39

Pm

41

43

45

Pme

47

49

RecPm

Figure 4.12. The response of LEACS to an array of performance measure values.

0.701

0.1 0.09

0.601 0.08 0.501

0.07 0.06 0.05

0.301

Pm

P value

0.401

0.04 0.03

0.201

0.02 0.101 0.01 0.001

Run 1

0 3

5

7

9

11

13

New LCA - Run 4

15

17

19

21

23

25

New LCA - Run 5

27

29

31 Pm

33

35

37 Pme

39

41

43

45

47

49

RecPm

Figure 4.13. Response to changes to Pm values.

98


0.901

0.1

0.801

0.09 0.08

0.701

0.07 0.601 0.06 0.05

Pm

P value

0.501

0.401 0.04 0.301 0.03 0.201

0.02

0.101

0.01

0.001

Run 1

0 3

5

7

9

11

13

15

17

19

21

23

25

New LCA - Run 6

27

29

31

Pm

33

35

37

Pme

39

41

43

45

47

49

RecPm

Figure 4.14. Sensitivity of response to changes in the Tuner value.

0.701

0.12

0.601

0.1

0.501 0.08 0.401

Pm

P value

0.06 0.301 0.04 0.201

0.02

0.101

0.001

Run 1

0 3

5

7

9

11

13

New LCA - Run 7

15

17

19

21

23

25

New LCA - Run 8

27

29

31 Pm

33

35

37 Pme

39

41

43

45

47

49

RecPm

Figure 4.15. Changes in the LEACS model behaviour where the EOC target value is altered.

99


4.3.2 Phase II – Tree Belt Simulations 4.3.2.1

Overview These results show two comparisons: Test 3 - a simulation of the current land use

configuration for a small sub-catchment of the upper Maronghi Creek catchment; and Test 4 a simulation using the same parameters but with the addition of contour aligned tree belts land use across some of the hillslopes. Note that this test did not include any adjustment to the DEM. There were two storm events that are evident, the first is a thunderstorm with a rain rate of 30 mm/hr-1 which occurred on the 12th January 1999 between 2 pm and 3 pm (event ‘A’ in Figure 4.11), while the second (event ‘B’ in Figure 4.11) was recorded between 5 pm on the 7th and 4 pm on the 9th of February 1999 with a maximum rate during this time of 20 mm/hr-1 and a total rainfall of approximately 153 mm collected at Cressbrook Dam.

4.3.2.2

Water Balance The water balance of the sub-catchment is provided as snap-shot of the catchment run-

off, recharge (unsaturated zone - UZ) and evapotranspiration rates through time. Figure 4.16 shows the catchment water balance for pastures while Figure 4.17 shows the catchment water balance for the tree belts scenario.

4.3.2.3

Depth of Overland Flow For Test 3 and Test 4 a time-series of depth to overland flow is provided for every 30

minutes after 12:15 pm on the day of thunderstorm event ‘B’ for land covers comprised of pastures (Figure 4.18) and tree belts (Figure 4.19).

100


40

ET Total discharge

35

Peak run-off Infiltration (UZ)

30

mm

25

20

15

10

5

0

date

00:00 1999-01-10

00:00 01-20

00:00 01-30

00:00 02-09

00:00 02-19

Figure 4.16. The sub-catchment water balance for the current pasture based simulation run.

40

ET Total discharge

35

Peak run-off Infiltration (UZ)

30

mm

25

20

15

10

5

0

date

00:00 1999-01-10

00:00 01-20

00:00 01-30

00:00 02-09

00:00 02-19

Figure 4.17. The sub-catchment water balance for the contour based tree belts simulation run.

101

Ryan, J.G. (2007) PhD Thesis - Chapter 4, LEACS a

b

c

d

e

f

g

h

i

j

Figure 4.18. Time-series of water depth for the simulation run based on current pastures.

102

Ryan, J.G. (2007) PhD Thesis - Chapter 4, LEACS a

b

c

d

e

f

g

h

i

j

Figure 4.19. Time-series of water depth for the simulation run based on contour tree belts.

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4.3.2.4

Water Velocity Maximum velocity was derived from the time-series for land uses based on pasture

(Figure 4.20) and tree belts (Figure 4.21).

ii i

Figure 4.20. Simulated velocity for grazed pastures in the Maronghi Ck sub-catchment.

ii i

Figure 4.21. Maronghi Ck sub-catchment velocity for land cover comprised of tree belts. Note how the tree belts reduce the velocity of water flows across hillslopes (i) compared to pasture (Fig. 4.20). Water is intercepted and redistributed along tree belts. A result of this is increased flow in gullies where tree belts terminate (ii), which can be addressed with either a dam and/or protected with riparian vegetation.

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4.3.2.5

Infiltration to the Unsaturated Zone (UZ) Figure 4.22a shows an example of how the recharge of the unsaturated zone (UZ) of

the soil profile changes across hillslopes when pastures are restored with tree belts (Figure 4.22b).

(a) (a)

Infiltration to the unsaturated zone – negative (mm/day)

(b) (b)

Figure 4.22. The difference between soil profile recharge rates on: (a) currently cleared pasture, and (b) tree belts. Note that the moisture tends to infiltrate more evenly across the hillslopes treated with tree belts.

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4.3.2.6

Evapotranspiration Rates The evapotranspiration rates reflect basic physiological differences between pastures

(Figure 4.23a) and tree belts (Figure 4.23b).

(a)

Evapotranspiration (mm/day)

(b)

Figure 4.23. The difference between ET rates reflect the values set for LAI and ET for a) current pasture systems; and b) tree belts.

106


4.4

Discussion

4.4.1 Phase I: LEACS STELLA Model 4.4.1.1

General Performance In Phase I, the method was applied a set of equations and logical statements. The

general outcome for LEACS STELLA is that the model functions in a logical manner. This is based on the response of the model to hypothetical data arrays for each run (Appendix A) and the land use preference as derived from farmer expert knowledge in Chapter 3 (i.e. P values). While these are purely hypothetical data, the response of the model to adjustments in the Tuner and RecPm to reflect either a more/less strict EOC or tolerance of deviations from that target in terms of farmer values, suggests the model could be adapted to suit a wide range of possible alternatives for an individual catchment. The model response is gradual where the Tuner forces restricted Pme adjustments which reflect greater/lower tolerance of a farmer to a certain level of ecohydrological dysfunction following intense thunderstorm events for a given land cover. In other words, variable change in the P value for a given land cover allows different resilience to change to alternate covers to be accommodated for modelling individual farmers land use preferences. For Runs 1 – 3 the performance of the hypothetical land cover was temporarily poor in relation to the EOC target value stipulated, the response was a gradual decrease in the P value (Figure 4.12). Alternatively, where the performance of the land use was always good (i.e. < EOC target) the model would increase the P value which in effect reinforces the preference for that land cover. Using a different (larger) set of Pm values (Runs 4 and 5) to assess the sensitivity of the model to magnitude of inputs, resulted in a greater drop in the P value in response (Figure 4.13). A further increase in the P value, that is to reinforce the preference for that land use over time until a maximum of ‘1’ was obtained, could not occur due to the ‘circular logic’ such model presents in Stella. Instead, the results for the P value from the prior simulation run were averaged and presented as the new P value for the next iteration. 107


Another test which was applied was for adjusting the value in the ‘Tuner’ (Run 6). The Tuner had a marked affect on the resulting Pme and thereafter adjustment of the P values (Figure 4.14). It appears that the model is less responsive to Pme when the value in the Tuner is set to a larger value then the Pme produces. This was circumvented in following tests (as it was needed) by applying a transform function which reduces the effect of the Tuner as the value of RecPm increases. The final tests (Runs 7 and 8) were to assess the effects of changing the RecPm on the output value of Pme and the adjustment in P values. In Figure 4.15 the model behaviour changes markedly to the same Pme but where the RecPm is set lower (Run 7), thus increasing the sensitivity of the model to large departures from the EOC target and decreasing the P value for the next simulation run (Run 8). In a real-world scenario, this would be the same as a farmer changing the land cover at specific locations across a hillslope to an alternative cover (e.g. tree belts) based on observable (or measurable) losses of water, sediment or nutrients.

4.4.1.2

Potential for Linking LEACS Phase I and II The coupling of LEACS Phase I with Phase II was not undertaken due to the inability

to calculate sediment erosion from hillslopes within MIKE SHE, and more specifically, due to the lack of data compatibility between MIKE SHE and STELLA. A method to calculate flow velocity was obtained from DHI (pers comm. Graeme Cox – DHI, Gold Coast), and used as a surrogate to reflect erosive potential. This method was a test for Phase II and not as an input into Phase I, although the potential certainly exists. At this stage of development, LEACS STELLA simply responds to hypothetical data arrays based on the structure of the equations used. This is because LEACS Phase I is a constrained optimisation process that takes an input value for a given cell, assesses the value against some pre-determined ‘target value’, and subsequently recalculates the change in the weighting applied to a given class for that cell (i.e. a type of land cover in this case) based on

108


the difference between ‘target’ and ‘actual’ values for that cell. The actual changing of landcover through a link back to the hydrological model was not completed, but is worthy of investigation as the need for such a system is considerable at present. As so far as the aim was to show ‘proof of concept’ for linking the data between two simulation models, the LEACS Stella model partially fulfils this objective.

4.4.1.3

Suggested Improvements to Stella Model The coupling of LEACS STELLA to a distributed hydrological simulation model

based on a raster grid format (such as MIKE SHE), presents an essential step but significant hurdle at present. The major limitation is one of data compatibility between different software, and a real time dynamic link between the two software environments. To circumvent this, a major step would be to replicate LEACS STELLA as Python or C++ code to link with a GIS or as a stand-alone application. Another improvement would be to use a distributed parameter hydrological model with an inbuilt sediment transport module, as the calculation of flow velocity is not necessarily related to the amount of run-off leaving a hillslope, it cannot be assumed to be a direct surrogate for sediment and nutrient transport. In any case, any outputs from MIKE SHE (or an equivalent model) would firstly need to be validated using field trials over at least a five year period to allow for the occurrence of intense thunderstorms and outputs to be measured. This must occur before a dynamic link between LEACES Stella and MIKE SHE would be worthwhile to establish.

109


4.4.2 Phase II: Simulated Affects of Tree Belts on Ecohydrological Functioning of Hillslopes 4.4.2.1

Overall Scope While the data link between LEACS Stella (Phase I) and MIKE SHE (Phase II) was

not achieved at the time of writing, the affect of tree belts showed they induce negative feedback to reduce the volume and velocity of stormwater runoff, increase infiltration and sediment retention, and further recycle moisture back to the lower atmosphere. This section discusses the results from the simulations of a sub-catchment of Maronghi Creek (Figures 4.16 – 4.23) to test the ecohydrological functioning of tree belts on cleared hillslopes currently comprised of pasture grasses (see Test 3 and Test 4 in Appendix B).

4.4.2.2

Implications from Phase II In Test 3 and Test 4 the aim was to gain an understanding of the potential for resting

landscape ecohydrological functioning through the use of both patches of vegetation fencedoff in topographic ‘saddles’ as well as contour aligned tree belts. In these simulation runs, the effects of changing LAI and ET for tree belts as a time-series was deemed irrelevant due to the short time-frames used. In any case, the specific LAI will vary for a particular species as well as respond non-linearly to stress through time (Wilby and Schimel 1999). The LAI and ET rates were therefore set similarly to native vegetation. The Manning’s M was changed to a slightly rougher surface under the trees to reflect fencing-off from stock access. This was done as the purpose of these strips was to act as filter strips and reduce both peak flow water magnitude and velocity as well remove sediment and nutrients from the flows (Lee et al. 2000; Eamus et al. 2005).

110


4.4.2.3

Water Balance The water balance models for Test 3 and Test 4 show a very similar result (Figures

4.16 and 4.17). This is to be expected of course, as the parameter estimates were identical with the only difference for tree belts being lower values of Manning’s M (lower) and higher values of ET and LAI. On closer inspection, however, there are subtle differences that were worth noting. Firstly, the peak run-off figures were different for the first (A) and second (B) thunderstorm events for each test. The responses to event ‘A’ was 17.5 mm and 18.5 mm for pasture and tree belts respectively, while these same land uses recorded 15 mm and 13.5 mm respectively for event ‘B’. While this does not present a clear picture as to what mechanisms were responsible for such deviations between events, it is likely to be the nature (i.e. intensity and duration) of a particular thunderstorm event that affects peak discharge rates and timing. These subtleties over a longer term lead to figures for total discharge for pasture of 19 mm to that of tree belts with 17.5 mm. Recharge to the UZ for pasture and tree belts was approximately 2 mm for both, while the evapotranspiration figure showed slightly enhanced evapotranspiration of 35 mm versus 36 mm respectively. The time-series provided in Figures 4.18 and 4.19 show the change (Δ) of stormwater depth following thunderstorm event ‘B’ at 30 min resolution from 12:15 pm through until 4:45 pm on the 8th February 1999. At this scale it is very difficult to delineate dissimilarities in this series except for some minor differences in depth over a few grid cells (i.e. 100 – 400 m2). The depth of runoff was in the order of 0.05 to 0.1 m less in the tree belt simulation runs. While these were very small actual differences, a narrow gully of approximately 30 m width would see a reduction in water of 45 m3-90 m3/15 min-1 at peak flows. The simulated performance of tree belts to actively intercept overland flow during heavy rainfall are consistent with Ellis et al. (2005b; 2006) who used field-based rainfall simulator trails for tree belts on hillslope with a Red Chromosol (duplex) soil near Boroowa, N.S.W. Under three simulated rainfall intensities (i.e. 45 mm hr-1 over 13 minutes, 45 mm hr-1

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over 30 min, and 75 mm hr-1over 30 min) a 12 m wide mixed tree belt comprised of Acacia spp. and Callistemon spp. was sufficient to reduce the amount of overland flow being generated down a slope gradient of 12%. The runoff generated above the tree belt in the first experiment was effectively trapped by the tree belt, while approximately 50% of the second event was captured, representing an approximate gain of water of 36 – 37% above incident rainfall respectively. The research showed that water breached the tree belt during the 75 mm hr-1/30 min event. As a comparative measure, Scanlan (1996) established that runoff in a native woodland on granodiorite near Charters Towers north-eastern Queensland may be greater than 20% when rainfall intensity exceeds 60 mm/h−1. There are examples in the literature where more run-off is generated from native woodlands than for cleared landscapes comprised of pastures (McIvor et al. 1995), as well as examples which state an increase in run-off following the clearing of native vegetation (Pressland 1976). The spatial heterogeneity of the soil affects processes such as infiltration rate and filed water capacity, such as impermeable layers that have intermittent depths across hillslopes (Becker 2004). This variation in soil moisture has a positive feedback on the growing conditions for vegetation (Wood 1999), as aggregation of vegetation (stems) allows a greater interception of moisture, sediment and nutrient, that in turn, enhances the growth of the vegetation. The process of ‘hydraulic lift’ of water by tree roots, for example, creates preferential flow paths and assist in the redistribution of soil moisture throughout the soil profile (Wainwright et al. 1999; Burgess et al. 2001). The exact relationships between differing vegetation types will be exacerbated or attenuated by the configuration and sizes of patches (i.e. clumps) or strips across hillslopes (Loch 2000; Ludwig et al. 2007), and the resultant interactions between the vegetation, soil, hillslope hydrological functioning, run-off and erosion will likely be complex and non-linear (Wainwright et al. 1999). In most cases, it has been found that trees alone cannot provide the ground-surface structure needed for adequate filtering mechanisms to be achieved, moreover, there is a need

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for an adequate ground-cover of tussock type grasses, sedges or reeds with high density and biomass (Karssies and Prosser 1999; Carey et al. 2000; Hook 2003). The abrupt change in roughness caused when longer stemmed and thicker often multiple stemmed grasses are encountered often creates a ‘backwater’ which causes sediment to flocculate (Hussein et al. 2007). These backwaters create ‘micro-terraces’ of organic matter that can trap up to 95% of eroded sediment from above the belts (Leguedois et al. 2005), which suggests that a substantial additional store of soil nutrients and moisture is available to plants within the belts. This provides a micro-climate where both trees and grasses also have the added benefit of providing a means for removing nutrients from both overland flow and sub-surface lateral flow. Vought et al. (1995), for example suggested that a buffer strip 10 m wide can reduce phosphorus loads bound to sediment by up to 95%. The inclusion of contour bunds with the tree belts can add further protection to hillslopes that are subject to high run-off levels following intense thunderstorms (NRW 2004b). It has been shown, for example, that contour bunds may reduce sediment loads reaching the bottom of slopes by more than six times the level of sediment eroded from unprotected hillslopes (Freebairn and Wockner 1986b). For this type of system to work effectively, however, there needs to be some consideration given to removing stock from beneath these strips to allow grass cover to be maintained, including increasing organic matter form leaf and bark litter from trees. Where heavy grazing removes the surface cover there may be a reduction of their effectiveness erosion may result (NRW 2004b).

4.4.2.4

Water Velocity and Erosivity The results of the flow velocity calculations confirm that overland flow rates and depth

are modified where tree belts are added to hillslopes currently comprised of native pastures (Figures 4.20 and 4.21 respectively). The tree belts appear to intercept stormwater runoff, increase infiltration (as in Figure 4.22b) and evapotranspiration, by redirecting intercepted

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water to drier sections of individual hillslopes. The simulated tree belts in Figure 4.21 exhibit markedly lower flow velocities (point i) compared to pastures (Figure 4.20), but then concentrate this intercepted flows where the tree belts terminate into second order gullies (point ii). This is possibly caused by the fact that the tree belts are aligned offset to the contour (~ -3%), while the MIKE SHE model itself is stated as being capable of responding to contrasting surface roughness (Manning’s M) along the boundaries between grid cells. A model by van Oost et al. (2000) tested the effects of landscape structure with a distributed hydrological model to estimate changes in erosion and sedimentation with modified configurations of land cover. The authors suggested that due to the interaction of changes in land use and the position of field boundaries with respect to topography, erosion rates either decreased or increased in certain locations by as much as 28%. The findings presented here suggest likewise, that adding tree belts to otherwise contiguous areas of pasture on steep hillslopes affects the amount and spatial patterning of water depth and velocities. This may reflect differences in surface roughness, organic biomass and soil macropores which together help stabilise slopes and prevent sheet erosion by slowing water velocity and increasing infiltration (Post et al. 2006). It is necessary for tree belts to incorporate a ground cover of grasses or sedges to be effective at controlling both water flow and velocity as well as removing sediments and nutrients from these flows (Rachman et al. 2004; Wang et al. 2004). This systems also improves the physical properties of soil through enhanced infiltration, nutrient cycling and inputs of soil organic matter, that in turn, help maintain soil aggregates and soil structure (Bregman 1993). Another benefit of tree belts over contour bunds is that they are semipermeable, and so can act as a ‘safety valve’ during intense thunderstorms where contour bunds would otherwise be overtopped or breached (Young 1989). Where these strips or contour bunds terminate, however, is certainly an area where riparian vegetation must also be

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restored to act as a buffer. Alternatively, the intercepted water could be subsequently redistributed to farm dams.

4.4.2.5

Changes in Infiltration The results for infiltration to the UZ of the soil profile showed that there were

observable differences in both temporal and spatial domains (Figures 4.22a and 4.22b). The most noticeable effect of the change from pasture to tree belts is that the infiltration into the UZ is spread more evenly over hillslopes in the later. For a given 15 minute time period, infiltration increased from 0.08 to 0.16 mm to approximately 0.16 to 0.32 mm (Figure 4.22a and Figure 4.22b respectively). These values seem a magnitude lower when compared to the results of Ellis et al. (2006) for surface infiltration of 8.8 mm and 14 mm during a 13 minute simulated rainfall in pasture and tree belt respectively, and in some circumstances up to 46% higher in the tree belt than the pasture. The ratio in infiltration from the data presented here is 100% greater for tree belts over that of pastures. It is generally acknowledged that land use (i.e. changes in land cover) modifies many of the factors that control water infiltration, soil erosion and sedimentation patterns for a given soil (van Oost et al. 2000; Rachman et al. 2004). Where native vegetation is removed, for example, there may be less buffering of rainfall, a slow decline in soil structure and fertility, less cycling of soil moisture, and more extreme runoff events (Elliot and Ward 1995; Martinez-Mena et al. 2000; Magdoff and Weil 2004; Williams and Saunders 2005). This is partly due to the fact that many native trees have structures which efficiently direct rainfall into the soil due to their branching habits which causes water to infiltrate to considerable depths at the base of the tree (Hatton and Nulsen 1999). Rooting depth has a marked affect on many hydrological functions across landscapes, including lateral flow both above and below the soil surface (Jackson 1999). It is excepted, however, that the actual partitioning and rates of infiltration and run-off between the soil

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surface and soil at depth will vary both spatially and temporally along with the topographic factors that determine recharge and discharge areas (Ludwig and Tongway 2002; Thorburn et al. 2002; Eamus et al. 2006). While infiltration is naturally highly variable, it is often reduced where inappropriate management practices result in surface sealing or soil compaction and increased sheet erosion from run-off (Tunstall and Webb 1981; Freebairn and Wockner 1986a). The addition of tree belts increases soil organic matter, litter and biological activity all of which improve soil structure and increase infiltration, and when in combination with grass swales, improves water capture and storage (Abel et al. 1997).

4.4.2.6

Evapotranspiration The final analysis was on the effects of evapotranspiration across the hillslopes

between current pastures and tree belts (Figures 4.23a and 4.23b). Evapotranspiration for the simulated tree belts was set to 2.60 mm/day-1 or approximately 949 mm/yr-1, which compares well with the estimates of average annual evapotranspiration calculated by Gordon et al. (2003) as approximately 885 mm (2.42 mm/day-1) for woodlands and 953 mm (2.61 mm/day1

) for open eucalypt forest. A larger percentage of water in the UZ was drawn within the tree

belts as a result of higher LAI, particularly water drawn from the groundwater source, although a field estimate was not obtained directly. More direct measurements include the study by Knight (2002) on the effect of tree belts on infiltration, evapotranspiration and water recharge in the 300–450 mm rainfall zone of the southern Murray–Darling Basin. In less than four years the soil directly below and near the belts had living roots down to 16 m below the surface which used approximately 600 mm of water from deep in the profile, that in turn, added up to four metres of extra soil-water storage capacity. White et al. (2002) measured soil water content and water use in a contour-planted belt of trees comprised of various Eucalyptus species at ‘Ucarro’ near Katanning, Western Australia. Over a 12-month period the measured uptake of groundwater exhibited similar rates (~ 150 mm) to measurements

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made by Knight et al. (2002), suggesting that trees were tapping into a substantial store of water that annuals could not directly access. The effects of setting different values for LAI and evapotranspiration were particularly evident in these two maps, with resulting rates being slightly less the actual rates provided (i.e. 1.4 for pasture and 2.6 mm/day-1 for tree belts and native vegetation). There was some differences noticed however, where inundation in gullies and stream channels prevented either pasture or trees from transpiring at optimum rates. It is anticipated that if a longer simulation period was established (e.g. 3-40 years) there would be ongoing change in the evapotranspiration rate as there is evidence of the evapotranspiration changing as trees age (Cornish and Vertessy 2001). The increased level of evapotranspiration from the soils also counters the added infiltration achieve by the tree root structures. This relationship between precipitation inputs, soil water capacity and evapotranspiration form a balance within naturally vegetated catchments, and are generally more tightly coupled as conditions become more arid (Wainwright et al. 1999). In general, using tree belts will increase catchment evapotranspiration to achieve a catchment water balance more that achieved through compared with pastures or crops (Zhang et al. 1999). The extra water return to the atmosphere via evapotranspiration pathways not only reduces the deep drainage, but may also form a particularly important mechanism for the generation of rainfall (Pal and Eltahir 2001).

4.5

Conclusion To achieve the feedback between land use/cover change and a distributed parameter

hydrological model is not that difficult analytically, but using two simulation models with different data types and languages makes the link complex and time consuming. The LEACS approach is a useful addition to the natural resource and catchment management toolbox. The conceptual model of LEACS was sound and the applied tests in the Stella systems simulation 117


environment suggested there is potential for the model to be further developed a s a standalone tool for NRM. The lack of integration between MIKE SHE outputs and the LEACS Stella model was a major shortcoming of LEACS Phase I. This proposition may easily be overcome where a suitable surrogate modelling platform houses temporal modelling and feedback using physical dynamics. This would entail being physically based and distributed, so to allow the LEACS Stella model to run on a raster grid cell basis. This approach would also see LEACS become viable for multi-agent system (MAS) modelling approaches, with each cell having it’s own model (agent). Considering the actual effects of contour aligned tree belts on surface hydrology, the findings presented here are quiet generalised due to the uncertain nature of the parameter estimates used. Nonetheless, there are clear differences between pastures (current) and the addition of tree belts on run-off volumes, velocities and spatial distribution due to the effects of increased surface roughness, infiltration and enhanced evapotranspiration. Where excessive water and nutrients currently cause problems for catchment water quality and quantity, the interception and redistribution of these resources on hillslopes benefits both production and NRM. The results discussed in this chapter have highlighted the effects of ecohydrological designs based on tree belts, and therefore provide a guide as to where and in what configuration native vegetation can aid both production and broader catchment objectives.

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Ryan, J.G. (2007) PhD Thesis - Chapter 5, Discussion & Conclusion

CHAPTER 5 DISCUSSION AND CONCLUSION

5.1

Chapter Overview This chapter summarises how each objective of the Thesis has been met, including

relevance to ecological theory and other research, the main implications for NRM, and potential future research questions. The specific objectives and related Chapters that are discussed are as follows: i)

address the knowledge gap in landscape ecological theory by developing a concpetual framework for landscapes described as complex adaptive systems (i.e. Chapter 2 CAL framework), with examples applied to dyfunctional ecohydrological systems.

ii)

determine if a graphics based survey is sufficient to capture information on the complex dynamics involved with land use changes by farmers through time when anaylsed with Bayesian belief networks (i.e. Chapter 3 - GLAMS approach);

iii)

demonstrate ‘proof of concept’ for the potential of a data-link to be used between a land use configuration model and a distributed hydrological process model to automate the process of redesigning ecohydrologically dysfunctional landscapes, and determine the ecohydrological effects of tree belts for this purpose (Chapter 4 LEACS).

5.2

Objective 1 – Complex Adaptive Systems Framework for Ecohydrological Systems

5.2.1 Achievement and Major Outcomes One of the major outputs of this research was the development of the conceptual framework of Complex Adaptive Landscapes (CAL), which combines tenets from complex

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adaptive systems (CAS) with principles of landscape structure, function and change from landscape ecology. The CAL framework was developed specifically for understanding the nature of complexity within interactions between human land use and ecohydrological systems. CAL allowed a conceptual link between the structure, functioning and change of landscape systems to six core tenets derived from complex adaptive systems principles. The framework is suitable for ecological research or catchment and natural resource management where complexity must be translated into manageable outcomes. In this research, CAL was used as a guide in establishing the LEACS modelling approach which formed Chapter 4. The need for a framework to deal with ecological complexity is gaining recognition, as indicated by the August 2005 discussions of the complex systems approach within the ‘Joint Meeting of the International Association for Ecology (INTECOL) and the Ecological Society of America (ESA)’ in Montreal, Canada. A summary paper by Proulx (2007) highlighted some important principles of the complex systems approach that could be applied to ecological research: interaction topology, ecological integration, biological object, spatiotemporal patterns, ecosystem management, and non-equilibrium thermodynamics. The importance of incorporating humans as an intrinsic part of the ecosystem was also highlighted, as were the interaction topologies [system interconnections] among biological objects. Rammel et al. (2007) adopted principles from complex adaptive systems to better understand the complexity of interactions between socio-economic and ecological systems using a heuristic based on a ‘co-evolutionary perspective’. Their framework enables the mapping of interactions between the resource base, social institutions, and the behaviour of individuals. The overlap of principles between the complex systems approach, coevolutionary perspective and CAL indicates common translation of complexity to more concise functions and properties of a particular landscape or socio-ecological system. Where general agreement can be applied directly to real world management outcomes, restoration

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designs, or conservation of natural resources and biodiversity, this represents an important contribution to progressing the understanding and further defining ecological complexity.

5.2.2 Implications for Natural Resource and Catchment Management The main conclusions that stem from the CAL framework have direct implications for sustainable NRM. As the six core tenets are already a summation of a large background of theory and research findings from many fields of ecology and complex systems science that they can be used to list some direct implications for research studies and NRM.

5.2.2.1

A Continuum of Scales This tenet states that landscapes are comprised of multiple components in levels that

are distributed across a continuum of scales in space and time. In the context of NRM, a continuum of scales relates suggests that when a sampling or monitoring strategy is developed in order to gain data on some landscape property or dynamic, it is likely that other spatial and temporal scales are excluded. The ‘perception of scale’ or ‘scale reference’ of the observer defines whether something is an object or a function at some range of scales, which if ignored, can profoundly affect the management or restoration of the landscape. This also has considerable bearing in research when determining the components, processes or the variables selected to characterise an aggregate system (Carpenter et al. 2001; Beisner et al. 2003). It has obvious ramifications for defining the functional limits or key objects that define a particular ecological system, and therefore the assumed sustainability of a particular NRM issue. If the external influences and effects from other ‘non-sampled’ systems at other spatial and temporal scales is missed, so to is potentially vital information on how the complex interactions between landscape systems shape ecological processes. For example, on-ground restoration focuses on scales more directly related to a single bend in the creek or a hillslope than the scales related to landscape structure or ecohydrological functioning at atmospheric

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and geomorphic scales. If management or restoration works do not account for longer-term changes in climate, hydrological processes, and the effects of heterogeneity, then the fuller dynamics that shape the system is underestimated. It is important for NRM to create the conditions necessary (e.g. demonstrations, guidelines, policies) for enhancing ecohydrological systems functioning at hillslope scales, which also fit into a larger catchment plan which identities climatic forcings that span regions and decades. An example includes where riparian restoration is exposed to greater flood depths and velocities due to source areas within the catchment being left untreated (e.g. bare ground, heavily grazed pasture). To address this would require a shift in perspective that creates planning and restoration frameworks that span individual creek bends to entire catchments, and accounts for temporal dynamics that have variable frequency between low flows to 1:100 year flood events.

5.2.2.2

Open Gradients Energy and resource gradients in complex adaptive systems are thermodynamically

open (Ilachinski 2001). These ‘teleconnections’ relate to the fact that local systems are subject to reciprocal forcings from external systems while also influencing other systems at potentially different times and locations (Hoerling et al. 1997). Teleconnections found between non-local linkages of the Earth’s surface and the atmosphere, for example, often produce remote effects based on events at other locations or times, such as the effects on precipitation at mid and high latitudes as a result of deforestation in the tropics (Gedney and Valdes 2000; Avissar et al. 2006). McAllister et al. (2006) suggests that semi-arid rangelands are also open systems subject to multiple influences from other subsystems. Ecohydrological systems are also open systems with a high degree of non-linearity, but despite this, order is maintained through the particular vegetation functional types that selectively intercept, transform and store water, carbon and solar energy at different rates and times (Odum 1971; Wu and Marceau 2002). The major implication for NRM is that the

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influence upon local systems from external forcings should be accounted for in operational works. In addition, the particular roles of any assemblage of vegetation (i.e. plant functional types) should be managed in terms of their ability to influence non-local systems through hitherto unforeseen teleconnections.

5.2.2.3

Multiple and Diverse Sets of Components CAL highlighted that most complex adaptive systems contain a high number of

components of many different forms that are organised into particular scale levels in space and time. A diversity of component types are supported by landscapes [ecosystems] to cope with alternative system dynamics, such as different functional states caused by endogenous or exogenous disturbances causing flux in available resources. As humans are also a component of the landscape and are a generator of disturbance, they are included explicitly in CAL. An example of a disturbance pattern by humans is land cover change, which results in a landscape matrix which either amplifies (positive) or dampens (negative) fluxes in energy and material between the components of the atmospheric, vegetative, edaphic and hydrological systems. The amount and spatial configuration of land cover across a landscape, for example, modifies fluxes which underpin the emergence of particular mesoscale circulations (i.e. regional scale) (Pielke et al. 2007). An important implication of this for NRM is that where land clearing takes place over large regions, concurrent changes in air circulation may also result in patterns of reduced precipitation and increased temperatures (McAlpine et al. 2007).

5.2.2.4

Interactions and Non-linear Feedback Mechanisms Multiple interactions between components and resource gradients constitute

‘feedbacks’ in landscapes, most of which are non-linear due to differences in temporal dynamics related to how energy and resources are buffered and partitioned. For example, Rind (1999) suggests that there is always a dynamic tension between order and chaos in the

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climate we experience due non-linear interactions, that in turn, promote the spontaneous emergence of self-organisation within broader atmospheric systems. The strength of the effects of ENSO on rainfall in Australia, is also suggested to be non-linear/asymmetric due to the complexity of teleconnections (Power et al. 2005). Even in atmospheric general circulation models (AGCMs) ensemble results show nonlinear climate responses which closely resemble observed shifts in the equatorial positions of the maximum rain responses and a phase shift in teleconnections of the upper troposphere (Hoerling et al. 1997). These non-linearities in the climatic system can cause impacts that are episodic and abrupt rather than slow and gradual, which in turn, can cause the climatic system to rest within any one of a number of possible meta-stable states (Rial et al. 2004). Although the recognition of the complex adaptive system properties within both climatic and ecohydrological systems must be accounted for in NRM, it is rarely achieved in practice. It assumed that the reasons for this are precisely because of the complexity involved, or perhaps as Walker and Salt (2006) suggest humans are not very good at noticing or responding to things that change slowly. This is the Catch-22 - without a sound conceptual model that can simplify complexity to a level sufficient for understanding, or without an adequate NRM framework that allows us to cope with feedbacks that may take decades to unfold, we are unlikely to apply the adaptations necessary to adjust to non-linear cycles and alternate functional states. That is, we are not sustainable. NRM needs to better understand how feedbacks between native vegetation, hydrological cycles and the atmospheric underpin resource partitioning and cycling and promote the buffering of disturbances. This must include a specific reference to the inherent time-lags between cause and response, such as reduced rainfall decades after land clearing took place (McAlpine et al. 2007) or the extinction threat to biodiversity (Brooks et al. 1999). The CAL framework provides a useful summary of principles from the field of complex adaptive systems which aid in this regard.

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5.2.2.5

Aggregation and Self-Organisation Feedback mechanisms are a catalyst for the formation of aggregates that, in turn, lead

to self-organisation among the components which underpins emergent patterns/behaviours at other levels, locations or times. Spatial aggregation of vegetation at patch scales, for example, is a fundamental aspect of self-organisation in semi-arid landscapes that gives rise to the emergence of landscape processes (Ludwig and Tongway 1995). The effects of shading in a semi-arid area, for example, can reduce evaporation from the soil by as much as 2% volumetric soil water content per day, and this in turn, modifies the microclimate and subsequently type of plants that can establish themselves which further self-reinforces landscape structure (Breshears et al. 1998). The heterogeneity of cover types has also been shown to affect the meso-scale patterns of the fluxes of mass, energy and momentum from the biosphere to the atmosphere (Brown and Arnold 1998; Hayden 1998). Other examples include linear aggregates which buffer and partition flux in resource gradients. Banded vegetation systems at landscapes scales, for example, can create relatively strong atmospheric circulations and regions for deep cumulonimbus convection due to through the juxtaposition of contrasting land cover or soil orders (Garrett 1982; Mahfouf et al. 1987; Avissar and Pielke 1991; Pielke 2001). The main implication for NRM is that aggregation underpins the numerous teleconnections between atmosphere, vegetation, soil, and hydrology. These connections obviously cross large spatial and temporal scales, and patchiness can be viewed at many levels and for many different functions. Over the longer-term, restoring ecohydrological functioning must be based on climatic and hydro-geomorphic cycles over at least decades to centuries to prevent their collapse during prolonged adverse conditions. The CAL framework provides examples of aggregated vegetation patches and banded vegetation systems across many spatial and temporal scales, and how these provide critical feedbacks to atmospheric and hydrological systems that enhance the self-organisation of ecohydrological processes.

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5.2.2.6

Multiple States and Transitions Environmental forcings may cause landscapes to fluctuate between alternate meta-

stable states that reflect different locally stable equilibrium points over some period of time (DeAngelis et al. 1989; O'Neill et al. 1989). The stability, resistance and resilience of multiple meta-stable states in landscapes are somewhat related phenomena. Stability may be defined as the probability of the system returning to its previous state following temporary disturbances, resistance is the probability for the components and the relationships between to ‘persist’ despite changing environmental conditions Holling (1973), and resilience the speed at which a system returns to equilibrium state following a perturbation DeAngelis (1980). Pertaining to the residence time of energy or materials within a landscape, the lower the residence time within the steady state system, the more resilient the system becomes. As O’Neill (2001) points out however, stability cannot be explained by the dynamics occurring only within some human defined boundaries to a landscape (or ecosystem) – they are ‘open’. As flux in energy and material induces different forcings over timescales from decades to millennia, a challenging problem exists in defining what may be a ‘normal’ range within biophysical structures and biogeochemical functions of landscapes (Belaoussoff and Kevan 1998; Campbell 2000). Is it what we have come to expect from 100 years of climatic records? The present state of the climate is affected by antecedent (i.e. historical) conditions, as well as the timing and magnitude of current perturbations to the system (Neubert and Caswell 1997). This means that small changes in landscape structure at one point in space or time may eventually lead to dramatically different future states for both climatic and ecohydrological systems (Rind 1999; Gunderson et al. 2002; Parker et al. 2003). This is particularly important point for NRM to accommodate – as CAL are sensitive to initial conditions, historical trends are not necessarily a good approximation of the future. To overcome this, NRM must firstly seek to understand the range of timeframes involved for various environmental forcings. Secondly, to understand how flux is manifest within a landscape longer-term monitoring

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should be established to obtain data which can parameterise physical models and provide projections of risk for ecosystem resilience. This is a necessary step to make projections about likely future landscape states more robust (Tait 2004).

5.2.3 Summary of Future Research i)

Test the robustness of the CAL model by applying it to other landscape systems at other locailities.

ii)

NRM must account for the potential of multiple stable states, non-lienar transitions between them, and human induced forcing that drive landscapes into ecohydrologically dysfunctional states.

iii)

CAL has important implications for field research and sampling designs:

iv)

Establish longer tem monitoring of climate, soil and hydrological systems functioning with timeframes of between 5 to 50 years.

v)

Monitor and collect detailed measurements for both in-situ and external sites concurrently to test for teleconnections; and

vi)

Test hypotheses for the affect of specific replications of differing vegetation types, structures and configurations from hillslope to catchment scales.

5.3

Objective 2 - GLAMS

5.3.1 Achievement and Major Outcomes Land use and NRM involve a variety of people displaying adaptive behaviour to nonlinear dynamics and changes to alternative landscape states (McAllister et al. 2006). Although spatial heterogeneity will affect ecological and agricultural systems, there has been limited research for estimating the resilience of interactions between land use and ecohydrological systems and how the resilience measures themselves are sensitive to change 127


(Carpenter et al. 2005). As it becomes difficult to achieve widespread adoption where the system is increasing complex or radically different to current farming practice (Pannell 1999), the challenge is to better understand complexity in socio-ecological systems without creating a model that is itself overly complex (McAllister et al. 2006). While the adapting behaviour and subsequent decision making will vary considerably for each farmer (Pannell 1999), the cumulative effect of decision making by all farmers has the potential to shape NRM outcomes for an entire catchment. Integrating NRM into land use management systems is only achievable through recognition of the complex interactions between humans and ecological systems, which in part must take into account how farmers affect the condition of natural resources, but also respond and adapt to changes in resource condition (Lal et al. 2001). While farmer behaviour should be incorporated into catchmentwide planning (Bryan et al. 2006), these plans themselves must also be amenable to implement as practical management solutions at the farm scale (Carberry 2001; Ridley 2004). Many of the existing approaches to capturing information on how farmers manage land use or natural resources are based on questionnaires, such as the survey undertaken by Bryan et al. (2006) to ascertain the importance of NRM to farmers in the Australian Murray-Darling Basin. This research developed a new approach - Graphical Landscape Map Survey (GLAMS), which varies markedly from a standard questionnaire approach through the use of ‘Landscape Maps’ that are amenable to analysis using Bayesian belief networks. GLAMS captured expert knowledge which yielded spatially and temporally relevant information on changes in farmers land use behaviour including land use change, adaptations to extended dry periods, and values of ecohydrological functions. GLAMS was able to capture farmer responses at variable spatial and temporal scales, in this case for three property sizes and from two months to greater than decades temporal scales. The approach generated information on the general management responses of farmers during average climatic conditions or extended dry periods and at different landscape

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positions. GLAMS also was able to estimate the ‘value’ farmers placed on ecohydrological functions in various parts of the landscape. These attributes of GLAMS represent a substantial improvement over exiting survey techniques, and add considerable value to the process of engaging farmers to capture their underlying preferences for land use and values ecohydrological management. The GLAMS approach was somewhat intuitive for framers to grasp and quick to complete. It is, therefore, suited to Landcare or catchment group surveys.

5.3.2 Major Implications for NRM and Catchment Management Specifically, water availability and water quality along with sheet and rill erosion were consistently the most important ecohydrological risks suggested by farmers in the Western Catchments region (see Chapter 3). The fact that there were some discrepancies between values for water quality in riparian areas to that of farm dams, however, highlights the differences between farm management objectives to those of catchment objectives. More generally, an interesting finding from GLAMS was that property size affected management flexibility and responsiveness to environmental change, particularly how increasing property size led to greater temporal lag before land use change occurred during extended dry periods (see Figure 3.8). This highlights the possibility that the ‘experience’ of drought is different for different farmers, or possibly that larger properties are more resilient. Property size was also found to be correlated with farmer values of native vegetation in the Brigalow Belt of central Queensland Seabrook et al. (2007), suggesting that landscape restoration designs generally need to consider the scale of production and a farmer’s capacity to change. Cary et al. (2002) suggest a range of external factors that influence land use change, such as farm income, age, training, having a farm plan, perception of financial security, and membership of a Landcare group. Bryan et al. (2006) suggest the drivers for farmers in the lower Murray-Darling Basin, include climate and societal change, commodity prices, and technological advances. Although GLAMS was not designed to isolate the key external socio-

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economic drivers of land use change, it is likely to be based on a somewhat similar set of factors as property size, land use history, age, education, finance, social networks and traditions. In reality, however, these drivers differ between regions depending upon a number of social and environmental factors as well as the relative profitability and appropriateness of a management action for a given location (Barr and Cary 2000; Cary et al. 2001). Catchment planning objectives must foster a common perspective with a farmer’s values to adapt to climate change, water quality, scarcity and environmental flows, the conservation of biodiversity and landscape aesthetics. While most farmers are quiet aware of the need to preserve the functioning of many ecohydrological systems, it is unlikely that widespread NRM benefits can be achieved until the costs incurred from individual actions undertaken by a farmer are offset to a sufficient level (Bryan et al. 2006). To bridge this gap, the local community must play a key role in the formulation of natural resource and catchment management plans, targets, and management actions to ensure effective understanding and uptake by farmers (Ewing et al. 2000).


Apply the GLAMS process to alternate regions by modifying the Landscape Maps.

ii)

Develop an electronic version of GLAMS based upon A3 sized digitizing tablets, and establish a direct link between GLAMS and BBN's through a GIS environment.

iii)

Include framers, Landcare and catchment management groups in survey designs and applications.

iv)

Include local community in the formulation of natural resource and catchment management plans.

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5.4

Objective 3 - The LEACS Model

5.4.1 Achievement and Major Outcomes The LEACS model was divided into two phases: 1) Phase I – to create a systems model capable of modifying preference values (of farmers) for land cover change based on magnitude of water velocity leaving a hillslope, and to demonstrate ‘proof of concept’ that a common data link of the above model with an existing ; and 2) Phase II - to distributed hydrological process model could yield sound ecohydrological restoration designs for hillslopes and small catchments.

5.4.1.1

Phase I – LEACS STELLA Phase I was a simple constrained optimisation process built in using the Stella systems

modelling software. This model calculates a ‘preference value’ (or P value) for a land cover to be applied in a particular 100 m2 cell of the landscape based on the volume and velocity of water generated by a distributed hydrological simulation model (MIKE SHE). The aim was for the P value within each grid cell (10 m x 10 m) to be reinforced (i.e. become more likely to be applied as a land cover) when the value returned form the hydrological simulation is below the recommended end of catchment (EOC) target value for a given performance factor, such as volume of run-off or sediment and nutrient exports. Where values were greater than recommended, the P value would decrease until such time that land cover causing the problem is replaced with a ‘less leaky’ land cover, such as from pastures to tree belts. To allow testing of LEACS Phase I to see if the model responded as per the logic stated above, a set of hypothetical data arrays were generated and imported through MS Excel into the LEACS STELLA model. The hypothetical data arrays mimicked the flow velocity simulated for Ivory Creek, with some Pm values (water runoff and velocity) deliberately being greater than the EOC target set in the LEACS STELLA model’s RecPm variable. Figures 4.12 through to 4.15 show that the model responded to these data arrays by adjusting

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the LCA and LCB (i.e. land cover P values) when Pm values were greater than the stipulated RecPm target. While no land cover actually changed from pasture to tree belts in Phase I, it did show proof of concept that the model was responding in the manner necessary for it to reflect the response in land use preferences when ecohydrological performance was sub-optimal. This suggests that with a dynamic link to a physically based distributed hydrological model to provide the simulated values for water runoff and velocity, sediment erosion or nutrient transport, LEACS would offer a means to objectively find ecohydrological restoration designs that target individual hotspots on hillslopes without the need to do so by manual manipulation of the land cover variable. There are examples of modelling approaches where farmer decision making has been simulated with agent based models to provide projections of potential land use change and adoption of NRM practices (Bryan et al. 2006). The major differences between this approach and LEACS is that the land use preferences in LEACS are based on the ecohydrological responses of a catchment based on outputs from a physically based distributed hydrological simulation. While LEACS did not utilise agent based models as in Bryan et al. (2006), this would be feasible where an individual agent ‘object’ was able to update itself based on a dynamic link with a distributed hydrological simulation model. Each agent could reflect one grid cell with its own set of variables, or a farmer which ‘managed’ a group of grid cells that is consistent with real-world cadastre. An example of a model which does use a hydrological model in combination with socio-economic factors to integrate agricultural economics, ecology and hydrology, is the model by Weber et al. (2001). This approach uses a GIS to link three simulation models ProLand for socio-economic factors, ELLA for ecological processes, and SWAT for hydrological functioning at a regional scale. The impacts on biodiversity and run-off and

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stream-flow resulting from land use change were able to be assessed through a common data sharing environment. One of the major differences between this approach and LEACS is the scale. Many of the socio-ecological and hydrological models used to develop land use change scenarios focus on regional or river basin scales, while LEACS aimed to utilise known decision making preferences from actual farmers at hillslope scales. Of course, the downside is that LEACS requires greater parameterisation, data storage is very large, and modelling time increases markedly. The upside is that LEACS is directly relevant to scales of farm management, which increases the likelihood of uptake by farmer and catchment groups. LEACS is a prototype design that changes land cover configuration to improve the ecohydrological functioning of landscapes. At this stage there is no dynamic link between LEACS Phase I and Phase II. To improve LEACS PHASE I would require a dynamic link to be established with a distributed hydrological simulation model, followed by real-world testing of ecohydrological restoration designs based on data derived from long-term field monitoring. This process, however, was well beyond the scope of this Thesis. It is a large undertaking and rather complicated process to achieve such an integrated modelling approach, and as such, it is recommended that future model develop utilise a team of experts and a group of supportive farmers.

5.4.1.2

Phase II – LEACS MIKE SHE The MIKE SHE (DHI 2005) model was used to simulate the difference between

pasture alone and pasture/tree belts together within grid cells to assess the change in ecohydrological functioning as a result of land cover change to tree belts (100 m2) on steep hillslopes. Even without contour bunds included in the simulations of tree belts, stormwater run-off was intercepted and water velocities slowed across the hillslopes, which re-directed water across the slope increasing infiltration. The orientation of the tree belts diagonal to the

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slope at an approximate 3% gradient and the added surface roughness, resulted in lateral flow of water along the tree belt (Figure 4.21), a considerable degree of water also infiltrated compared to the pasture simulations (Figures 4.22b and 4.22a respectively). The interception and infiltration of stormwater runoff within the tree belts reduced flow across a 30 m second order stream by between 4.5-9.0 ML/15 min-1 at peak flows, which is enough to fill a large (> 1M) farm dam in approximately three to five hours. In field experiments using a rainfall simulator, Ellis et al. (2006) found that tree belts gained up to 37% more water above incident rainfall. This represents a substantial additional store of soil moisture and nutrients available to plants within the tree belts able to be redistributed. Ticehurst et al. (2005) investigated the effect of tree belt designs on overland and lateral subsurface flows on hillslope in the Billabong Creek Catchment, Holbrook, NSW. The authors suggested that shallow lateral flow paths are intercepted by tree belts, but this effect will vary depending on antecedent soil moisture and the intensity of the precipitation event. Newham et al. (2003) consider that a rigorous model requires balancing water-table depth, infiltration rates, soil water holding capacity at saturated and field conditions, and surface roughness (Manning’s n). Although MIKE SHE uses Manning’s M, changing parameter values between 0.15 and 0.45 affected outputs of water depth, velocity and duration of flows. The generation of stormwater run-off was very sensitive to changes in the parameter ‘depth to water-table’, while the lack of availability of a ‘two-layer’ soil landscape profile, also limited confidence in simulated effects of tree belts. To make simulations more robust requires an adequate representation of the actual real-world processes inferred by the model (Mendoza et al. 2002), including an ability to capture the non-linear dynamics between the scale of a patch to configuration of patches at landscape scales (Ludwig et al. 2007). For example, the timing or magnitude of a climatic event and the antecedent soil water content have a bearing on the dominant processes acting on a hillslope at a given time (Ticehurst et al. 2005). This has importance for modelling the

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effects of tree belts on hillslope hydrology, as to have confidence in projections, the parameters and variables need to be measured or estimated at fine resolution to deal with spatial and temporal heterogeneity (Takken et al. 1999). Gathering this type of data to better parameterise the model, however, requires a substantial investment in monitoring to provide data to parameterise model variables (Wainwright et al. 1999).

5.4.2 Implications for Natural Resource and Catchment Management During extended dry periods, depleted pastures have minimal biomass which exposes more soil to compaction and wind and water erosion, and creek banks become fragile and mobile. Effective land management, therefore, must accommodate the impact of extreme events which are likely to be the most damaging (Bird et al. 1992). It is suggested that one of the greatest risks to ecohydrological systems in any catchment arises during intense thunderstorms that occur within extended dry periods (i.e. drought). An important implication for NRM across the steep headwaters of the Maronghi Creek catchment, is that large subsurface flows of water result from the formation of shallow and porous soils on an impermeable hard-pan of Granodiorite of variable depth. This generates large amounts of kinetic energy and erosive power through the rapid runoff of water within gullies and creeks, which results in stream bank erosion, gullying, and sand slugs of deposited sediments (see Figures 4.6 and 4.7). Catchments comprised of granite and granodiorite type rocks, are shown to be prone to gullying and rill erosion (Murphy and Flewin 1993; Rutherford 2000). Streams that are sourced within granite geology often have beds that have been aggraded by sand-sized particles as this size class of sediment is the most easily transported as bed-load. The resulting ‘sand-slugs’ tend to move as a ‘wave’ down the catchment, in-filling natural pools and turning the bed into a featureless sheet of sand that instigates ‘knock-on’ effect to other parts of the catchment, and may take decades to centuries to recover to natural functioning (Rutherford

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2000). The addition of tree belts to hillslopes increases surface roughness and organic matter in tree belts are efficient in intercepting most runoff, sediment and nutrients in stormwater runoff. Evaporation of soil-water, wind erosion and heat stress on plants are all reduced, providing suitable habitat conditions for soil organisms that soil and plant health (Hairsine and van Dijk 2006). Whereas in drier times, the deep rooted perennials redistribute soil-water in the direction of the greatest difference in soil-water potential and increase moisture content in deeper sub-soils (Burgess et al. 2001). To gain full capacity as a sink, however, designs must comprise both a tree component and an adequate cover of high density and biomass droughttolerant tussock type grasses, sedges or reeds to provide the adequate entrainment, filtering and infiltration of water across hillslopes (Karssies and Prosser 1999; Carey et al. 2000; Hook 2003; Wang et al. 2004). While tree belts can add to the palatability of pasture grasses (Jackson and Ash 2001), if the additional organic matter within the tree belt is subsequently removed by grazing or fire, this may decrease infiltration and increase the risk of erosion (Scanlan 1992). NRM and pasture management are key research questions for the grazing lands of northern and southern Australia (MLA 2005), to demonstrate the potential of designs put forward for ecohydrological restoration, long-term field trials are essential to provide georeferenced data on flux within climatic (e.g. temperature, wind speed and direction), hydro-geomorphic (e.g. stormwater runoff, water velocity, infiltration) and ecological (e.g. plant biomass, LAI variations) variables. But as climatic monitoring is long-term and the technological applications uncertain, establishing pilot projects with farmer partnerships can demonstrate the potential gains in yield and farm-gate returns that may result from tree belts, which may then foster further adoption (Pannell 1999; Cleugh 2003).

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There are clear effects from tree belts on thunderstrom run-off volumes, velocities and spatial re-distributions, most likely resulting from increased surface roughness, infiltration and enhanced evapotranspiration.

ii)

The input parmater estimates for the hydrological model needs finer-scale field data while outputs also need to be validated through longer-term field based monitoring. These findings need to be confirmed with actual filed trials for a measure on the degree to which tree belts increase the resilience of the landscapes. Apply designs in varying climatic and hydrogeomorphic regions.

iii)

A major improvements in LEACS as a process will be when it integrates decision preferences with a distributed hydrological simulation model in purpose built software to allow the seamless integration of data when landcover files are updated.

5.5

Conclusion The synthesis of complex adaptive systems properties and functions through six core

tenets that apply to landscape and ecohydrological systems, adds considerable insight into their functioning and responses to changes induced by humans. When applied to ecohydrological systems, it was shown that small and incremental modifications to landscape structure through land cover change, has positive feedback effects on ecohydrological systems with inherent time-lags between change and response up to several decades. The responses include modified run-off, flooding and environmental flow regimes, sediment load in fluvial systems, lower soil moisture and vegetation growth and biomass, less precipitation recycling, increased lateral wind speed and deceased surface convergence, and decreased evapotranspiration. Management frameworks must acknowledge complex ecohydrological functions that operate over a range of scales in space and time, particularly how patches are non-linearly 137


related to landscape scales, or sub-catchments are non-linearly related to river basin scales. Humans drive land use change which, in turn, modify the buffering and partitioning of flux in climatic, hydrogeomorphic and ecohydrological processes in the landscape. Many of the drivers of land use/cover change are based on the particular values farmers have of a landscape. It is important, therefore, for NRM and ecohydrological restoration alike to take account of the range of the variability in individual perceptions of farmers as this has marked ramifications for the future condition of the landscape. Simulating the hydrological response of a catchment to land cover change is one means to understanding the likely effects of future land use/cover change on ecohydrological functioning. The LEACS model demonstrated a potential tool for increasing the applicability of simulation modelling when designing ecohydrological restoration projects, by adding the values of individual farmers in a spatially explicit manner. The integration of the LEACS model with a multi-agent system modelling approach is a valuable addition to catchment management and NRM. They allow the simulation of possible cost-benefit ratios of a large number of ecohydrological restoration deigns, while recognising the practical constraints facing farmers. The simulation results support the hypothesis that stormwater run-off volumes and velocities will be reduced by adding tree belts to hillslopes, while infiltration and the spatial redistribution of water is also enhanced. This stems from the orientation of tree belts to 3% across the slope gradient and increased surface roughness and evapotranspiration. The tree belts function as a semi-porous buffer and sink in which water, sediments and nutrients are intercepted and stored instead of being lost from the catchment. This has obvious benefits for catchment water quality, but it also benefits production and biodiversity. While in theory tree belts are an effective restoration technique, long-term monitoring (i.e. > ten years) needs to underpin future model development and validation to enable confidence to be had in subsequent design recommendations.

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Ryan, J.G. (2007) PhD Thesis - References

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Ryan, J.G. (2007) PhD Thesis – Appendix A

APPENDIX A: OUTPUTS FROM A SERIES OF LEACS STELLA RUNS.

Run 1 – Original Pvalue set at 0.75 Time Limiter NEW LCA NEW Pvalue LCB Pvalue 0 0.6498 0.6498 0.3502 0.25 0.6498 0.6498 0.3502 0.5 0.645 0.645 0.355 0.75 0.62375 0.62375 0.37625 1 0.585333 0.585333 0.414667 1.25 0.554833 0.554833 0.445167 1.5 0.581 0.581 0.419 1.75 0.645267 0.645267 0.354733 2 0.647467 0.647467 0.352533 2.25 0.64855 0.64855 0.35145 2.5 0.649083 0.649083 0.350917 2.75 0.6494 0.6494 0.3506 3 0.6496 0.6496 0.3504 3.25 0.649708 0.649708 0.350292 3.5 0.6498 0.6498 0.3502 3.75 0.6498 0.6498 0.3502 4 0.6498 0.6498 0.3502 4.25 0.6498 0.6498 0.3502 4.5 0.6498 0.6498 0.3502 4.75 0.6498 0.6498 0.3502 5 0.6498 0.6498 0.3502 5.25 0.6498 0.6498 0.3502 5.5 0.6498 0.6498 0.3502 5.75 0.6498 0.6498 0.3502 6 0.6498 0.6498 0.3502 6.25 0.6498 0.6498 0.3502 6.5 0.6498 0.6498 0.3502 6.75 0.6498 0.6498 0.3502 7 0.6498 0.6498 0.3502 7.25 0.6498 0.6498 0.3502 7.5 0.6498 0.6498 0.3502 7.75 0.6498 0.6498 0.3502 8 0.648533 0.648533 0.351467 8.25 0.631 0.631 0.369 8.5 0.593833 0.593833 0.406167 8.75 0.54625 0.54625 0.45375 9 0.508 0.508 0.492 9.25 0.5165 0.5165 0.4835 9.5 0.545667 0.545667 0.454333 9.75 0.5955 0.5955 0.4045 10 0.6276 0.6276 0.3724 10.25 0.636833 0.636833 0.363167 10.5 0.6435 0.6435 0.3565 10.75 0.645583 0.645583 0.354417 11 0.646 0.646 0.354 11.25 0.649325 0.649325 0.350675 11.5 0.6498 0.6498 0.3502 11.75 0.6498 0.6498 0.3502 12 0.6498 0.6498 0.3502

NewP Conversion

Pm

Pme

Pvalue

RecPm

Transform

Tuner

0.6498 0.6498 0.645 0.62375 0.585333 0.554833 0.581 0.645267 0.647467 0.64855 0.649083 0.6494 0.6496 0.649708 0.6498 0.6498 0.6498 0.6498 0.6498 0.6498 0.6498 0.6498 0.6498 0.6498 0.6498 0.6498 0.6498 0.6498 0.6498 0.6498 0.6498 0.6498 0.648533 0.631 0.593833 0.54625 0.508 0.5165 0.545667 0.5955 0.6276 0.636833 0.6435 0.645583 0.646 0.649325 0.6498 0.6498 0.6498

0.0001 0.0001 0.0025 0.013125 0.032333 0.047583 0.0345 0.002367 0.001267 0.000725 0.000458 0.0003 0.0002 0.000146 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.000733 0.0095 0.028083 0.051875 0.071 0.06675 0.052167 0.02725 0.0112 0.006583 0.00325 0.002208 0.002 0.000338 0.0001 0.0001 0.0001

0.02505 0.02505 0.02625 0.031563 0.041167 0.048792 0.04225 0.026183 0.025633 0.025363 0.025229 0.02515 0.0251 0.025073 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.025367 0.02975 0.039042 0.050938 0.0605 0.058375 0.051083 0.038625 0.0306 0.028292 0.026625 0.026104 0.026 0.025169 0.02505 0.02505 0.02505

0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75

0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05

-0.1002 -0.1002 -0.105 -0.12625 -0.16467 -0.19517 -0.169 -0.10473 -0.10253 -0.10145 -0.10092 -0.1006 -0.1004 -0.10029 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.10147 -0.119 -0.15617 -0.20375 -0.242 -0.2335 -0.20433 -0.1545 -0.1224 -0.11317 -0.1065 -0.10442 -0.104 -0.10068 -0.1002 -0.1002 -0.1002

-0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25

181


Run 2 – Pvalue set at the average of all returns from ‘NEW LCA Pvalue’ in Run 1 - (0.63) Time Limiter NEW NEW NewP Pm Pme Pvalue RecPm LCA LCB Conversion Pvalue Pvalue 0 0.530586 0.530586 0.469414 0.530586 0.0001 0.02505 0.6308 0.05 0.25 0.530586 0.530586 0.469414 0.530586 0.0001 0.02505 0.6308 0.05 0.5 0.525786 0.525786 0.474214 0.525786 0.0025 0.02625 0.6308 0.05 0.75 0.504536 0.504536 0.495464 0.504536 0.013125 0.031563 0.6308 0.05 1 0.466119 0.466119 0.533881 0.466119 0.032333 0.041167 0.6308 0.05 1.25 0.435619 0.435619 0.564381 0.435619 0.047583 0.048792 0.6308 0.05 1.5 0.461786 0.461786 0.538214 0.461786 0.0345 0.04225 0.6308 0.05 1.75 0.526053 0.526053 0.473947 0.526053 0.002367 0.026183 0.6308 0.05 2 0.528253 0.528253 0.471747 0.528253 0.001267 0.025633 0.6308 0.05 2.25 0.529336 0.529336 0.470664 0.529336 0.000725 0.025363 0.6308 0.05 2.5 0.529869 0.529869 0.470131 0.529869 0.000458 0.025229 0.6308 0.05 2.75 0.530186 0.530186 0.469814 0.530186 0.0003 0.02515 0.6308 0.05 3 0.530386 0.530386 0.469614 0.530386 0.0002 0.0251 0.6308 0.05 3.25 0.530494 0.530494 0.469506 0.530494 0.000146 0.025073 0.6308 0.05 3.5 0.530586 0.530586 0.469414 0.530586 0.0001 0.02505 0.6308 0.05 3.75 0.530586 0.530586 0.469414 0.530586 0.0001 0.02505 0.6308 0.05 4 0.530586 0.530586 0.469414 0.530586 0.0001 0.02505 0.6308 0.05 4.25 0.530586 0.530586 0.469414 0.530586 0.0001 0.02505 0.6308 0.05 4.5 0.530586 0.530586 0.469414 0.530586 0.0001 0.02505 0.6308 0.05 4.75 0.530586 0.530586 0.469414 0.530586 0.0001 0.02505 0.6308 0.05 5 0.530586 0.530586 0.469414 0.530586 0.0001 0.02505 0.6308 0.05 5.25 0.530586 0.530586 0.469414 0.530586 0.0001 0.02505 0.6308 0.05 5.5 0.530586 0.530586 0.469414 0.530586 0.0001 0.02505 0.6308 0.05 5.75 0.530586 0.530586 0.469414 0.530586 0.0001 0.02505 0.6308 0.05 6 0.530586 0.530586 0.469414 0.530586 0.0001 0.02505 0.6308 0.05 6.25 0.530586 0.530586 0.469414 0.530586 0.0001 0.02505 0.6308 0.05 6.5 0.530586 0.530586 0.469414 0.530586 0.0001 0.02505 0.6308 0.05 6.75 0.530586 0.530586 0.469414 0.530586 0.0001 0.02505 0.6308 0.05 7 0.530586 0.530586 0.469414 0.530586 0.0001 0.02505 0.6308 0.05 7.25 0.530586 0.530586 0.469414 0.530586 0.0001 0.02505 0.6308 0.05 7.5 0.530586 0.530586 0.469414 0.530586 0.0001 0.02505 0.6308 0.05 7.75 0.530586 0.530586 0.469414 0.530586 0.0001 0.02505 0.6308 0.05 8 0.529319 0.529319 0.470681 0.529319 0.000733 0.025367 0.6308 0.05 8.25 0.511786 0.511786 0.488214 0.511786 0.0095 0.02975 0.6308 0.05 8.5 0.474619 0.474619 0.525381 0.474619 0.028083 0.039042 0.6308 0.05 8.75 0.427036 0.427036 0.572964 0.427036 0.051875 0.050938 0.6308 0.05 9 0.388786 0.388786 0.611214 0.388786 0.071 0.0605 0.6308 0.05 9.25 0.397286 0.397286 0.602714 0.397286 0.06675 0.058375 0.6308 0.05 9.5 0.426453 0.426453 0.573547 0.426453 0.052167 0.051083 0.6308 0.05 9.75 0.476286 0.476286 0.523714 0.476286 0.02725 0.038625 0.6308 0.05 10 0.508386 0.508386 0.491614 0.508386 0.0112 0.0306 0.6308 0.05 10.25 0.517619 0.517619 0.482381 0.517619 0.006583 0.028292 0.6308 0.05 10.5 0.524286 0.524286 0.475714 0.524286 0.00325 0.026625 0.6308 0.05 10.75 0.526369 0.526369 0.473631 0.526369 0.002208 0.026104 0.6308 0.05 11 0.526786 0.526786 0.473214 0.526786 0.002 0.026 0.6308 0.05 11.25 0.530111 0.530111 0.469889 0.530111 0.000338 0.025169 0.6308 0.05 11.5 0.530586 0.530586 0.469414 0.530586 0.0001 0.02505 0.6308 0.05 11.75 0.530586 0.530586 0.469414 0.530586 0.0001 0.02505 0.6308 0.05 12 0.530586 0.530586 0.469414 0.530586 0.0001 0.02505 0.6308 0.05

Transform

Tuner

-0.1002 -0.1002 -0.105 -0.12625 -0.16467 -0.19517 -0.169 -0.10473 -0.10253 -0.10145 -0.10092 -0.1006 -0.1004 -0.10029 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.10147 -0.119 -0.15617 -0.20375 -0.242 -0.2335 -0.20433 -0.1545 -0.1224 -0.11317 -0.1065 -0.10442 -0.104 -0.10068 -0.1002 -0.1002 -0.1002

-0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25

182


Run 3 – Pvalue set at the average of all returns from ‘NEW LCA Pvalue’ in Run 2 - (0.51) Time Limiter NEW NEW NewP Pm Pme Pvalue LCA LCB Conversion Pvalue Pvalue 0 0.411372 0.411372 0.588628 0.411372 0.0001 0.02505 0.5116 0.25 0.411372 0.411372 0.588628 0.411372 0.0001 0.02505 0.5116 0.5 0.406572 0.406572 0.593428 0.406572 0.0025 0.02625 0.5116 0.75 0.385322 0.385322 0.614678 0.385322 0.013125 0.031563 0.5116 1 0.346905 0.346905 0.653095 0.346905 0.032333 0.041167 0.5116 1.25 0.316405 0.316405 0.683595 0.316405 0.047583 0.048792 0.5116 1.5 0.342572 0.342572 0.657428 0.342572 0.0345 0.04225 0.5116 1.75 0.406839 0.406839 0.593161 0.406839 0.002367 0.026183 0.5116 2 0.409039 0.409039 0.590961 0.409039 0.001267 0.025633 0.5116 2.25 0.410122 0.410122 0.589878 0.410122 0.000725 0.025363 0.5116 2.5 0.410655 0.410655 0.589345 0.410655 0.000458 0.025229 0.5116 2.75 0.410972 0.410972 0.589028 0.410972 0.0003 0.02515 0.5116 3 0.411172 0.411172 0.588828 0.411172 0.0002 0.0251 0.5116 3.25 0.41128 0.41128 0.58872 0.41128 0.000146 0.025073 0.5116 3.5 0.411372 0.411372 0.588628 0.411372 0.0001 0.02505 0.5116 3.75 0.411372 0.411372 0.588628 0.411372 0.0001 0.02505 0.5116 4 0.411372 0.411372 0.588628 0.411372 0.0001 0.02505 0.5116 4.25 0.411372 0.411372 0.588628 0.411372 0.0001 0.02505 0.5116 4.5 0.411372 0.411372 0.588628 0.411372 0.0001 0.02505 0.5116 4.75 0.411372 0.411372 0.588628 0.411372 0.0001 0.02505 0.5116 5 0.411372 0.411372 0.588628 0.411372 0.0001 0.02505 0.5116 5.25 0.411372 0.411372 0.588628 0.411372 0.0001 0.02505 0.5116 5.5 0.411372 0.411372 0.588628 0.411372 0.0001 0.02505 0.5116 5.75 0.411372 0.411372 0.588628 0.411372 0.0001 0.02505 0.5116 6 0.411372 0.411372 0.588628 0.411372 0.0001 0.02505 0.5116 6.25 0.411372 0.411372 0.588628 0.411372 0.0001 0.02505 0.5116 6.5 0.411372 0.411372 0.588628 0.411372 0.0001 0.02505 0.5116 6.75 0.411372 0.411372 0.588628 0.411372 0.0001 0.02505 0.5116 7 0.411372 0.411372 0.588628 0.411372 0.0001 0.02505 0.5116 7.25 0.411372 0.411372 0.588628 0.411372 0.0001 0.02505 0.5116 7.5 0.411372 0.411372 0.588628 0.411372 0.0001 0.02505 0.5116 7.75 0.411372 0.411372 0.588628 0.411372 0.0001 0.02505 0.5116 8 0.410105 0.410105 0.589895 0.410105 0.000733 0.025367 0.5116 8.25 0.392572 0.392572 0.607428 0.392572 0.0095 0.02975 0.5116 8.5 0.355405 0.355405 0.644595 0.355405 0.028083 0.039042 0.5116 8.75 0.307822 0.307822 0.692178 0.307822 0.051875 0.050938 0.5116 9 0.269572 0.269572 0.730428 0.269572 0.071 0.0605 0.5116 9.25 0.278072 0.278072 0.721928 0.278072 0.06675 0.058375 0.5116 9.5 0.307239 0.307239 0.692761 0.307239 0.052167 0.051083 0.5116 9.75 0.357072 0.357072 0.642928 0.357072 0.02725 0.038625 0.5116 10 0.389172 0.389172 0.610828 0.389172 0.0112 0.0306 0.5116 10.25 0.398405 0.398405 0.601595 0.398405 0.006583 0.028292 0.5116 10.5 0.405072 0.405072 0.594928 0.405072 0.00325 0.026625 0.5116 10.75 0.407155 0.407155 0.592845 0.407155 0.002208 0.026104 0.5116 11 0.407572 0.407572 0.592428 0.407572 0.002 0.026 0.5116 11.25 0.410897 0.410897 0.589103 0.410897 0.000338 0.025169 0.5116 11.5 0.411372 0.411372 0.588628 0.411372 0.0001 0.02505 0.5116 11.75 0.411372 0.411372 0.588628 0.411372 0.0001 0.02505 0.5116 12 0.411372 0.411372 0.588628 0.411372 0.0001 0.02505 0.5116

RecPm

Transform Tuner

0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05

-0.1002 -0.1002 -0.105 -0.12625 -0.16467 -0.19517 -0.169 -0.10473 -0.10253 -0.10145 -0.10092 -0.1006 -0.1004 -0.10029 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.10147 -0.119 -0.15617 -0.20375 -0.242 -0.2335 -0.20433 -0.1545 -0.1224 -0.11317 -0.1065 -0.10442 -0.104 -0.10068 -0.1002 -0.1002 -0.1002

-0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25

183


Run 4 – Original Pvalue set at 0.75 Time Limiter NEW NEW LCA LCB Pvalue Pvalue 0 0.6498 0.6498 0.3502 0.25 0.6498 0.6498 0.3502 0.5 0.645 0.645 0.355 0.75 0.62375 0.62375 0.37625 1 0.584 0.584 0.416 1.25 0.536333 0.536333 0.463667 1.5 0.567 0.567 0.433 1.75 0.645267 0.645267 0.354733 2 0.647467 0.647467 0.352533 2.25 0.64855 0.64855 0.35145 2.5 0.649083 0.649083 0.350917 2.75 0.6494 0.6494 0.3506 3 0.6496 0.6496 0.3504 3.25 0.649708 0.649708 0.350292 3.5 0.6498 0.6498 0.3502 3.75 0.6498 0.6498 0.3502 4 0.6498 0.6498 0.3502 4.25 0.6498 0.6498 0.3502 4.5 0.6498 0.6498 0.3502 4.75 0.6498 0.6498 0.3502 5 0.6498 0.6498 0.3502 5.25 0.6498 0.6498 0.3502 5.5 0.6498 0.6498 0.3502 5.75 0.6498 0.6498 0.3502 6 0.6498 0.6498 0.3502 6.25 0.6498 0.6498 0.3502 6.5 0.6498 0.6498 0.3502 6.75 0.6498 0.6498 0.3502 7 0.6498 0.6498 0.3502 7.25 0.6498 0.6498 0.3502 7.5 0.6498 0.6498 0.3502 7.75 0.6498 0.6498 0.3502 8 0.648533 0.648533 0.351467 8.25 0.631 0.631 0.369 8.5 0.593833 0.593833 0.406167 8.75 0.54625 0.54625 0.45375 9 0.508 0.508 0.492 9.25 0.465333 0.465333 0.534667 9.5 0.48 0.48 0.52 9.75 0.5085 0.5085 0.4915 10 0.542667 0.542667 0.457333 10.25 0.579 0.579 0.421 10.5 0.614 0.614 0.386 10.75 0.629167 0.629167 0.370833 11 0.637333 0.637333 0.362667 11.25 0.64375 0.64375 0.35625 11.5 0.644 0.644 0.356 11.75 0.6458 0.6458 0.3542 12 0.6498 0.6498 0.3502

NewP Conversion

Pm

Pme

Pvalue

RecPm

Transform Tuner

0.6498 0.6498 0.645 0.62375 0.584 0.536333 0.567 0.645267 0.647467 0.64855 0.649083 0.6494 0.6496 0.649708 0.6498 0.6498 0.6498 0.6498 0.6498 0.6498 0.6498 0.6498 0.6498 0.6498 0.6498 0.6498 0.6498 0.6498 0.6498 0.6498 0.6498 0.6498 0.648533 0.631 0.593833 0.54625 0.508 0.465333 0.48 0.5085 0.542667 0.579 0.614 0.629167 0.637333 0.64375 0.644 0.6458 0.6498

0.0001 0.0001 0.0025 0.013125 0.033 0.056833 0.0415 0.002367 0.001267 0.000725 0.000458 0.0003 0.0002 0.000146 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.000733 0.0095 0.028083 0.051875 0.071 0.092333 0.085 0.07075 0.053667 0.0355 0.018 0.010417 0.006333 0.003125 0.003 0.0021 0.0001

0.02505 0.02505 0.02625 0.031563 0.0415 0.053417 0.04575 0.026183 0.025633 0.025363 0.025229 0.02515 0.0251 0.025073 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.025367 0.02975 0.039042 0.050938 0.0605 0.071167 0.0675 0.060375 0.051833 0.04275 0.034 0.030208 0.028167 0.026563 0.0265 0.02605 0.02505

0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75

0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05

-0.1002 -0.1002 -0.105 -0.12625 -0.166 -0.21367 -0.183 -0.10473 -0.10253 -0.10145 -0.10092 -0.1006 -0.1004 -0.10029 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.10147 -0.119 -0.15617 -0.20375 -0.242 -0.28467 -0.27 -0.2415 -0.20733 -0.171 -0.136 -0.12083 -0.11267 -0.10625 -0.106 -0.1042 -0.1002

-0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25

184


Run 5 – Pvalue set at the average of all returns from ‘NEW LCA Pvalue’ in Run 4 - (0.38) Time Limiter NEW NEW NewP Pm Pme Pvalue RecPm LCA LCB Conversion Pvalue Pvalue 0 0.278206 0.278206 0.721794 0.278206 0.0001 0.02505 0.378406 0.05 0.25 0.278206 0.278206 0.721794 0.278206 0.0001 0.02505 0.378406 0.05 0.5 0.273406 0.273406 0.726594 0.273406 0.0025 0.02625 0.378406 0.05 0.75 0.252156 0.252156 0.747844 0.252156 0.013125 0.03156 0.378406 0.05 3 1 0.212406 0.212406 0.787594 0.212406 0.033 0.0415 0.378406 0.05 1.25 0.164739 0.164739 0.835261 0.164739 0.056833 0.05341 0.378406 0.05 7 1.5 0.195406 0.195406 0.804594 0.195406 0.0415 0.04575 0.378406 0.05 1.75 0.273672 0.273672 0.726328 0.273672 0.002367 0.02618 0.378406 0.05 3 2 0.275872 0.275872 0.724128 0.275872 0.001267 0.02563 0.378406 0.05 2.25 0.276956 0.276956 0.723044 0.276956 0.000725 30.02536 0.378406 0.05 3 2.5 0.277489 0.277489 0.722511 0.277489 0.000458 0.02522 0.378406 0.05 9 2.75 0.277806 0.277806 0.722194 0.277806 0.0003 0.02515 0.378406 0.05 3 0.278006 0.278006 0.721994 0.278006 0.0002 0.0251 0.378406 0.05 3.25 0.278114 0.278114 0.721886 0.278114 0.000146 0.02507 0.378406 0.05 3 3.5 0.278206 0.278206 0.721794 0.278206 0.0001 0.02505 0.378406 0.05 3.75 0.278206 0.278206 0.721794 0.278206 0.0001 0.02505 0.378406 0.05 4 0.278206 0.278206 0.721794 0.278206 0.0001 0.02505 0.378406 0.05 4.25 0.278206 0.278206 0.721794 0.278206 0.0001 0.02505 0.378406 0.05 4.5 0.278206 0.278206 0.721794 0.278206 0.0001 0.02505 0.378406 0.05 4.75 0.278206 0.278206 0.721794 0.278206 0.0001 0.02505 0.378406 0.05 5 0.278206 0.278206 0.721794 0.278206 0.0001 0.02505 0.378406 0.05 5.25 0.278206 0.278206 0.721794 0.278206 0.0001 0.02505 0.378406 0.05 5.5 0.278206 0.278206 0.721794 0.278206 0.0001 0.02505 0.378406 0.05 5.75 0.278206 0.278206 0.721794 0.278206 0.0001 0.02505 0.378406 0.05 6 0.278206 0.278206 0.721794 0.278206 0.0001 0.02505 0.378406 0.05 6.25 0.278206 0.278206 0.721794 0.278206 0.0001 0.02505 0.378406 0.05 6.5 0.278206 0.278206 0.721794 0.278206 0.0001 0.02505 0.378406 0.05 6.75 0.278206 0.278206 0.721794 0.278206 0.0001 0.02505 0.378406 0.05 7 0.278206 0.278206 0.721794 0.278206 0.0001 0.02505 0.378406 0.05 7.25 0.278206 0.278206 0.721794 0.278206 0.0001 0.02505 0.378406 0.05 7.5 0.278206 0.278206 0.721794 0.278206 0.0001 0.02505 0.378406 0.05 7.75 0.278206 0.278206 0.721794 0.278206 0.0001 0.02505 0.378406 0.05 8 0.276939 0.276939 0.723061 0.276939 0.000733 0.02536 0.378406 0.05 8.25 0.259406 0.259406 0.740594 0.259406 0.0095 70.02975 0.378406 0.05 8.5 0.222239 0.222239 0.777761 0.222239 0.028083 0.03904 0.378406 0.05 2 8.75 0.174656 0.174656 0.825344 0.174656 0.051875 0.05093 0.378406 0.05 8 9 0.136406 0.136406 0.863594 0.136406 0.071 0.0605 0.378406 0.05 9.25 0.093739 0.093739 0.906261 0.093739 0.092333 0.07116 0.378406 0.05 7 9.5 0.108406 0.108406 0.891594 0.108406 0.085 0.0675 0.378406 0.05 9.75 0.136906 0.136906 0.863094 0.136906 0.07075 0.06037 0.378406 0.05 10 0.171072 0.171072 0.828928 0.171072 0.053667 50.05183 0.378406 0.05 3 10.25 0.207406 0.207406 0.792594 0.207406 0.0355 0.04275 0.378406 0.05 10.5 0.242406 0.242406 0.757594 0.242406 0.018 0.034 0.378406 0.05 10.75 0.257572 0.257572 0.742428 0.257572 0.010417 0.03020 0.378406 0.05 8 11 0.265739 0.265739 0.734261 0.265739 0.006333 0.02816 0.378406 0.05 7 11.25 0.272156 0.272156 0.727844 0.272156 0.003125 0.02656 0.378406 0.05 30.0265 0.378406 0.05 11.5 0.272406 0.272406 0.727594 0.272406 0.003 11.75 0.274206 0.274206 0.725794 0.274206 0.0021 0.02605 0.378406 0.05 12 0.278206 0.278206 0.721794 0.278206 0.0001 0.02505 0.378406 0.05

Transform

Tuner

-0.1002 -0.1002 -0.105 -0.12625 -0.166 -0.21367 -0.183 -0.10473 -0.10253 -0.10145 -0.10092 -0.1006 -0.1004 -0.10029 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.1002 -0.10147 -0.119 -0.15617 -0.20375 -0.242 -0.28467 -0.27 -0.2415 -0.20733 -0.171 -0.136 -0.12083 -0.11267 -0.10625 -0.106 -0.1042 -0.1002

-0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25

185


Run 6 – The effect of adjusting the Tuner to a higher value - (0.38) Time Limiter NEW NEW NewP Pm LCA LCB Conversion Pvalue Pvalue 0 0.77495 0.77495 0.22505 0.77495 0.0001 0.25 0.77495 0.77495 0.22505 0.77495 0.0001 0.5 0.77375 0.77375 0.22625 0.77375 0.0025 0.75 0.768438 0.768438 0.231563 0.768438 0.013125 1 0.7585 0.7585 0.2415 0.7585 0.033 1.25 0.746583 0.746583 0.253417 0.746583 0.056833 1.5 0.75425 0.75425 0.24575 0.75425 0.0415 1.75 0.773817 0.773817 0.226183 0.773817 0.002367 2 0.774367 0.774367 0.225633 0.774367 0.001267 2.25 0.774637 0.774637 0.225363 0.774637 0.000725 2.5 0.774771 0.774771 0.225229 0.774771 0.000458 2.75 0.77485 0.77485 0.22515 0.77485 0.0003 3 0.7749 0.7749 0.2251 0.7749 0.0002 3.25 0.774927 0.774927 0.225073 0.774927 0.000146 3.5 0.77495 0.77495 0.22505 0.77495 0.0001 3.75 0.77495 0.77495 0.22505 0.77495 0.0001 4 0.77495 0.77495 0.22505 0.77495 0.0001 4.25 0.77495 0.77495 0.22505 0.77495 0.0001 4.5 0.77495 0.77495 0.22505 0.77495 0.0001 4.75 0.77495 0.77495 0.22505 0.77495 0.0001 5 0.77495 0.77495 0.22505 0.77495 0.0001 5.25 0.77495 0.77495 0.22505 0.77495 0.0001 5.5 0.77495 0.77495 0.22505 0.77495 0.0001 5.75 0.77495 0.77495 0.22505 0.77495 0.0001 6 0.77495 0.77495 0.22505 0.77495 0.0001 6.25 0.77495 0.77495 0.22505 0.77495 0.0001 6.5 0.77495 0.77495 0.22505 0.77495 0.0001 6.75 0.77495 0.77495 0.22505 0.77495 0.0001 7 0.77495 0.77495 0.22505 0.77495 0.0001 7.25 0.77495 0.77495 0.22505 0.77495 0.0001 7.5 0.77495 0.77495 0.22505 0.77495 0.0001 7.75 0.77495 0.77495 0.22505 0.77495 0.0001 8 0.774633 0.774633 0.225367 0.774633 0.000733 8.25 0.77025 0.77025 0.22975 0.77025 0.0095 8.5 0.760958 0.760958 0.239042 0.760958 0.028083 8.75 0.749062 0.749062 0.250938 0.749062 0.051875 9 0.7395 0.7395 0.2605 0.7395 0.071 9.25 0.728833 0.728833 0.271167 0.728833 0.092333 9.5 0.7325 0.7325 0.2675 0.7325 0.085 9.75 0.739625 0.739625 0.260375 0.739625 0.07075 10 0.748167 0.748167 0.251833 0.748167 0.053667 10.25 0.75725 0.75725 0.24275 0.75725 0.0355 10.5 0.766 0.766 0.234 0.766 0.018 10.75 0.769792 0.769792 0.230208 0.769792 0.010417 11 0.771833 0.771833 0.228167 0.771833 0.006333 11.25 0.773438 0.773438 0.226563 0.773438 0.003125 11.5 0.7735 0.7735 0.2265 0.7735 0.003 11.75 0.77395 0.77395 0.22605 0.77395 0.0021 12 0.77495 0.77495 0.22505 0.77495 0.0001

Pme

Pvalue

RecPm

Transform Tuner

0.02505 0.02505 0.02625 0.031563 0.0415 0.053417 0.04575 0.026183 0.025633 0.025363 0.025229 0.02515 0.0251 0.025073 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.02505 0.025367 0.02975 0.039042 0.050938 0.0605 0.071167 0.0675 0.060375 0.051833 0.04275 0.034 0.030208 0.028167 0.026563 0.0265 0.02605 0.02505

0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75

0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05

2.50E-02 2.50E-02 0.02375 0.018438 0.0085 -0.00342 0.00425 0.023817 0.024367 0.024638 0.024771 0.02485 0.0249 2.49E-02 2.50E-02 2.50E-02 2.50E-02 2.50E-02 2.50E-02 2.50E-02 2.50E-02 2.50E-02 2.50E-02 2.50E-02 2.50E-02 2.50E-02 2.50E-02 2.50E-02 2.50E-02 2.50E-02 2.50E-02 2.50E-02 0.024633 0.02025 0.010958 -0.00094 -0.0105 -0.02117 -0.0175 -0.01038 -0.00183 0.00725 0.016 0.019792 0.021833 0.023438 0.0235 2.40E-02 2.50E-02

0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125

186


Run 7 – The effect of changing the RecPm value of 0.02. Time Limiter NEW NEW LCBNewP LCA Pvalue Conversion Pvalue 0 0.749904 0.749904 0.250096 0.749904 0.25 0.749904 0.749904 0.250096 0.749904 0.5 0.747597 0.747597 0.252403 0.747597 0.75 0.737384 0.737384 0.262616 0.737384 1 0.718279 0.718279 0.281721 0.718279 1.25 0.695369 0.695369 0.304631 0.695369 1.5 0.710108 0.710108 0.289892 0.710108 1.75 0.747725 0.747725 0.252275 0.747725 2 0.748782 0.748782 0.251218 0.748782 2.25 0.749303 0.749303 0.250697 0.749303 2.5 0.749559 0.749559 0.250441 0.749559 2.75 0.749712 0.749712 0.250288 0.749712 3 0.749808 0.749808 0.250192 0.749808 3.25 0.74986 0.74986 0.25014 0.74986 3.5 0.749904 0.749904 0.250096 0.749904 3.75 0.749904 0.749904 0.250096 0.749904 4 0.749904 0.749904 0.250096 0.749904 4.25 0.749904 0.749904 0.250096 0.749904 4.5 0.749904 0.749904 0.250096 0.749904 4.75 0.749904 0.749904 0.250096 0.749904 5 0.749904 0.749904 0.250096 0.749904 5.25 0.749904 0.749904 0.250096 0.749904 5.5 0.749904 0.749904 0.250096 0.749904 5.75 0.749904 0.749904 0.250096 0.749904 6 0.749904 0.749904 0.250096 0.749904 6.25 0.749904 0.749904 0.250096 0.749904 6.5 0.749904 0.749904 0.250096 0.749904 6.75 0.749904 0.749904 0.250096 0.749904 7 0.749904 0.749904 0.250096 0.749904 7.25 0.749904 0.749904 0.250096 0.749904 7.5 0.749904 0.749904 0.250096 0.749904 7.75 0.749904 0.749904 0.250096 0.749904 8 0.749295 0.749295 0.250705 0.749295 8.25 0.740868 0.740868 0.259132 0.740868 8.5 0.723005 0.723005 0.276995 0.723005 8.75 0.700135 0.700135 0.299865 0.700135 9 0.681751 0.681751 0.318249 0.681751 9.25 0.661245 0.661245 0.338755 0.661245 9.5 0.668294 0.668294 0.331706 0.668294 9.75 0.681992 0.681992 0.318008 0.681992 10 0.698413 0.698413 0.301587 0.698413 10.25 0.715876 0.715876 0.284124 0.715876 10.5 0.732697 0.732697 0.267303 0.732697 10.75 0.739987 0.739987 0.260013 0.739987 11 0.743912 0.743912 0.256088 0.743912 11.25 0.746996 0.746996 0.253004 0.746996 11.5 0.747116 0.747116 0.252884 0.747116 11.75 0.747981 0.747981 0.252019 0.747981 12 0.749904 0.749904 0.250096 0.749904

Pm

Pme

Pvalue

RecPm

Transform Tuner

0.0001 0.0001 0.0025 0.013125 0.033 0.056833 0.0415 0.002367 0.001267 0.000725 0.000458 0.0003 0.0002 0.000146 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.000733 0.0095 0.028083 0.051875 0.071 0.092333 0.085 0.07075 0.053667 0.0355 0.018 0.010417 0.006333 0.003125 0.003 0.0021 0.0001

0.020096 0.020096 0.022403 0.032616 0.051721 0.074631 0.059892 0.022275 0.021218 0.020697 0.020441 0.020288 0.020192 0.02014 0.020096 0.020096 0.020096 0.020096 0.020096 0.020096 0.020096 0.020096 0.020096 0.020096 0.020096 0.020096 0.020096 0.020096 0.020096 0.020096 0.020096 0.020096 0.020705 0.029132 0.046995 0.069865 0.088249 0.108755 0.101706 0.088008 0.071587 0.054124 0.037303 0.030013 0.026088 0.023004 0.022884 0.022019 0.020096

0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75

0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02

-9.61E-05 -9.61E-05 -0.0024 -0.01262 -0.03172 -0.05463 -0.03989 -0.00227 -0.00122 -0.0007 -0.00044 -0.00029 -0.00019 -1.40E-04 -9.61E-05 -9.61E-05 -9.61E-05 -9.61E-05 -9.61E-05 -9.61E-05 -9.61E-05 -9.61E-05 -9.61E-05 -9.61E-05 -9.61E-05 -9.61E-05 -9.61E-05 -9.61E-05 -9.61E-05 -9.61E-05 -9.61E-05 -9.61E-05 -0.0007 -0.00913 -0.027 -0.04986 -0.06825 -0.08876 -0.08171 -0.06801 -0.05159 -0.03412 -0.0173 -0.01001 -0.00609 -0.003 -0.00288 -2.02E-03 -9.61E-05

0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125

187


Run 8 – The average of Run 7 ‘NEW LCA Pvalue’ as input to this run - (0.26) Time Limiter NEW NEW NewP Pm Pme LCA LCB Conversion Pvalue Pvalue 0 0.263556 0.263556 0.736444 0.263556 0.0001 0.020096 0.25 0.263556 0.263556 0.736444 0.263556 0.0001 0.020096 0.5 0.261249 0.261249 0.738751 0.261249 0.0025 0.022403 0.75 0.251036 0.251036 0.748964 0.251036 0.013125 0.032616 1 0.231931 0.231931 0.768069 0.231931 0.033 0.051721 1.25 0.209021 0.209021 0.790979 0.209021 0.056833 0.074631 1.5 0.223761 0.223761 0.776239 0.223761 0.0415 0.059892 1.75 0.261377 0.261377 0.738623 0.261377 0.002367 0.022275 2 0.262435 0.262435 0.737565 0.262435 0.001267 0.021218 2.25 0.262955 0.262955 0.737045 0.262955 0.000725 0.020697 2.5 0.263212 0.263212 0.736788 0.263212 0.000458 0.020441 2.75 0.263364 0.263364 0.736636 0.263364 0.0003 0.020288 3 0.26346 0.26346 0.73654 0.26346 0.0002 0.020192 3.25 0.263512 0.263512 0.736488 0.263512 0.000146 0.02014 3.5 0.263556 0.263556 0.736444 0.263556 0.0001 0.020096 3.75 0.263556 0.263556 0.736444 0.263556 0.0001 0.020096 4 0.263556 0.263556 0.736444 0.263556 0.0001 0.020096 4.25 0.263556 0.263556 0.736444 0.263556 0.0001 0.020096 4.5 0.263556 0.263556 0.736444 0.263556 0.0001 0.020096 4.75 0.263556 0.263556 0.736444 0.263556 0.0001 0.020096 5 0.263556 0.263556 0.736444 0.263556 0.0001 0.020096 5.25 0.263556 0.263556 0.736444 0.263556 0.0001 0.020096 5.5 0.263556 0.263556 0.736444 0.263556 0.0001 0.020096 5.75 0.263556 0.263556 0.736444 0.263556 0.0001 0.020096 6 0.263556 0.263556 0.736444 0.263556 0.0001 0.020096 6.25 0.263556 0.263556 0.736444 0.263556 0.0001 0.020096 6.5 0.263556 0.263556 0.736444 0.263556 0.0001 0.020096 6.75 0.263556 0.263556 0.736444 0.263556 0.0001 0.020096 7 0.263556 0.263556 0.736444 0.263556 0.0001 0.020096 7.25 0.263556 0.263556 0.736444 0.263556 0.0001 0.020096 7.5 0.263556 0.263556 0.736444 0.263556 0.0001 0.020096 7.75 0.263556 0.263556 0.736444 0.263556 0.0001 0.020096 8 0.262947 0.262947 0.737053 0.262947 0.000733 0.020705 8.25 0.254521 0.254521 0.745479 0.254521 0.0095 0.029132 8.5 0.236657 0.236657 0.763343 0.236657 0.028083 0.046995 8.75 0.213788 0.213788 0.786212 0.213788 0.051875 0.069865 9 0.195404 0.195404 0.804596 0.195404 0.071 0.088249 9.25 0.174897 0.174897 0.825103 0.174897 0.092333 0.108755 9.5 0.181946 0.181946 0.818054 0.181946 0.085 0.101706 9.75 0.195644 0.195644 0.804356 0.195644 0.07075 0.088008 10 0.212065 0.212065 0.787935 0.212065 0.053667 0.071587 10.25 0.229528 0.229528 0.770472 0.229528 0.0355 0.054124 10.5 0.24635 0.24635 0.75365 0.24635 0.018 0.037303 10.75 0.253639 0.253639 0.746361 0.253639 0.010417 0.030013 11 0.257564 0.257564 0.742436 0.257564 0.006333 0.026088 11.25 0.260648 0.260648 0.739352 0.260648 0.003125 0.023004 11.5 0.260769 0.260769 0.739231 0.260769 0.003 0.022884 11.75 0.261634 0.261634 0.738366 0.261634 0.0021 0.022019 12 0.263556 0.263556 0.736444 0.263556 0.0001 0.020096

Pvalue

RecPm

Transform Tuner

0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652 0.263652

0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02

-9.61E-05 -9.61E-05 -0.0024 -0.01262 -0.03172 -0.05463 -0.03989 -0.00227 -0.00122 -0.0007 -0.00044 -0.00029 -0.00019 -1.40E-04 -9.61E-05 -9.61E-05 -9.61E-05 -9.61E-05 -9.61E-05 -9.61E-05 -9.61E-05 -9.61E-05 -9.61E-05 -9.61E-05 -9.61E-05 -9.61E-05 -9.61E-05 -9.61E-05 -9.61E-05 -9.61E-05 -9.61E-05 -9.61E-05 -0.0007 -0.00913 -0.027 -0.04986 -0.06825 -0.08876 -0.08171 -0.06801 -0.05159 -0.03412 -0.0173 -0.01001 -0.00609 -0.003 -0.00288 -2.02E-03 -9.61E-05

0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125 0.96125

188

Ryan, J.G. (2007) PhD Thesis – Appendix B

APPENDIX B: SUMMARY OF HYDROLOGICAL SIMULATION MODEL PARAMETER ESTIMATES

Run No.

Infiltration mm/s (m/s)

Infiltration Run-off Depth to Saturation Field (mm/hr) threshold water-table capacity capacity (WT)

Manning’s Comment M

n/a

n/a

n/a

n+1 2

n/a

initial parameters – erratic

n/a

n/a

n/a

n/a

n/a

0.00000090 0.00324 3.24

n/a

n/a

n/a

n/a

n/a

0.00000400 0.0144 14.4

0.0001

900 mm

n/a

n/a

n/a

3

0.00000600 0.0216 21.6

0.0001

1200 mm

0.16

0.15 field

15/20/30

4

0.00000800 0.0288 28.8

0.0001

1500 mm

0.16 sat

0.15 field

15/20/30

5

0.00000972 0.035

35

0.0001

1500 mm

0.16 sat

0.15 field

15/20/30

6

0.00000611 0.022

22

0.0001

1200 mm

0.15 sat

0.14 field

15/20/30

7

0.00000417 0.015

15

0.0001

900 mm

0.15 sat

0.14 field

15/20/30

8

0.00000417 0.015

15

0.0001

600 mm

0.15 sat

0.14 field

15/20/30

9

0.00000417 0.015

15

0.0001

500 mm

0.14 sat

0.13 field

15/20/30

10

0.00000611 0.022

22

0.0001

500 mm

0.20 sat

0.16 field

15/20/30

11

0.00000556 0.02

20

0.0001

500 mm

0.14 sat

0.13 field

15/20/30

12

0.00000417 0.015

15

0.0001

500 mm

0.14 sat

0.13 field

15/20/30

13

0.00000347 0.0125 12.5

0.0001

450 mm

0.13 sat

0.12 field

15/20/30

14

0.00000417 0.015

15

0.0001

300 mm

0.14 sat

0.13 field

15/20/30

ok low flow

15

0.00000556 0.02

20

0.0001

300 mm

0.14 sat

0.13 field

15/20/30

ok low flow

16

0.00000389 0.014

14

0.0001

10 mm

0.14 sat

0.13 field

15/20/30

ok low flow

17

0.00000611 0.022

22

0.0001

0 mm

0.16 sat

0.14 field

15/20/30

high flow peak/ fair flow time

18

0.00000611 0.022

22

0.0001

300 mm

0.16 sat

0.14 field

15/20/30

low flow peak/fast flow time

19

0.00000472 0.017

17

0.0001

150 mm

0.14 sat

0.12 field

15/20/30

low flow peak/fast flow time

20

0.00000833 0.03

30

0.0001

0.001 mm 0.20 sat

0.16 field

15/20/30

very low flow at all times

21

0.00000833 0.03

30

0.0001

0.001 mm 0.10 sat

0.095 field 15/20/30

very low flow at all times

22

0.00000778 0.028

28

0.0001

0 mm

0.18 sat

0.16 field

15/20/30

fair flow peak/fair flow time

23

0.00001000 0.036

36

0.0001

0 mm

0.18 sat

0.16 field

15/20/30

fair flow peak/long flow time

Test1 0.00001000 0.036

36

0.001

0 mm

0.18 sat

0.16 field

15/20/30

fair flow peak/long flow time

25

0.00000611 0.022

22

0.001

450 mm

0.16 sat

0.14 field

20/30/40

low flow peak/ short flow time

26

0.00000347 0.0125 12.5

0.001

450 mm

0.18 sat

0.16 field

20/30/40

fair flow peak/ fair flow time

27

0.00000347 0.0125 12.5

0.001

450 mm

0.16 sat

0.14 field

20/30/40


28

0.00000347 0.0125 12.5

0.001

450 mm

0.15 sat

0.14 field

20/30/45


Test2 0.00000347 0.0125 12.5

0.0001

300 mm

0.18 sat

0.16 field

20/30/45


30

0.00000347 0.0125 12.5

0.0001

150 mm

0.12 sat

0.11 field

20/30/45


31

0.00001000 0.036

0.0001

0 mm

0.18 sat

0.16 field

20/30/45


32

0.00000347 0.0125 12.5

0.0001

150 mm

0.12 sat

0.11 field

20/30/45


36

Test3 0.00000278 0.01

10

0.0001

150 mm

0.13 sat

0.12 field

15/20/30


Test4 0.00000278 0.01

10

0.0001

150 mm

0.13 sat

0.12 field

x/30/45


189

Ryan, J.G. (2007) PhD Thesis – Appendix C

APPENDIX C. AN EXAMPLE OF THE TYPE OF THUNDERSTORM SIMULATED FOR THE MARONGHI CREEK CATCHMENT

Source: ‘Weatherzone’ website, Saturday 16th December 2006. http://www.weatherzone.com.au/

190