Healthy Waterways Management Strategy Evaluation: Scoping Study for the Development of a 'catchment-tocoast' MSE in SE Queensland - Phase 2 – Final Report Final Report: December 2010 Wealth from Oceans (WfO) Project team Leo Dutra (PI), William de la Mare, Pascal Perez, Nick Ellis, Sharon Tickell, Toni Cannard, Olivier Thebaud, Ricardo Pascual, Peter Bayliss, Chris Moeseneder, Cathy Dichmont, Fabio Boschetti and Sean Pascoe
Final Report 20 September 2010
Healthy Waterways Ltd [Insert client contact (delete if not required)]
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Dr Eva Abal (Healthy Waterways)
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Dr Mara Wolkenhauer (Healthy Waterways)
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Dr Andy Steven (CSIRO Wealth from Oceans Flagship)
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Dr Wendy Proctor (CSIRO Wealth from Oceans Flagship)
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Contents Acknowledgements ................................................................................... 15
Abbreviations ............................................................................................ 17
Executive Summary ................................................................................... 19
Preface ..................................................................................................... 21
Objectives of the report .................................................................................................... 21
Use and limitation of the MSE Tool (software) ................................................................. 21
Organisation of the report................................................................................................. 21
1.
Need for management strategy evaluation in South East Queensland .. 23
1.1
Setting ..................................................................................................................... 23
1.1.1
Water quality issues ................................................................................................. 23
1.1.2
Healthy Waterways .................................................................................................. 25
1.1.3
Adaptive management ............................................................................................. 25
1.1.4
Monitoring water quality ........................................................................................... 26
1.1.5
Linking catchment management to water quality ..................................................... 27
1.2
Management strategy evaluation (MSE)................................................................. 27
1.2.1
Developing a Catchment-to-coast management strategy evaluation (C2C MSE) ... 28
1.2.2
Complementing the C2C MSE concept and functionalities...................................... 30
1.2.3
The conceptual model of the catchment-to-coast MSE Tool version 2 .................... 31
1.3
The collaborative approach for the C2C MSE......................................................... 32
1.3.1
Vision document....................................................................................................... 33
1.3.2
Interviews ................................................................................................................. 34
1.3.3
Workshop ................................................................................................................. 37
1.3.4
Towards a non-interactive evaluation of decisions (closed-loop MSE) .................... 38
1.3.5
Implications of interviews and workshop for the conceptual model of the catchmentto-coast MSE Tool ............................................................................................... 39
1.4
Chapter conclusions................................................................................................ 39
2.
Biophysical Catchment-to-Coast Models ............................................. 41
2.1
Upper catchments ................................................................................................... 41
2.1.1
Methods ................................................................................................................... 42
2.1.2
Results ..................................................................................................................... 43
2.1.3
Implications for the MSE simulation ......................................................................... 47
2.2
Estuarine empirical biophysical model .................................................................... 47
2.3
Moreton Bay empirical biophysical model............................................................... 50
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3.
Management Model ............................................................................. 55
3.1
Nominal Abatement Coefficient (NAC) and Actual Abatement Coefficients (AAC) .56
3.2
Scoping the management actions used in the MSE Tool........................................58
3.2.1
Rural management Actions ......................................................................................58
3.2.2
Urban management actions .....................................................................................58
4.
Triple Bottom Line Assessment ........................................................... 63
4.1
Report Card Model ..................................................................................................63
4.1.1
Freshwater report card .............................................................................................63
4.1.2
Estuarine and marine waterways report card ...........................................................64
4.2
Social perception model ..........................................................................................68
4.2.1
Approach ..................................................................................................................68
4.2.2
Calculating the social perception index ....................................................................70
4.2.3
Conclusions ..............................................................................................................71
4.3
Economic model component ...................................................................................71
4.3.1
Management interventions and associated costs.....................................................72
4.3.2
Defining effectiveness ..............................................................................................73
5.
Trial MSE simulation results for Brisbane, Logan and Albert catchments ......................................................................................................... 77
5.1
Scoping the catchment management objectives .....................................................77
5.2
Evaluating catchment management strategies .......................................................77
5.2.1
Report card grades ...................................................................................................78
5.2.2
Social perception ......................................................................................................82
5.2.3
Benefits per household and cost effectiveness ........................................................84
5.3
Chapter summary ....................................................................................................86
6.
Overall Discussion and Conclusions ................................................... 87
6.1
Overarching catchment-to-coast MSE framework...................................................87
6.2
Enabling Tool for catchment-to-coast MSE .............................................................88
6.3
Evaluating strategies with the catchment-to-coast MSE Tool .................................89
6.4
Expert opinion to inform the management model....................................................89
6.5
Other applications of the MSE Tool.........................................................................90
6.5.1
Hybridisation of process-based and statistical models .............................................90
6.5.2
Using the MSE Tool to design monitoring programs ................................................91
6.5.3
Visualisation..............................................................................................................91
References ................................................................................................ 93
Appendix 1 – Enabling Tool for Management Strategy Evaluation .............. 99
Introduction .......................................................................................................................99
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Technical aspects of the MSE Tool ................................................................................ 100
Software framework architecture.................................................................................... 100
Architecture layers .............................................................................................................. 101
Domain model components ................................................................................................ 101
Model composition wrappers .............................................................................................. 102
MSE configuration file ......................................................................................................... 103
Module service .................................................................................................................... 103
MSE project ........................................................................................................................ 103
Model execution engine ...................................................................................................... 103
Results file manager ........................................................................................................... 104
Strategy record files ............................................................................................................ 104
Replicate results file ............................................................................................................ 104
Windows forms user interface............................................................................................. 105
Domain model components for the SEQ catchment-to-coast MSE ............................... 105
Appendix 2 – Prior information sent to participants in the pressureresponse interviews held in August 2010 ......................................... 108
Appendix 3 – 2009 Ecosystem Health Monitoring Program (EHMP) Report Card Methods .................................................................................. 111
Appendix 4 - Tutorial of the MSE Tool ..................................................... 113
Configuration of the C2CMSE ........................................................................................ 113
Import MSE Configuration................................................................................................... 113
Configuring scenarios ......................................................................................................... 113
Configuring the Strategy ..................................................................................................... 114
Management implementation ......................................................................................... 116
Freshwater, estuarine and Moreton Bay biophysical responses .................................... 116
Comparing report card grades........................................................................................ 118
Social perception assessment........................................................................................ 118
Economics assessment.................................................................................................. 119
Visualising previously recorded strategies ..................................................................... 123
Visualising results of previously recorded strategies...................................................... 124
Appendix 5 – Glossary of terms ............................................................... 125
Social perception model terms ....................................................................................... 125
Social group ........................................................................................................................ 125
Social aspects/values ......................................................................................................... 126
Ecosystem services ............................................................................................................ 126
Economic model terms ................................................................................................... 127
Management action terms .............................................................................................. 128
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Biophysical model terms.................................................................................................129
Model terms ....................................................................................................................132
Glossary Sources ...........................................................................................................132
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List of Figures Figure 1. Map of South East Queensland catchments. .............................................................. 24
Figure 2. The Adaptive Management Cycle (© South East Queensland Healthy Waterways Partnership). ........................................................................................................................ 26
Figure 3. Traditional MSE framework based on Kell et al. (2007) and Dichmont et al. (2006)... 28
Figure 4. Conceptualisation of the C2C MSE approach (from on Pantus et al., 2008) (© South East Queensland Healthy Waterways Partnership). ........................................................... 29
Figure 5. The Catchment-to-coast Management Strategy Evaluation approach whereby the MSE Tool is used to mediate interaction between the project team and stakeholders (based on Dutra et al. (in press))..................................................................................................... 31
Figure 6. Conceptual framework of the MSE Tool...................................................................... 32
Figure 7. Generic model of decisional processes based on in-depth interviews content analysis. ............................................................................................................................................. 35
Figure 8. Synthesis of methods used in the C2CMSE................................................................ 39
Figure 9. Representation of the upper catchment model............................................................ 41
Figure 10. Partial effect plots from a random forest model fitting hst to Year, Season, Us, Rs and Ms. The ticks along the top show the deciles of the distribution (for Us and Rs the first few deciles are at 0%)................................................................................................................ 44
Figure 11. Level of accuracy of random forest model for the upper catchments........................ 46
Figure 12. Activity diagram for Bay Water Quality Prediction Component. ................................ 51
Figure 13. Performance of the random forest model at 25 sites in the bay for Total Nitrogen. Each horizontal panel represents a different site with site IDs in the brown strips on the left. Closed blue circles represent actual observations and open red circles represent predicted observations. This version does not contain the tide and wind predictors. ......................... 52
Figure 14. An example on how management interventions affect the biophysical models used in the MSE-system. Dotted line indicates the point in which the management intervention was done. Dashed line shows the limit between observations and simulation........................... 53
Figure 15. Summary of biophysical models................................................................................ 54
Figure 16. The management model of the MSE Tool is applied over the stream network. Brown, green and yellow squares indicate urban, forest and rural land-use. Red squares indicate that a management intervention was implemented, affecting the nearest downstream EHMP station (green circles)............................................................................................... 56
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Figure 17. Perceptions (median) and maximum and minimum scores of Water Clarity value, from two different social groups deteriorate when Report Card scores shift from A to F. (A) long-term urban residents and (B) Business – Tourism/Recreational related. ....................71
Figure 18. Freshwater report card grades for Strategies A, B and C for the Mid Brisbane catchment. ...........................................................................................................................79
Figure 19. Estuarine report card grades for Strategies A, B and C for the Logan estuary. ........80
Figure 20. Estuarine report card grades for Strategies A, B and C for the Albert estuary. .........80
Figure 21. Estuarine report card grades for Strategies A, B and C for the Brisbane estuary. ....81
Figure 22. Marine report card grades for Strategies A, B and C for Bramble Bay......................81
Figure 23. User management history by action for managed regions (Best Farming Practices). .............................................................................................................................................82
Figure 24. Average of social perception index for each strategy for all regions. ........................83
Figure 25. Average of social perception index for each strategy for Brisbane estuary...............83
Figure 26. Cumulative total benefits ($) due to water quality improvements for all managed regions. ................................................................................................................................84
Figure 27. Cost effectiveness of strategy A in the Logan, Albert and Brisbane estuaries. Negative values means the strategy is not effective............................................................85
Figure 28. Cost effectiveness of strategy B in the Logan, Albert and Brisbane estuaries. Negative values means the strategy is not effective............................................................85
Figure 29. Cost effectiveness of strategy C in the Logan, Albert and Brisbane estuaries. Negative values means the strategy is not effective............................................................86
Figure 30. Software framework architecture diagram of the MSE Tool. ...................................101
Figure 31. Domain model representing how the software models from South East Queensland represented in the MSE Tool are configured, and how they are linked to each other. ......107
Figure 32. MSE-SEQ management model is applied over the stream network. Brown, green and yellow squares indicate urban, forest and rural land-use. Red square indicates a management intervention was implemented, affecting the nearest EHMP station (green circles) downstream...........................................................................................................109
Figure 33. Manager window......................................................................................................113
Figure 34. Interactive open-loop MSE window. ........................................................................114
Figure 35. Interactive open-loop MSE initial window where the user can select and implement management actions in the region.....................................................................................115
Figure 36. Interactive open-loop MSE initial window where the user can visualise costs associated with each management action with break-down capital and operating. ..........116
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Figure 37. Results display of the management implementation option. ................................... 117
Figure 38. Results display of the biophysical response model. ................................................ 117
Figure 39. Results display of the report card assessment model. ............................................ 118
Figure 40. Results display of the social perception model. The black vertical line in middle of each bar is the median value of all respondents, the top whisker is the maximum value of all panel members and the bottom whisker is the minimum of all panel members. .......... 119
Figure 41. Results display for “Management Costs” in the economic model tab. .................... 120
Figure 42. Results display for “Management Costs” in the economic model showing cumulative management costs and management cost details. ........................................................... 121
Figure 43. Results display for “Benefits” in the economic model tab showing percentage water quality improvements by region and benefit per household due to water quality improvement by region. ..................................................................................................... 121
Figure 44. Results display for “Benefits” in the economic model tab showing cumulative benefits per households in selected management regions and cumulative total benefits to households in all regions. .................................................................................................. 122
Figure 45. Results display for “Cost Effectiveness” in the economic model tab. ...................... 122
Figure 46. User management history by actions for all selected regions. ................................ 123
Figure 47. Detailed management actions for previously recorded strategies........................... 124
Figure 48. Summary results of previously recorded strategies................................................. 124
List of Tables Table 1. Key messages from interviews with stakeholders on pressure-response issues with comments from the project team. ........................................................................................ 36
Table 2. Estimates and standard errors of fixed-effect coefficients Cc, aU and aM, and estimates of σs and σst, after fitting the mixed-effect model using REML. ........................................... 45
Table 3. The quantity αa is the effectiveness of the action α per unit cell. It is the area of riparian revegetation that is equivalent to 1 unit of action. ............................................................... 47
Table 4. Management actions, Nominal Abatement Coefficients (NAC), time lags, costs and references used to calculate costs and NAC for the “literature-based configuration” project. ............................................................................................................................................. 60
Table 5. Management actions, Nominal Abatement Coefficients (NAC), time lags, costs and references used to calculate costs and NAC for “expert-based configuration” project. ...... 61
Table 6. Meaning of Report Card Grades................................................................................... 64
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Table 7. Compliance thresholds for water quality in Estuaries ...................................................65
Table 8. Compliance thresholds for marine reporting regions. ...................................................65
Table 9. Estuary thresholds to generate report card scores. ......................................................67
Table 10. Bay thresholds to generate report card scores ...........................................................68
Table 11. Environmental goals for freshwater, estuarine and marine systems in the Ecosystem Health Report Card grade system, report card grades and the description of each of the grades..................................................................................................................................69
Table 12. Social value indicators considered by the expert panel in the scoring exercise ........70
Table 13. Social groups defined by the expert panel – each column lists a different category ..70
Table 14. The meanings attributed to each of the Ecosystem Health report card grades (left) and assumed changes in overall water quality associated with improvements in the scores as calculated by the report card system (right)....................................................................74
Table 15. Specification of the budget (in “virtual” million dollars) for each management strategy tested in the MSE Tool. .......................................................................................................78
Table 16. The development environment specification used in the MSE Tool. ........................100
Table 17. Nominal Abatement Coefficients (NAC) influencing TN and TB and implementation lag for management interventions that were used in the MSE Tool. .................................110
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ACKNOWLEDGEMENTS We would like to thank Dr Eva Abal (Healthy Waterways), Dr Mara Wolkenhauer (Healthy Waterways), Ms Di Tarte (Healthy Waterways), Mr John Bennett (DERM), Dr Andy Steven (WfO/ORCA Theme Leader) and Dr Wendy Proctor (WfO/Coastal Futures Stream Leader) for their support throughout the period of the project. We thank the following people for providing valuable information to construct the MSE Tool through their participation in interviews and/or attendance to workshops (in alphabetical order of institutions): Dr Toni Weber (BMT/WBM). Ms Fiona Chandler, Ms Julie McLellan, Mr Kieron Beardmore, Mr John Rush and Mr Paul Belz (Brisbane City Council). Dr Ana Norman, Dr Eva Plaganyi-Lloyd, Dr John Parslow, Dr Trevor Hutton, Dr Wayne Rochsester, Dr Hector Lozano Montes, Dr Jim Greenwood, Dr Murni Greenhill and Dr Peta Dzidic (CSIRO). Ms Annie Lalancette (Concordia University, Canada/CSIRO) David Gray (Department of Agriculture and Food, Western Australia). Dr Ian Ramsey, Mr John Bennett and Mr Paul McDonald (DERM). Assoc. Prof. Francis Pantus, Prof. Jon Olley and Dr Fran Sheldon (Griffith University). Dr Eva Abal, Ms Di Tarte, Dr Mara Wolkenhauer, Mr Piet Fillet and Ms Anne Simmi (Healthy Waterways). Dr Anne Dray (HEMA Consulting/CSIRO). Dr Mark Pascoe (International Water Centre). Cr Victor Atwood and Mr Gary Ellis (Ipswich City Council). Mr Donald J Mackenzie (Logan City Council). Mr Peter Loose (Moreton Bay Regional Council). Mr Ben McMullen, Mr Michael Asnicar, Mr Mick Smith, Mr Stephen Skull and Mr Graham Webb (Sunshine Coast Regional Council). Dr Helen Ross (University of Queensland). Dr Laura Stocker (University of Western Australia). Dr John Parslow also provided relevant input to the research team in relation to future directions to link empirical with process-oriented catchment and the receiving waters models, provided on Chapter 6. Finally, we thank Dr Manuela Taboada (Queensland University of Technology) for assistance with graphic design of report cover and figures 4 and 5.
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ABBREVIATIONS AAC – Actual Abatement Coefficient AM – Adaptive Management BWQM – Bay Water Quality Model Chl a – Chlorophyll a C2C – Catchment-to-Coast EHMP – Ecosystem Health Monitoring Program IL – Implementation Lag MA – Management Action MS – Monitoring Station MSE – Management Strategy Evaluation MSE Tool – Catchment-to-Coast Management Strategy Evaluation software NAC – Nominal Abatement Coefficient SEQ – South East Queensland TB – Turbidity TBL – Triple Bottom Line TN – Total Nitrogen TP – Total Phosphorous WfO – Wealth from Oceans Flagship
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EXECUTIVE SUMMARY South East Queensland (SEQ) is the fastest growing population in Australia. With the projected population growth from nearly 3 million in 2010 to approximately 4 million in 2026, changes in land-use (e.g. from rural to urban) and construction of additional infrastructure are inevitable. More people in the region will also increase demands for water, sewage treatment plants and recreational areas in SEQ waterways. If not properly managed, these changes are expected to have profound consequences on environmental, social and economic values in the region. This setting highlights the need for an overarching integrated approach to improve decisionmaking and provide decision-makers with the means to assess alternative outcomes of their decisions on indicators of environmental, social and economic “health”. Management Strategy Evaluation (MSE) is an approach that can be used to support decision-making processes by assessing alternative management regimes and making explicit the inevitable trade-offs that managers need to make. MSE was originally designed to assess alternative fisheries management approaches (Butterworth and Punt 1999; de la Mare 1996; Smith 1994). It has been tailored to the catchment-to-coast (C2C) conditions of SEQ with a focus on the design of management strategies to improve and maintain the health of waterways, including Moreton Bay and coastal receiving waters. The Catchment-to-Coast MSE approach is defined by the following three elements: (i) the project team; (ii) the stakeholders; and (iii) a computer simulation tool to model different components of the natural resource management system, such as ecosystem and human responses, monitoring and observation, assessment, management decisions and actions, and associated learning. We refer to the computer simulation tool as the MSE Tool. The project team was represented by the Wealth from Oceans Flagship team, responsible for assisting with stakeholder engagement, knowledge integration and development of the MSE Tool. Stakeholders actively contribute to the design and development of the Tool, which is used to test management strategies and to mediate also the interaction between project team and stakeholders. The interaction between the stakeholders and the project team also helps to manage stakeholder’s expectations for the MSE tool to realistic levels. The catchment-t0-Coast CMSE approach was implemented in the following four stages: • Establish a living Vision Document that contains high-level specifications of the MSE Tool in terms of the needs of the end users and stakeholders. • Interview stakeholders to learn about their current decision-making processes and to elicit what information they need in order to make their decisions (these are included in the MSE Tool). • Develop the MSE Tool for use by stakeholders. • Undertake workshops whereby stakeholders use the tool to test alternative management strategies. The project team used information on how stakeholders interacted with the Tool to improve it. The above participatory approach permits stakeholders to therefore make significant contributions to the design of the MSE Tool, and simultaneously allows the project team to discover what information managers consider relevant when making decisions, what issues are relevant to water quality management in SEQ, and how they want to use the tool. By using inputs from prospective users in its development, the tool value-adds to their existing
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knowledge and expertise because it enables them to rapidly develop and evaluate local and/or regional water quality management plans. In order to enable stakeholder participation we developed software tool that links all models necessary for a C2C MSE (e.g. biophysical, social, economic, decision-making). A catchmentto-coast biophysical model was also developed, where managers are able to test the effects of their actions on waterway health. The empirical biophysical model of the MSE Tool uses empirical data from 253 estuarine and marine monitoring sites of the ecosystem health monitoring program (EHMP) network. The data captures relationships between variables within and between sites on monthly intervals for up to 10 years. Empirical relationships are used in the biophysical model to simulate future stochastic observations and to propagate the effects of local interventions along the waterway network. A management model of the MSE Tool, which permits users to implement catchment management actions over 1 km2 grid cells overlapping the hydrological network. The user can select a range of rural and urban management options that affect water quality at EHMP stations. Each management action applied in each cell produces a pressure-response relationship that changes water quality at its nearest EHMP station. The MSE Tool uses a triple bottom line (TBL) approach, which serves to highlight the very different concerns involved with the environmental, social and economic aspects of SEQ (Davies 2004: 298). The indicators used in the tool are the following: • The report card model, which simulates report card grades for freshwater, estuarine and marine regions of SEQ. It uses outputs form the biophysical model, and generates scores ranging from A (excellent) to F (fail). • The social perception model, which uses outputs from the report card to calculate social perception scores of 22 groups in SEQ. • The economic model, which consists of three sub-models: • Costs of management actions, which calculates estimated costs of installation and ongoing yearly maintenance for selected management actions using a 1 km2 grid. • Economic benefits model, which calculate the economic benefit associated to improvements in water quality. • Cost effectiveness, which calculates costs per improvements in water quality. The TBL approach enables users to evaluate trade-offs of different management strategies implemented at local and regional scales. The C2CMSE approach focuses on management outcomes by implementing measures of progress against performance criteria, helping managers learn about the managed system in order to achieve their objectives. In summary, the C2CMSE approach helps managers with the following: • Allows them to make and learn from mistakes quickly and without adverse consequences. • Check whether or not objectives are achievable and when the system might get there. • Improve collaboration with other agencies, stakeholders and scientists. • Make trade-offs between multiple management objectives and options. • Design monitoring strategies and evaluate assessment methods. That is, what to measure, how often and where. • Identify critical knowledge gaps. • Design around structural uncertainty by using different model structures when evaluating management strategies.
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PREFACE The Healthy Waterways Inc (henceforth Healthy Waterways) and the CSIRO Wealth from Oceans Flagship (WfO) jointly developed the catchment-to-coast (C2C) Management Strategy Evaluation (MSE) framework. The framework adopts an approach that uses computermodelling simulation to help resource managers in the exploration of future scenarios related to the impact of land-use change on the health of SEQ waterways, including Moreton Bay. It is important to differentiate the MSE approach from the supporting tool used in MSE. The overarching C2CMSE approach includes the methods used to engage with stakeholders with the aim of informing the modelling tool used to evaluate C2C management strategies. Hence, in the remainder of the report we refer to the MSE approach as applied to catchments as the C2CMSE, and the simulation modelling tool as the MSE Tool. Both approach and Tool evolved from the pilot study delivered in 2008 (Pantus et al. 2008), which aimed to demonstrate the approach in the Logan-Albert catchment. The scope of the report is the application of the catchment-to-coast MSE approach to SEQ. This document constitutes the final report for the Project Healthy Waterways Management Scenario Evaluation: Scoping Study for the Development of a ‘catchment-to-coast’ MSE in SE Queensland - Phase 2.
Objectives of the report The report comprises Milestone 5 in the revised deliverable document (de la Mare, 2009). The objectives of the report are to describe the MSE Tool and its component models, and to provide a practical demonstration of the use of the Tool to improve water quality in the Logan, Albert, and Brisbane catchments. The specific report outputs are: • Delivery of MSE-software release 2.0, including improvements in usability for a wide range of stakeholders and reflecting the HWP “look and feel” (attached CD-ROM). • Description of critical biophysical and socio-economic models and decision processes developed in collaboration with the Partnership during the Learning and DecisionMaking project represented in the MSE. • Reporting the preliminary MSE results for the Logan, Albert and Brisbane catchments.
Use and limitation of the MSE Tool (software) The MSE Tool is a proof-of-concept tool for the Healthy Waterways only and was not intended for dissemination or for commercial or predictive purposes. Therefore, the MSE Tool Version 2 is to be used only for Healthy Waterways in-house demonstration purposes. Simulated results can be used in public seminars, to the exclusion of any commercial exploitation of the results. The Healthy Waterways does not intend to modify or improve the MSE Tool v2.0 software. Future improvements and modifications will be part of the development of the MSE Tool v3.0.
Organisation of the report The report provides the overarching foundations for the catchment-to-coast MSE with practical results of its implementation in SEQ. The report has the following format: Chapter 1: Need for management strategy evaluation in South East Queensland. This chapter highlights the need for the C2CMSE approach in SEQ and describes water quality issues in the
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region. The concepts of MSE and the adjustments necessary to its application in the C2C domain are explored. Chapter 2: Biophysical catchment-to-coast models. This chapter describes in detail how the upper catchments (freshwater), estuarine and marine models were constructed. Chapter 3: Management model. This chapter focuses on the management model used superimposed to the biophysical model. The management model contains a set of management actions that affect the biophysical model with flow on effects in local and regional water quality. Chapter 4: Triple bottom line assessment. This chapter includes a description of the report card, and the social perception and economic models used for integrated assessment in the MSE Tool. Chapter 5: Trial simulation results for Brisbane, Logan and Albert catchments. This chapter provides the initial results of applying management strategies in these catchments. Preliminary coarse results of illustrative strategies on environmental, social and economic indicators used in the MSE Tool are provided. Chapter 6: Overall discussion and conclusions. This chapter provides key conclusion and recommendations for future research directions to strengthen the catchment-to-coast MSE approach and supporting MSE Tool.
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1.
NEED FOR MANAGEMENT STRATEGY EVALUATION IN SOUTH EAST QUEENSLAND
Australia is a coastal nation with more than 85 per cent of its population living within 50 kilometres of the coast. With projected population growth from about 22 million in 2010 to around 35 million by 2049, changes in land-use (e.g. from rural to urban) and construction of additional infrastructure (e.g. roads and dams) will have profound results on the way Australians live and interact with their surrounding natural environment. As the fastest growing region in Australia, South East Queensland (SEQ) represents the challenges planners and resource managers will face in order to accommodate a growing population. With the projected population growth from nearly 3 million in 2010 to approximately 4 million in 2026, changes in land-use and construction of additional infrastructure are inevitable. More people in the region will also increase demands for water, sewage treatment plants (STPs) and recreational areas in SEQ waterways (Abal et al. 2005a: 20; South East Queensland Healthy Waterways Partnership 2002). If not properly managed, these changes are expected to have profound consequences to the environmental, social and economic well-being of our society. Resource managers and planners from SEQ should not only anticipate where to accommodate future developments, but they should also consider how water quality in rivers, estuaries and Moreton Bay will respond to these developments and what can be done to mitigate associated negative social and environmental impacts. The nature of the problem is not only biophysical. Often problems of natural resources management and planning, such as the ones affecting SEQ, rely upon elusive political judgement for resolution (Rittel and Webber 1973). Practical examples of the interconnection between biophysical and political issues in SEQ are outlined below: • Managers have to face multiple and often conflicting objectives, such as achieving environmental targets at the least cost without upsetting production interests (e.g. farmers). • There are multiple agencies with different yet often similar mandates. SEQ has 11 local councils interacting with several State government agencies, each responsible for managing different aspects of the catchments and coasts. • SEQ encompasses complex spatial and ecological interactions. For instance, Moreton Bay and its associated estuaries are high conservation value assets that are mutually dependant. This setting indicates that there is a need for an overarching approach to improve decisionmaking and provide decision-makers with the means to learn about and assess alternative outcomes of their decisions in environmental, social and economic indicators. Management Strategy Evaluation (MSE) is an approach that can be used to support the decision-making process by assessing alternative management regimes and presenting trade-offs managers need to make.
1.1
Setting
1.1.1
Water quality issues
SEQ covers an area of 22,420 km2, stretching 240 km from Noosa in the north to the Queensland-New South Wales border in the south, and 140 km west to Toowoomba (Department of Infrastructure and Planning, 2008) (Figure 1). The western catchments are
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the regions’ primary water supply, largely for urban use but also for agriculture (Bunn et al. 2010). The receiving waters of Moreton Bay and its associated estuaries are high conservation value assets and support significant fisheries. They are extensively used also for recreation and tourism (Abal et al. 2005a). Some of the habitats in SEQ are currently under pressure due to urban and industrial developments, which are likely to continue as a consequence of projected population growth. Only about one quarter of the original native vegetation in SEQ remain intact, and often much less along
stream and river corridors in some catchments. Increases in sediment and nutrient runoff is a result of extensive clearing in upper parts of the catchments, cultivation on the floodplains, over-grazing and local development of urban centres (Bunn et al. 2010; Olley et al. 2000). As a result, the hydrology of most SEQ catchments has been substantially altered, not only from interception by dams but also because of changes in land-use and vegetation that have resulted in altered runoff responses to rainfall events and ‘flashier’ stream flows (Bunn et al. 2010).
Figure 1. Map of South East Queensland catchments.
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1.1.2
Healthy Waterways
The Healthy Waterways (formerly Moreton Bay Waterways and Catchments Partnership and between 2002 and 2010, South East Queensland Healthy Waterways Partnership) is a special collaboration between government, industry, researchers and the community, who work together to ameliorate the condition of catchments and waterways including Moreton Bay (South East Queensland Healthy Waterways Partnership 2002). To ensure the implementation of a long-term strategy to improve conditions in the waterways, the following single clear vision for the future health of waterways in the region was developed with stakeholders (South East Queensland Healthy Waterways Partnership 2007a): “By 2026, our waterways and catchments will be healthy ecosystems supporting the livelihoods and lifestyles of people in South East Queensland, and will be managed through collaboration between community, government and industry.” In order to achieve this vision the Healthy Waterways developed a long-term strategy, which is an integrated set of activities coordinated in the region with a timeframe of 5 years (South East Queensland Healthy Waterways Partnership 2007a). The current Strategy covers the period between 2007 and 2012, and is part of a longer-term program to achieve the Healthy Waterway’s vision. The Strategy contains over 500 agreed committed actions to maintain and improve the health of waterways by: • Reducing urban and non-urban diffuse source pollution. • Protecting and conserving high ecological value waterways. • Decreasing point source pollution. • Improving catchment health. • Combating coastal algal blooms. • Increasing the commitment and capacity of the general community. • Improving management via better modelling and evaluation. • Refining the ecosystem health and event monitoring programs. The Strategy uses the adaptive management (AM) cycle framework, which is ideally suited to dealing with interlinked socio-ecological systems, which are typically very complex and difficult to understand, often leading to considerable uncertainty as to the way in which they will respond to management initiatives (South East Queensland Healthy Waterways Partnership 2007b).
1.1.3
Adaptive management
The AM cycle framework takes into account the uncertainties that arise in the face of management procedures, emphasising that managers learn about potential impacts through experience with management itself. Adaptive management focuses on continuous learning when making decisions (Figure 2). The approach is characterised as “ongoing monitoring and evaluation, leading to improvement in the identification and implementation of management actions” (as opposed to some kind of optimal solution). The approach not only leads to improved understanding of ways of dealing with resource management issues, it also provides the flexibility necessary for dealing with changing socio-economic or socio-ecological relationships (South East Queensland Healthy Waterways Partnership 2007b). In the cycle objectives, activities, monitoring protocols and evaluation procedures are established and then refined as new information is gathered in each part of the cycle (Lynam et al. 2002; Walters 1986).
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Figure 2. The Adaptive Management Cycle (© South East Queensland Healthy Waterways Partnership).
The framework emphasises that we need to understand ‘where we are’ and ‘where we want to be’ with regards to the system we want to manage (Figure 2). AM is the basis of the Healthy Waterways’ approach to understanding issues and implementing solutions to improve the health of waterways in SEQ. It is supported by a combination of the Ecosystem Health Monitoring Program (EHMP), decision support tools (e.g. the MSE Tool), the high-level scientific expertise, working groups and partner bodies, and the Healthy Waterways’ regional coordination and consultation mechanisms (South East Queensland Healthy Waterways Partnership 2007b).
1.1.4
Monitoring water quality
As part of the overarching adaptive approach to maintain the health of SEQ waterways, it is necessary to have an ongoing and up-to-date understanding of changes to the quality of, and the potential threats to, its health. The Ecosystem Health Monitoring Program (EHMP) assesses the ecosystem responses to both natural pressures and human activities. It is embedded within the Healthy Waterways’ adaptive management framework, which links monitoring to management (Figure 2). The EHMP is a comprehensive integrated aquatic monitoring program with a ‘catchment-tocoast’ philosophy. It was established in 2000 to assess the effectiveness of management and planning activities aimed at improving the health of SEQ waterways using a broad range of biological, physical and chemical indicators. The EHMP is widely recognised as one of the most comprehensive and successful aquatic monitoring programs in Australia (EHMP 2008). Freshwater waterways are monitored at 135 representative sites across SEQ. Five ecological indicators are used to assess the health of freshwater ecosystems: (1) Physical and Chemical; (2) Nutrient Cycling; (3) Ecosystem Processes; (4) Aquatic Macroinvertebrates; and (5) Fish (South East Queensland Healthy Waterways Partnership 2009: 26).
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The Estuarine and Marine Report Card Grade is calculated by combining an Ecosystem Health Index (EHI) and a Biological Health Rating (BHR) to produce a single value of ecosystem health. A total of 253 sites throughout SEQ’s 9 major estuaries and 9 marine areas are monitored on a monthly basis (South East Queensland Healthy Waterways Partnership 2009). Annual assessment of ecological health, based on the EHMP, are released by the Healthy Waterways in the form of a Report Card in which 18 major catchments, 18 estuaries and nine zones within Moreton Bay are graded from ‘A’ (excellent) to ‘F’ (fail) (EHMP 2008). The EHMP and associated report card system allows management bodies to readily evaluate and communicate the ecosystem and community benefits resulting from their investment in environmental protection. It provides both managers and researchers with the feedback required to better target investments to maintain or improve the health of waterways (EHMP 2008).
1.1.5
Linking catchment management to water quality
It is currently unknown exactly how much influence a particular management action applied in a particular part of the landscape has on water quality or river condition at a point downstream. It is unclear also how the combination of different spatial arrangements of management actions will affect the health of waterways in SEQ (Bunn et al. 2010), nor the time lag required for a management action to take effect such as riparian revegetation, or to be fully effective. However, the regions that contribute most to diffuse source pollution in SEQ is known, therefore allowing these regions to be targeted for investment (Abal et al. 2005b).
1.2
Management strategy evaluation (MSE)
The MSE approach was originally designed to assess alternative fisheries management regimes (de la Mare 1996; Punt et al. 2001; Sainsbury et al. 2000; Smith 1994), but has also been used in the coastal zone (McDonald et al. 2006; Pantus et al. 2008). MSE uses computer simulation models to assess the consequences of a range of management strategies, presenting the results as a set of the trade-offs in the performance measures of selected indicators across a range of management objectives (Smith 1994). The evaluation of different management strategies is made against user- and resource-orientated objectives set by managers that should take into account various stakeholder views and needs (Butterworth et al. 2010). A management strategy is characterised as a set of rules that transform the results of an assessment into management actions, thus closing the AM loop (Figure 2, Pantus et al. (2008)). MSE requires the representation of two systems to assess management strategies: the natural system and the management system (Figure 3). The natural system is represented by an operating model (OM), which essentially simulates the world to be managed. The OM represents plausible hypotheses about how the world works and is intended to test the robustness of management strategies given current knowledge and what we can and cannot control. The management system is represented by the management model, which includes an assessment model to derive estimates of performance measures from simulated observations. Outputs from the assessment model inform the management model about actions to be implemented (Dichmont et al. 2006; Kell et al. 2007) and can even modify management objectives. MSE is essentially a tool that helps managers achieve their goals on an uncertain world.
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Figure 3. Traditional MSE framework based on Kell et al. (2007) and Dichmont et al. (2006).
1.2.1
Developing a Catchment-to-coast management strategy evaluation (C2C MSE)
The C2C MSE framework was initially conceptualised by Pantus et al. (2008) (Figure 4) with the objective of demonstrating the application of MSE concepts in a C2C environment. This seminal work is referred to as version 1 of the MSE Tool. It aimed at putting the adaptive management framework into practice, meaning that management actions are modified in response to changes in circumstances. The adaptive nature of the C2C MSE implies that we need to monitor progress toward a set of previously defined targets (Figure 3). Consequently, MSE is implemented with the objective of incorporating all major elements of the ‘real world’ adaptive management that would significantly affect the performance measures (Pantus et al., 2008: 9) (Figure 4). We use Pantus et al. (2008) to explain Figure 4 as follows. In the 6-box diagram it is assumed that managers make decisions based on some knowledge about the system they are managing. These decisions are then translated into specific management actions that will help them achieve their objectives. For example, based on historical poor water quality results they can decide to plant riparian vegetation in gullies and stream banks to improve water quality in the waterways. In the management action box, the decisions are implemented as a set of quantifiable measures. For example, planting a given area of riparian vegetation in a particular region. Uncertainties and time lags are also implemented in the management actions. For instance, it takes some time for the vegetation to grow or for a sewage treatment plant to be constructed or upgraded. Also, the implementation of management actions do not necessarily conform with what was originally planned in terms of effectiveness and planning time lines. The management actions will then influence human and ecosystem response models, which are constructed based on the best knowledge available. These response models can include models of the ecosystem (biological, environment), economy, water quality and quantity, social responses, etc. The choice of models to be included will depend on the objectives of the modelling exercise and they are used to assess the effects of management actions in the system. Another important feature that is necessary for a C2C MSE is the representation of an observation system. Our understanding about waterways health comes from monitoring programs, which differ for example, if samples are collected in different spatial and temporal scales. An average of a water quality indicator will vary if samples are collected only during events, daily, monthly or at longer time intervals. Therefore, we need to design a spatial-
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temporal observation/monitoring element, which acts as a snapshot of a complete system understanding. The observation model thus simulates the way we monitor the real-world through field observation programs. However, as monitoring data is characterised as having various sources of error and variance, these should also be included in the observation box. Examples of error include errors in reading an instrument and error in the instrument itself. Variability in measurements can be attributed to natural variability in the underlying process being measured (e.g. concentrations of total nitrogen are affected by a range of non-observed variables). Pantus et al. (2008) refers to the result of observation error and process variability as uncertainties. Walker et al. (2003) refers to this particular source of uncertainty as uncertainty in model parameters. Other examples of uncertainties referred by Walker et al. include uncertainties in the model structure, boundaries of what should be included in the models used in the MSE, and the accumulated uncertainty outcomes from the examples above. The assessment box “simulates the reporting phase and contains methods to turn collected data into management performance measures”. The reporting can be as simple as summary statistics for the indicators under consideration or more sophisticated methods such as the simulation of the report card grade system. The next element of the MSE is the learning box, which is the place where the outcomes of management actions are compared with management objectives and where the decision rules are incorporated (‘if this happens, than do that’). The learning component will inform the next MSE cycle.
Figure 4. Conceptualisation of the C2C MSE approach (from on Pantus et al., 2008) (© South East Queensland Healthy Waterways Partnership).
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1.2.2
Complementing the C2C MSE concept and functionalities
In version 2 of the MSE-Tool we built on the framework developed for version 1 (Figure 4) to further develop some of the C2C MSE concepts and functionalities. In particular we wanted to better understand how managers learn and make decisions in a C2C setting. To do this we refactored the MSE-tool by making significant improvements in the user interface to allow easy interaction, in its architecture to allow further flexibility in the way models are designed and implemented, and how model outputs are visualised. With regards to the decision-making process, in the context of traditional fisheries MSE, decisions by managers are based on well-understood ‘levers’, such as catches and by-catches, while considering regulatory constraints and profitability. This allows the MSE to include well-defined decisions rules in the modelling framework (Figure 3), resulting in a closed loop as identified in the AM cycle (Figure 2). However, assessing management strategies to improve water quality in a catchment-to-coast setting includes a much wider set of political and biophysical constraints for managers, many of which have not been properly investigated as yet (see page 23 and Chapter 1.1.5). This precludes the straight application of the traditional fisheries MSE approach in the coastal zone, as it is necessary to understand the decision rules and the effects of management actions in the C2C domain. As a result, the fisheries-type MSE approach must be adapted to the more complex C2C setting. We identified two defining characteristics of the C2CMSE approach: • Stakeholder engagement is essential due to the far greater political aspects (e.g. council located upstream negatively affecting waterways at council downstream) and other uncertainties involved in C2C management. Local planners and resource managers are responsible for quality-related decisions in their jurisdictions, but they also need to understand the consequences of their decisions further downstream in order to improve regional water quality. In this context the C2CMSE approach can be used to supplement local/regional decision-making processes. • It needs to integrate the wide diversity and wealth of knowledge from scientists and experts working in the region. This second characteristic reinforces the first by providing a modelling tool that is scientific sound. Similar to the fisheries MSE, the catchment-to-coast MSE can use a variety of computer simulation models (e.g. qualitative, statistical, process-oriented) to assess management strategies and presenting results as a set of the trade-offs between selected indicators across competing management objectives. However, due to the catchment-to-coast MSE characteristics listed above, we used collaborative methods to engage with stakeholders to define the specifications of the computer simulation model (The MSE Tool) (Figure 5). At this stage it is important to specifically define the elements/agents of the C2C MSE approach as: • Stakeholder: a person or organisation with an interest or concern about catchmentto-coast issues. Businesses people, councils, residents are examples of stakeholders. • Project Team: The CSIRO WfO research team responsible for developing the MSE approach and the associated simulation tool, and to assist the Healthy Waterways with stakeholder engagement with respect to the refinement and application of the tool. • MSE Tool: software that integrates models about the managed system and allows its users to test management strategies on this system. The software records all choices made by users and yearly values of environmental, social and economic indicators. The following are used to evaluate strategy outcomes at the end of each assessment period: • Indicators of the biophysical realm (e.g. water quality variables). • Indicators of social satisfaction, linked to biophysical indicators.
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•
Indicators of management costs, including estimates of investment and maintenance costs calculated by the MSE Tool as a consequence of management actions selected by the user.
The collaborative approach we used in the C2CMSE enabled stakeholders to make significant contributions to the design of the Tool because they had the opportunity to use and learn from it prior to release. Conversely, the project team learned about what information managers consider relevant when making decisions, the issues related to C2C water quality management in SEQ, and the way they want to use the tool. All this information was used to improve the Tool and it is important to note that the C2CMSE approach is an ongoing ‘best practice continual improvement’ process and, hence, learning is an important outcome to all involved (Figure 5).
Figure 5. The Catchment-to-coast Management Strategy Evaluation approach whereby the MSE Tool is used to mediate interaction between the project team and stakeholders (based on Dutra et al. (in press)).
1.2.3
The conceptual model of the catchment-to-coast MSE Tool version 2
The project team built a conceptual model of the MSE Tool Version 2 (Figure 6), based on the framework presented in Figure 4. It is important to note that the concept of scenario used in version 2 differs from the term scenario used in version 1. In version 2 scenarios are defined as alternative sets of initial condition values (Appendix 1), while in version 1 scenario is defined as a set of management actions to be tested against a set of objectives (management scenario). The conceptual framework implemented in version 2 consists of human activities and decisions represented by a real or a ‘virtual’ manager (in brown) who makes decisions and implements rural, urban or water treatment actions affecting the response of the simulated SEQ catchments (green). In the current version the observations are incorporated in the response models as it uses the EHMP stations and sampling periods. In version 3 of the MSE Tool we will hybridise the process-oriented models initially developed in version 1 with the empirical approach used in version 2 to de-couple responses and observations. The world is assessed through the EHMP report card grades, social perceptions in terms of the grades and
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economic outcomes (yellow). In the interactive open-loop simulation, managers are able to learn by assessing the outcomes of their decisions based on pre-defined environmental, social and economic criteria (blue) through the MSE Tool user interface. However, a computer assisted learning element can be used based on the same rules managers use to make decisions. Version 2 allows users to record, store and retrieve the strategy and compare it with other strategies in the closed-loop (non-interactive) pathway. In the future it is expected to implement an actual learning model in the MSE Tool to automate the learning and decisionmaking components. MSE is about testing if management strategies are robust under different circumstances. For example, we want to know the outcomes of strategies under different governance and social support arrangements, future development scenarios and future weather and climate scenarios. Some of these scenarios are currently available in the MSE Tool (Chapter 1.3.1). To deal with uncertainties, the Tool allows also the strategies to be run over multiple (stochastic) repetitions needed to explore their effects on an uncertain future.
Figure 6. Conceptual framework of the MSE Tool.
1.3
The collaborative approach for the C2C MSE
The C2CMSE approach applied in SEQ includes sharing documents with stakeholders (Healthy Waterways) to specify the requirements for the MSE Tool, interviews with stakeholders, and an interactive workshop (organised by the project team, with participation
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of Healthy Waterways representatives and representative decision makers1) to allow stakeholders to use and provide feedback about the tool. The approach enables the project team to better understand the users’ needs, expectations and the context of decision-making in SEQ, and overall to define specifications for the MSE Tool.
1.3.1
Vision document
The project team engaged with the Healthy Waterways to specify the requirements for Version 2 of the MSE Tool. These were added to the requirements previously carried out from Version 1. The Vision Document (Pascual et al. 2009) was used to define the high-level requirements of MSE Tool Version 2.0 in terms of the needs of the end users and stakeholders. The document identified the changes required from Version 1.0 and who were the end users and stakeholders for Version 2. The document was submitted to the Healthy Waterways and WfO and discussed at a meeting in the Healthy Waterways office on the 10th of August 2009. In essence, the MSE Tool was intended to be a design system for adaptive management, or a “flight simulator” for managers. In a simulator we know the exact state of the world – the things we cannot observe or afford to observe in the real world. Hence, we can evaluate our management system using more information than is generally available in real world decision-making. The following specifications were used in the development of the MSE Tool: • Must allow management of all of SEQ, not just one catchment. • Must simulate the report card results. • To be used by managers as part of their work. • To be used in 5-year regional planning time frames. • To be used on ‘standard’ windows desktop environment. • Free or open source database. • The user interface (UI) must be easy to use and be responsive. • Time lines: V2.0 by April 2010, V2.1 by September 2010. V3 is planned. The following requirements were carried over from the previous version (V1.0): • Must support models in non-.net languages. • Deal explicitly with uncertainty through support of stochastic models, explicit expression of uncertainty in results and support for the specification of uncertainty through UI. • Must allow multiple runs in an unattended environment. Below are the projected requirements for future versions: • Must be able to cross model platforms in a web UI environment. • Must be able to share results and the initialisation of data changes. • Must allow model development by people who are not framework developers. • Must work with multiple sets of models.
A detailed description of the software architecture of the MSE Tool version 2 is provided in Appendix 1.
Representative decision makers are managers from councils or natural resource management bodies. 1
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1.3.2
Interviews
Retrospective analysis of decisions In the retrospective analysis of decisions the project team explored key decisions about water quality related projects taken in the last 20 years in order to guide development of the MSE Tool. The research team undertook in-depth interviews with 19 policy-makers to better understand the enabling factors and constraints to decisions regarding water quality management in SEQ. In particular, we sought to clarify the relationships between information and decision-making in order to build scenarios and present results through the MSE Tool user interface. Content analysis techniques were used to analyse interviews, leading to a generic model of decisional processes (Figure 7). The model was used to design an interactive version of the MSE Tool (open-loop version). The interactive version was used to elicit learning processes during social experiments conducted with representative decision-makers and researchers. Results from the retrospective analysis of decisions suggest that decision-making processes involve six major classes of factors (Figure 7): (1) Context (favourable or not; presence of an exceptional event or not); (2) information (availability and quality); (3) assumptions (robust or not; consensual or not); (4) leadership (presence of transactional2 leaders or not; support from executive leaders3 or not); (5) regulatory framework (existence or not; conflicting or not); (6) communication (effective or not; good or bad quality); and (6) logistical considerations (adequacy and timing) that impact on the effective realisation of a decision. The relative importance of each factor, and the sequence through which each of these factors interplays, depends on the type of decision to be made (intervention, planning, education or legislation). The information was used to inform the scenarios currently available in the MSE Tool and the overarching reporting methods of the tool.
2
Transactional leaders motivate people through the use of reward/punishment approaches.
Executive leaders are responsible for the highest level planning and strategic implementation of management decisions. 3
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Figure 7. Generic model of decisional processes based on in-depth interviews content analysis.
Expert opinion to inform the management model included in the MSE Tool With the objective of refining the list of management actions available in the MSE Tool, and to refine abatement coefficients of management actions in water quality at EHMP monitoring stations, the project team interviewed eight experts between August 16th and 27th 2010. A document containing information on how the management model influences the biophysical model was sent to participants prior to the interviews (Appendix 2). Interviews were semistructured and lasted for approximately one hour each. The interviews indicated areas for improvement for the development of the MSE Tool, including expanding and re-organising the list of available management actions and improving the efficiency of management action algorithms (for details on the management model of the MSE Tool see Chapter 3). The key messages from interviewees are summarised in Table 1. Because the interviews with experts were conducted in the final days prior to delivery of the project, only a limited number of suggestions could be included as part of a project configuration available in the MSE Tool. For example, the project team did not have time to undertake a literature review of the economic costs of the management actions proposed by interviewees. These will be further investigated in future versions of the MSE Tool. Additionally, there was not time to discuss the suggestions with the Healthy Waterways. Hence, information from both interviews (retrospective and expert opinion to inform the management model) was used to build two project configurations (see Chapter 0 for details): • Literature-based configuration: contains the management actions used during the openloop workshop. • Expert based configuration: contains management actions from expert opinion elicited from interviews. Each of the configurations has the following scenarios:
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• •
Scenario 1: Always Effective (effective governance and social support (e.g. strong leadership, effective communication and supporting regulation)). Scenario 2: Decreasing Effectiveness (weak governance and weak social support; e.g. poor leadership, poor communication and conflicting regulation).
Interviewees provided some useful comments about the tool and directions of future improvements that will be considered in version 3 (Table 1). Table 1. Key messages from interviews with stakeholders on pressure-response issues with comments from the project team.
Area for
Key messages
improvements
Management actions
Keep management actions general instead of specific to facilitate visualisation of results. STP upgrade option should be more specific and include water re-use (domestic and industrial) and recycling. Artificial wetlands have been historically problematic in SEQ due to ineffective design but it is worthwhile keeping it in the MSE Tool. However, it’s a limited action due to the need for space to build them. Use SQID (stormwater quality improvement devices) in urban areas instead of rural. Fencing, water troughs and porous weirs should be amalgamated into one category called “off stream watering” to prevent stock access to stream. Future versions of the MSE Tool should consider the two stages involved in urban development in the action “best urban design”:
• •
Development phase: land clearing, where land is in transition to Urban with potential for high sediment and nutrient runoff. Operational phase: total nitrogen and total suspended solids will be lower than in the development phase, but will have higher frequency of flows and loads.
Best Urban Design for Brownfield (sites that are limited in expansion due to environmental contamination) developments could be also an option to be used in future versions of the MSE Tool. Include option “erosion and sediment control” to be applied during the development phase. Ground cover (e.g. trash blanketing) should be included under best farming practices. Re-vegetation (grass, riparian and non-riparian) should be allowed both in rural and urban land-uses. Contour planting in hill slopes may be another option in best farming practices. The MSE Tool should include a specific management action to deal with gully/channel rehabilitation, as most of the sediments delivered to Moreton Bay are from gully erosion (Caitcheon et al. 2001). Grass swales are actions that are reasonably cheap and effective. There are some studies showing its effectiveness and costs for the Logan council area are available. Effectiveness of management actions
Re-vegetation is one of the most effective ways to improve water quality. Local conditions (flow, slope, depth of channel, soil type, vegetation type) affect the effectiveness of management actions in a very complex way. Using a weight to the management actions that uses only distance is probably too simplistic. Further field research is needed to improve management action effectiveness in the MSE Tool. The project team should run process models to get the relationships between
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Area for
Key messages
improvements
management actions and distance to EHMP. This way the effectiveness coefficients will be more in line with current research in SEQ Who is responsible to adopt a management action is an important factor in determining its effectiveness. Normally if individuals are responsible to adopt a measure to improve water quality, they will do only if cost effective (generally less than 10 percent of individuals will adopt a management practice that is not cost effective). If the responsibility of implementing the measure is up to the council its implementation is normally higher than 50 percent Effectiveness coefficients for gully/stream rehabilitation should be higher than onground work on farms, as most of the sediment delivered to Moreton Bay (~70 percent) is originated in gullies (see above). Potential use of the C2CMSE approach and Tool
It is important that councils from upper catchments and estuaries work together to improve overall water quality. Currently there are some councils co-located in the same catchments that do not have effective communications. The C2CMSE could be used to (re-) establish communication between councils, for example, the Southern Implementation Group. TWCM (Total Water Recycle Management) is a plan that councils with populations larger than 10,000 people are obliged to provide by 2012. The MSE Tool could assist in this planning process.
Overall interviews with experts provided very useful outcomes which were partially included in version 2. Despite the fact that it is widely recognised that further research is needed in order to better understand quantitatively (e.g. proportional change) how a catchment intervention can improve water quality at a monitoring station downstream, a few interviewees felt uncomfortable with the approach used during the interview. In particular, the question about numbers or a range that could be used in the management model as a proportional change influencing water quality indicators at EHMP stations downstream. Other experts highlighted the usefulness of the approach by suggesting that qualitatively the proportional changes of each management action could be seen comparatively on a relative scale. With the current knowledge it is possible to at least rank actions in terms of their relative effectiveness. Experts also suggested the use of process-oriented catchment models to derive the effectiveness of each management action.
1.3.3
Workshop
Interactive “open-loop” MSE The interactive “open-loop” MSE workshop was held at the Healthy Waterways office on April 27th and 30th 2010 and in the CSIRO Cleveland Laboratories on the 2nd of July. A fourth workshop was held at the CSIRO Laboratories in Floreat (WA) on the 19th of May 2010 but files containing the results were corrupted and could not be used for analysis. Participants were members of the Healthy Waterways Scientific Expert Panel, council representatives/managers, and researchers, selected by Healthy Waterways for the two initial workshops and invited by the project team for the third workshop (n=22, where 6 participants attended the workshop on 27/4, 8 participants on 30/4 and 8 participants on 2/7). The project team used information collected during the interviews for the retrospective analysis of decisions to construct the interactive open-loop version of the MSE Tool (Figure 8). During the workshop the project team explored learning (information required, indicators used to evaluate success and failure of a decision) and decision-making mechanisms under a controlled social experiment (i.e. which changes in the system are expected to contribute to the overall management goals and which variables can be effectively manipulated to achieve the goals.
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The interactive open-loop version of the MSE Tool allowed the manager to be in control of the operation of the Tool so that they could implement their management strategies, make decisions and learn about the consequences of their strategies for a simulated catchment in SEQ. The participants could adjust their decisions after reviewing information about how the system changed in response to their actions. During the workshop participants were asked to “manage” the virtual Caboolture catchment for a simulated period of 30 years to improve water quality, also considering social and economic constraints. Participants were given the option to select when they wanted to assess their decisions (1, 2 or 5 virtual years) and also the opportunity to modify their strategies according to model outputs. Workshop participants trialled the model according to different sets of instructions for two experiments. Their decision-making strategies were recorded and compared. A report containing the details about methods and results is currently in preparation by the project team. The interaction between managers and the MSE Tool in the “open-loop” simulation resulted in a more flexible way to define the suitability of a management decision in the region, and provided also the opportunity to change decisions at any stage of the process. Initial results from the workshop showed that participants adopted strategies with very different profiles, such as applying management actions in the upper reaches of the catchments, adopting strategies focusing more on the urban environment, or a mix of both. Some strategies were much more successful at minimising costs entailed to achieve improved water quality at the scale of catchments, with the least effective approaches being four times more expensive than the most effective approaches. Overall, outputs provided by the MSE Tool were useful to participants as the strategies were modified to achieve their initial goals. The approach used in the workshop helped participants change some of their perceptions and values about the effects of their decisions on water quality, social and economic indicators. A detailed report containing results of the open-loop workshop is currently in preparation.
1.3.4
Towards a non-interactive evaluation of decisions (closedloop MSE)
Planners and resource managers in SEQ are unlikely to have the time to rerun the open-loop MSE over the hundreds of repetitions needed to explore the effects of their strategies on an uncertain future. This problem is solved by automating the decision process that managers use in conjunction with important uncertainties that are built into the MSE Tool models (e.g. effective governance and social support and weak governance and weak social support). The closed-loop functionality (Figure 8) records, stores and retrieves management strategies, allowing them to be compared and run under different scenarios. This is applied as a preliminary resource before the implementation of learning and decision-making models.
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Figure 8. Synthesis of methods used in the C2CMSE.
1.3.5
Implications of interviews and workshop for the conceptual model of the catchment-to-coast MSE Tool
The interviews and workshops validated the conceptual model of the MSE Tool Version 2. The open-and closed-loops contained in Version 2 were regarded as useful due to the issues involved in constructing an algorithm or a process that simulates learning and decisionmaking process. Instead, the open-loop allows the user to record their strategies and compare to other strategies, which makes the learning component outside the MSE Tool in the current version, meaning that real managers learn-by-doing.
1.4
Chapter conclusions
The C2CMSE approach is defined by the following three elements: (1) the project team; (2) the stakeholders; and the (3) MSE Tool (Figure 5). The project team is represented by the WfO team, which is responsible for stakeholder engagement, knowledge integration and development of the computer simulation tool (MSE Tool). Stakeholders actively contributed to the design and development of the Tool, which is used to test management strategies, but also to mediate the interaction between project team and stakeholders. The interaction between stakeholders and the project team also helps to keep stakeholders’ expectations about the tool at realistic levels. The C2CMSE approach was implemented in the following four stages: • Establish a living Vision Document that contains high-level specifications of the MSE Tool in terms of the needs of the end users and stakeholders. • Interview stakeholders to learn about their current decision-making processes and to elicit what information they need in order to make their decisions (these are included in the MSE Tool). • Develop the MSE Tool for use by stakeholders.
39
•
Undertake workshops whereby stakeholders use the tool to test alternative management strategies. The project team used information on how stakeholders interacted with the Tool to improve it.
The above participatory approach permits stakeholders to therefore make significant contributions to the design of the MSE Tool, and simultaneously allows the project team to discover what information managers consider relevant when making decisions, what issues are relevant to water quality management in SEQ, and how they want to use the tool. By using inputs from prospective users in its development, the tool value-adds to their existing knowledge and expertise because it enables them to rapidly develop and evaluate local and/or regional water quality management plans. The C2CMSE approach focuses on management outcomes by implementing measures of progress against performance criteria, helping managers learn about the managed system in order to achieve their objectives. In summary, the C2CMSE approach helps managers with the following: • Allows them to make and learn from mistakes quickly and without adverse consequences. • Check whether or not objectives are achievable and when the system might get there. • Improve collaboration with other agencies, stakeholders and scientists. • Make trade-offs between multiple management objectives and options. • Design monitoring strategies and evaluate assessment methods. That is, what to measure, how often and where. • Identify critical knowledge gaps. • Design around structural uncertainty by using different model structures when evaluating management strategies. In future versions of the MSE Tool we intend to develop a learning and decision-making model based on the outcomes of the development of version 2. Refining the current models and expanding the approach used in version 2 to include water quantity issues is another task for the future. This will require a broader engagement with a wider stakeholder community and improvements in the current modelling framework (details in Chapters 2 and 6.5.1).
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2.
BIOPHYSICAL CATCHMENT-TO-COAST MODELS
2.1
Upper catchments
In version 2 of the MSE Tool, the upper catchment response model is not implemented. Instead, we have a linear mixed-effects model that links EHI of freshwater EHMP stations with the distribution of vegetation cover in their contributing catchment. Each 1x1 km cell (in white in Figure 9) of the management grid is characterised by a land use type informed by the following finer resolution land cover maps (Figure 9): • High density vegetation (D) • Medium density vegetation (M) • High density vegetation located within a 100m wide corridor along of a stream (R) • Urban (U) • Void areas (paddocks, bare fallow) (V)
Figure 9. Representation of the upper catchment model. Letters represent land cover: D –high density vegetation, M – Medium density vegetation, R – high density vegetation within a 100m in each side of the stream, U – urban, V – void areas (e.g. paddocks and bare fallow).
The MSE Tool allows for management actions in the upper catchments to affect report card grades directly. These actions should have detectable effects on the water quality indicators at the upper catchment monitoring stations and, consequently, on the report card scores. However, unlike with the estuary stations, the upper catchment indicators are difficult to model statistically. In the estuaries six indicators are measured (total nitrogen (TN), total phosphorous (TP), turbidity (TB), Chlorophyll a (Chl a), LP (light penetration) and dissolved
41
oxygen (DO) and, at each site, their joint distribution is described by a multivariate lognormal distribution (see section 2.2). In the upper catchments, however, the indicators are different and do not have an easily described joint distribution. This is because the indicators comprise a mixture of data types, such as counts, proportions and interval data. To overcome this difficulty we adopted a two-stage approach: • First, using statistical models of the historical data, we established a relationship between the ecosystem health score Hs of a station and certain measurable quantities, such as land use and riparian vegetation Rs, which would be affected by management actions. In a simulation, Hs is then predicted for each station, and these scores are used to compute the report card score for each upper catchment region. • Second, the historical indicator data set is used as a population from which to draw samples. Given a station with health score Hs, we look in the historical data set for a station-year combination whose annual EHI value is closest to this score. Then the indicator data from that historical record is used for the station s. The indicator data can then be used to drive conditions at the downstream estuary stations. In summary the approach will calculate future EHI based on historical data on EHI indicators. This approach will be further explored in version 3. In this report we describe the first stage only, because this is the stage that has been implemented in the current version of the Tool. In this version the management actions in the upper catchments affect the monitoring station at the top of the estuary directly, in the same way that all other estuarine stations are modelled, throughout the watershed. The second stage of our approach will be implemented in a later version of the MSE Tool. In this version, the flow-on of effects to the estuary will be via the upper catchment grid cells themselves. That is, management actions implemented in an upper catchment directly influence the uppermost estuarine EHMP station connected to that grid cell.
2.1.1
Methods
In order to find the relationship between station properties and health score, we have been guided by the approach described in Sheldon (2010). Sheldon finds that the annual health score for the catchment strongly depends on the proportion of urbanised land, the proportion of pasture and the amount of rainfall. The health score decreases when urbanised or pasture land increases, or when rainfall decreases. In the context of the MSE, we wish to find a relationship between the health score and some quantity that is affected by management actions. We obtained data on vegetation cover within each 1km square management cell of the MSE Tool. Each cell was classified as being either Dense, Medium or void with respect vegetation cover, or Urban. We also obtained the area of each cell that was within 100m of each side of a stream of order 4 or more. Cells on such a stream are candidates for the riparian revegetation management action. We then computed the area of the 100m stream buffer that was within cells classified as Dense. We use this as a measure of Rs, the Riparian vegetation. As the simulation proceeds, Rs will change as management actions are applied. For each station we computed within the watershed for station •
the percentage of urban cells
•
the percentage of void cells
•
the percentage of medium vegetation cells,
•
the percentage of dense vegetation cells, and
42
• Rs the percentage of 100m stream buffer within dense vegetation cells. The spatial coverage data on which these quantities were based date from around 2006. Sheldon (2010) also stated that rainfall is important. We did not have detailed rainfall data in a form ready for analysis and, hence, used Year as a factor in the analysis as a surrogate for rainfall. Finally, we obtained historical ecosystem health scores Hst for all freshwater stations s and times t over the period 2002–8. These are the numeric scores (between 0 and 1) derived from the raw index data (conductivity, temperature, dissolved oxygen, pH, δ13, C, GPP, R24, δ15, N, richness, PET, SIGNAL, PONSE, fish O/E and % aliens) as described in Healthy Waterways Inc (Healthy Waterways Inc 2010). The report card grade score (a letter in the range A to D– or F) is derived from the average numeric score over the upper catchment region.
2.1.2
Results
Because the score H is a bound response, models based on least-squares tend to have nonnormal residuals. It is common to apply the ‘arcsine’ transformation, with this deficiency, and we do this here.
to deal
As an initial exploratory step we analysed the data using Random Forests (Breiman 2001) in order to gauge the dependence on the explanatory variables. Unlike linear models, that impose linear dependence on the explanatory variables, random forests allow the data to ‘speak for themselves’ in that the dependence on the variables, which is an output of the fitting process, can take an arbitrary form. The form is provided by a diagnostic called a partial dependence plot. Figure 10 shows such a plot for a model in which Year, Season, Us, Rs and Ms are fit to h. The dependencies are in broad agreement with Sheldon’s. For instance, the scores are lower in spring and decrease with increasing urbanisation. There is some inter-annual variation and there appears to be a threshold in Ms. This is the kind of non-linearity that is difficult to anticipate for when fitting linear models. There is a very slight increase with Rs although there is a wiggle at the 90th decile that may be due to special circumstances at the station contributing to that data.
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Figure 10. Partial effect plots from a random forest model fitting hst to Year, Season, Us (urban), Rs (riparian) and Ms (medium density vegetation). The ticks along the top show the deciles of the distribution (for Us and Rs the first few deciles are at 0%).
Although Random Forests are good for exploring dependencies, the model description, being an amalgamation of hundreds of regression trees, does not allow for easy interpretation. In particular, random forests do not provide a simple coefficient for Rs. Instead, following Sheldon (2010), we fit the transformed scores with linear mixed-effects models, using the Random Forest partial plots as a guide. The fixed effects were the numeric variables Us, Rs and M50 (a variable having value 1 when Ms > 50 and 0 otherwise) and categorical variables Season, Year and Catchment. We included Station as a random effect to account for unexplained variation of the kind suggested by the partial dependence plot for Rs. Formally, the model was
, , . Here, Cc, Cy and Cs are the effects for Catchment, Year and Season respectively, aU, aM, aR are coefficients for the numeric terms, Zs is the station random effect and σst is the residual
44
(1)
random variate. The estimates of the coefficients and variances are given in Table 2. We also calculated the best linear unbiased predictors Zs for each station (i.e. the station random effects). Table 2. Estimates and standard errors of fixed-effect coefficients Cc, aU and aM, and estimates of σs and σst, after fitting the mixed-effect model using REML.
Type
Coefficient
Catchment
Albert
1.2260
0.0518
Bremer
1.0288
0.0360
Caboolture
1.1954
0.0462
Pimpama/Coomera
1.2187
0.0601
Tallebudgera/Currumbin
1.2592
0.0537
Lockyer
0.9977
0.0313
Logan
1.0846
0.0282
Lower Brisbane
1.0440
0.0467
Oxley
1.0572
0.0675
Maroochy
1.1744
0.0479
Mid Brisbane
1.1956
0.0998
Mooloolah
1.2408
0.0478
Nerang
1.2439
0.0441
Noosa
1.2238
0.0473
Pine
1.0531
0.0482
Pumicestone
1.2183
0.0590
Redlands
0.9799
0.0416
Stanley
1.1924
0.0386
Upper Brisbane
1.0358
0.0281
Season
Autumn Spring
Year
Value
0 –0.0496
2002
0
2003
0.0150
Std.Error
– 0.0066 – 0.0144
2004
0.0457
0.0143
2005
0.0001
0.0142
2006
0.0097
0.0143
2007
–0.0306
0.0143
2008
0.0342
0.0169
Riparian
0.0083
0.0267
Land use
–0.0021
0.0007
0.0979
0.0238
Random effects
0.0933 0.1133
45
Our analysis agrees partly with Sheldon’s results in that she found negative effects for Urban and Pasture land uses. Dense and Medium vegetation (positive effect) is equivalent to low Void vegetation which equates to Sheldon’s Pasture land use. We also found, like Sheldon, that spring scores were lower than autumn scores. Most importantly, aR is positive so riparian revegetation actions can be used to drive H in the right direction. Although the model captures some of the features of the observations (Figure 11) there remains substantial unexplained variability. The model is least accurate for the F scores, which is the domain where management actions are most needed. The unexplained variability is probably due to detailed spatial effects in each catchment. Later versions of this model may build on the work of Peterson et al (2010), using more refined land-use metrics based on stream flow distance and flow accumulation.
Figure 11. Level of accuracy of random forest model for the upper catchments.
We also made an extensive search through alternative models using backward selection and likelihood ratio tests (having fitted the models with the maximum likelihood method). Because such methods insist on dropping non-significant terms, the optimal models tend to exclude weak effects such as riparian vegetation. However, the purpose of this analysis is to find a coefficient for riparian vegetation that we can use in the simulation. Additionally, we know riparian vegetation must have an effect; the non-significance of the term simply means we have large uncertainty in the estimate, but the estimate we do have is the best estimate given the choice of model. Furthermore the Random Forest analysis confirms that there is a small effect and the results from the mixed-effects analysis are consistent with this.
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2.1.3
Implications for the MSE simulation
The coefficients in Table 2 can be used directly in a simulation. To simulate the variation among years (or, equivalently, rainfall) the Year effect is chosen at random from the 7-year terms in Table 2. For each year and station we generate two health scores, one for spring and one for autumn, each with and independent random noise value σst. The score hst is generated from Equation (1) with the appropriate coefficients drawn from the Table. If this exceeds π/2 it is set to π/2. Then Hst is computed with the inverse relationship Hst = sin2 hst. As the simulation proceeds, the riparian revegetation increases when management actions are applied to the cells c in the watershed of the station s. The effective increase in riparian vegetation Est at time t for station s is
where As is the area of the watershed and Acs is the area of the management cell. The quantity αa is the effectiveness of the action a per unit cell. It is the area of riparian revegetation that is equivalent to 1 unit of action. There needs to be a limit to the amount of revegetation that can be effectively applied. This is set as the total percentage area of river buffer Bs in the watershed. The value of riparian revegetation at time t then becomes:
The values αa are given in Table 3. Table 3. The quantity αa is the effectiveness of the action α per unit cell. It is the area of riparian revegetation that is equivalent to 1 unit of action.
Action a
2.2
Effectiveness α α
Best Farming Practices
0.03
Best Urban Design
0.00
Gully/Channel Rehabilitation
0.10
Revegetation
0.10
Rural Riparian Revegetation
0.10
Rural Stormwater
0.10
Sewage Treatment Plant Upgrade
0.00
Urban Stormwater
0.00
Estuarine empirical biophysical model
The Healthy Waterways EHMP has a network of 253 estuarine and marine monitoring sites at which a range of water quality related variables are measured at monthly intervals. Data have been collected for the last ten years and capture important relationships between measured variables within sites and between sites. These results were used to derive empirical relationships in order to: • Generate, for each node (EHMP monitoring station) in the network, future simulated multivariate stochastic observations that have similar statistical properties in terms of magnitude, variability and correlation to those observed in the data collected under the EHMP.
47
• • •
Be able to force some of the variables at any given site to take on new average values to simulate the effects of local management actions. See the consequential effects of those local actions on the remaining variables at that site as captured through the historic correlations. Propagate the effects of management actions at any set of sites on the network to examine the consequential cumulative affects across the network.
It is assumed that the effects of management changes propagate in the same direction as net water flow. All of the data are concentrations or local properties (such as light penetration) and so the effects of flow volumes are assumed implicit, but nonetheless captured in the data through the effects of correlation. Suppose that a monitoring site is below the confluence of two streams, one with a high flow volume relative to the other. We would therefore expect that the observations at the downstream site will be more highly correlated with the observations from the high flow branch site than they will be with those from the low flow site. Therefore, the historic concentration observations should implicitly capture much of the volume effects even though no volumetric flow data are used in the model. The underlying assumption of the empirical model is that the observations at each site reflect site specific processes that determine the relationships between the different monitoring variables and that those relationships are consistent over time. Thus if a management action was to increase one of the variables, we would expect that other strongly positively or negatively correlated variables would go up or down as well, while weakly correlated variables would show little effect. By using the correlation between variables we are assuming that the effects of changing a variable are linear, which is probably reasonably accurate for small management actions, but may become increasing less accurate for large changes, particularly when a management action drives parameters outside the ranges observed in the historical data. However, the results should still be qualitatively reasonable, even if quantitatively inaccurate. Between sites it is also assumed that there are relationships such that a change in a variable at an upstream site has a consistent and predictable effect at a downstream site. In MSE it is common to look at relative changes in management strategies instead of absolute values. The first objective is to generate simulated stochastic observations at a single site using the observed statistical properties of the observations. The observed values of the EHMP program are positive and usually right skewed, so it is reasonable to use a lognormal distribution to model each variable. Moreover, we assume that the joint distribution follows a multivariate lognormal distribution. Let yi be a matrix of the multivariate observations at a specific site i, with r rows corresponding to each monitoring event and n columns, one for each monitoring variable. Let xi = log(yi) | y > 0
The log-transformed observations will have a mean vector
Equation 1
and a variance matrix Si,
calculated using the usual methods of multivariate statistics. This information can be used to generate a random vector from a multivariate normal distribution by transformation of a vector of random numbers drawn independently from a standard normal distribution, N(0,1). That is: Equation 2
Where is a vector of independent random numbers from a standard normal distribution and Z is a matrix based on the principal components transformation and is given by:
48
Equation 3
L is a diagonal matrix whose elements are:
where
Si and G is a matrix whose columns are Si. The variance matrix Si needs to be positive definite.
are the eigen values of the variance matrix
the corresponding eigen vectors of
If there are too few observations Si will be singular, and occasionally, due to finite precision in calculations, the eigen values will not all be positive. Zero or small negative eigen values are replaced by a small positive number (10-15). This has a negligible effect on the random numbers generated (about 1 part in 108).
A vector of random numbers from a multivariate lognormal distribution is given by: Equation 4
The random numbers in
will be correlated in accordance with the variance Si.
One or more of the expected values in
can be multiplicatively adjusted. If only one variable
is adjusted the multiplicative adjustment is achieved exactly. If more than one is adjusted their expected values will be a compromise driven by the covariance structure. The multiplicative adjustment β for the parameter j is achieved by drawing zi,j from a normal distribution with an expected value given by:
The variances of
can also be adjusted by adjusting the variance of the random number
distribution used to generate
, i.e.
where γ is a proportional change in variance. This adjustment is needed where β is determined by a regression model as set out below. Where a monitoring site in an estuary or stream has another site immediately upstream (or sites immediately upstream at a confluence), the data will reflect local effects and effects propagated from upstream. These latter are accounted for by fitting a univariate regression
49
model with the dependent variable being only one of the observed variables at the downstream site, and independent variables consisting of all the upstream site variables. So, let vi,k,t be the univariate downstream dependent variable at site k at time t. At the upstream site j we have a multivariate observation vj,t. Since we are generating multivariate lognormal random numbers, vi,k,t and the corresponding upstream variable vi,j,t are replaced with their logarithms. Where there are two or more upstream sites, the upstream observations are augmented accordingly. In general;
Where f(.) is a predictive regression function and εt is a residual assumed to have a normal distribution N(0, σr). Any form of regression model could be used, but in the current implementation simple multiple linear regressions are used for sites in estuaries and streams. A step-down regression procedure is used to select the subset of upstream variables that have significant predictive capacity. Simulated observations at site k can only be calculated after the simulated observations at upstream sites have been generated. The upstream contribution to the simulated value for vi,k is given by:
where
is a random vector generated for the upstream site (or sites at a confluence). The
simulated observation vector is generated by adjusting the mean for the dependent univariate downstream variable with the combined multiplier;
where
is the mean value of the log transformed dependent variable values of the observed
values and β is any further local multiplicative adjustment as defined above.
is predicted
R2 of
from a regression that accounts for a proportion the variance of the observed values of the dependent variable, where R2 is the usual coefficient of determination. In generating the multivariate lognormal random numbers the variance of the dependent variable will need to be reduced because some of the variability will derive from the variability induced from the upstream site or sites. The remaining variability arises from the residuals of the regression model. The random numbers are generated by transforming a standard normal distribution, i.e. with a variance of 1 for each variable. Therefore, the residual variance σ2r = 1 – R2 and so the random number combining the systematic upstream effect and the residual component for the dependent variable is generated from a normal distribution:
The vector of observations is generated as described earlier using equations (2) and (4).
2.3
Moreton Bay empirical biophysical model
For observations in Moreton Bay, the simulated values of the dependent variable use a Random Forest model as the predictive regression function. The Bay Water Quality Model (BWQM) is a statistical model that generates predictions of water quality variables in the bay from measurements of water quality variables in the river mouths and other drivers. The
50
statistical model is specified by parameters that have been estimated from prior analysis of water quality data on a historical data set. The activity diagram (Figure 12) for this component is fairly straightforward. The BWQM takes a set of parameters to define the model. It is then able to take River Mouth Water Quality Measures as input. By combining these inputs with the parameters, the Model calculates Bay Water Quality Measures as output. The Random Forest model is a collection of 500 decision trees, each fit to random samples of historical water quality data spanning 2000–2008. Prediction from a random forest is achieved by averaging the predictions on each of the decision trees (Breiman, 2001). Random forests have been shown to have good predictive accuracy at the expense of interpretability. For each bay water quality variable a separate random forest model is fitted. The explanatory (or driver) variables on which the decision trees are built are as follows: LAT:
the latitude of the site in the bay
LON:
the longitude of the site in the bay
WQi:
water quality measurement at river mouth site i in the last month
There is potential to extend the model to include tide and wind in future versions. The driver water quality variables (WQ) used in the model are TN (mg/L), TP (mg/L), DO (% saturation), Chl-a (µg/L), TB (NTU) and Secchi depth (m). Only those sites for which sufficient data were available are included in the models. In Random Forest models, variable selection is implicit in the model fitting process. Therefore, all remaining variables are included in the model. Figure 13 shows how the model performs on a subset of sites, where it is possible to see how close observed and simulated data are (r2=0.64).
Figure 12. Activity diagram for Bay Water Quality Prediction Component.
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Figure 13. Performance of the random forest model at 25 sites in the bay for Total Nitrogen. Each horizontal panel represents a different site with site IDs in the brown strips on the left. Closed blue circles represent actual observations and open red circles represent predicted observations. This version does not contain the tide and wind predictors.
Figure 14 presents outputs of the estuarine biophysical model. It shows an example of a management intervention (dotted line) applied in an EHMP station at the Noosa River 29 km from its mouth. Dashed line indicates the limit between observations (EHMP dataset) and simulations. Figure 14 shows the effects of a theoretical management intervention, which reduces TN, also reducing levels of TP, which are strongly correlated. The Figure also shows that just reducing Total Nitrogen is likely to have little impact on the other variables (light penetration, DO, TB, and chl-a), as they are all weakly correlated to TN.
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Figure 14. An example on how management interventions affect the biophysical models used in the MSE-system. Dotted line indicates the point in which the management intervention was done. Dashed line shows the limit between observations and simulation.
In the model the effects of management actions propagate in the same direction as net water flow. Figure 5 shows a schematic representation of the hydrological network in SEQ, each node being an EHMP monitoring station. Node n1 represents an uppermost estuarine station where we randomly allocate indicator values from the historical EHMP records. The node n2 is affected by n1, but also by local influences. Further down at the mouth of the river, node n3 is affected by several nodes as well as local effects. In the bay, all nodes are influenced in some way by nodes at the mouths of the estuaries. At each EHMP station local effects and changes from upstream stations are propagated with consequential cumulative affects across the network.
53
Figure 15. Summary of biophysical models.
54
3.
MANAGEMENT MODEL
The management model of the MSE Tool enables the users to implement management strategies by selecting management actions in the catchments, which produce effects on the biophysical model (Chapter 1), with flow-on effects on report card grades (Chapters 1.1.4 and 4.1), social perception (Chapter 4.2) and economic outcomes (Chapter 4.3). The statistical biophysical model of the MSE Tool uses empirical data from the 127 freshwater and 253 estuarine and marine monitoring sites of the EHMP network (see Chapter 1.1.4). The data captures relationships between variables within and between sites on monthly intervals for the previous 10 years. Empirical relationships are used in the biophysical model to simulate future stochastic observations and to propagate the effects of local interventions along the monitoring network. The management model of the MSE Tool is applied over a grid cell overlapping the hydrological network (Figure 16), where the user can select a range of rural and urban management options that affect water quality at EHMP stations. Each management action applied in each cell produces a pressure-response relationship that changes water quality at its nearest downstream EHMP station. Current assumptions supporting the pressureresponse model are the following (see Figure 13, Table 4 and Table 5): • One cell (1km x 1km) can support up to four Management Actions (MA). • Each MA affects only two biophysical variables at the EHMP station: Total Nitrogen (TN) and Turbidity (TB). • Each MA is characterised by Nominal Abatement Coefficients (NACs) for TN and TB. NAC represents the maximum abatement coefficient possible for a given intervention if (1) the monitoring station is located on the same cell AND (2) this cell is considered as the only contributor to the monitoring station (equivalent to a 1x1 km catchment). • Each MA has a specific Implementation Lag (IL; time lag for first effects and time lag for full effect) that influences its NAC. • Each cell influences directly its closest downstream EHMP Monitoring Station (s). • Each grid cell contains information used to calculate costs and an assigned land-use (farmland, urban and natural). Information included in each cell are the following: • Riparian buffer area (based on 100m from both sides of the river margins). • Stream length (up to order 4) • Vegetation: • Dense • Medium • Void • Urban • The actual abatement coefficient (AAC) affecting TN and TB is based on individual cell contributions. • Local conditions are represented through a weight used to calculate the AAC at s. The weight uses riverbed distance (d) between the centre of a cell and its closest s in its calculation. If the monitoring station is located in the same cell as the management action and this cell is considered as the only contributor to s, then NAC=AAC (Figure 16).
55
Figure 16. The management model of the MSE Tool is applied over the stream network. Brown, green and yellow squares indicate urban, forest and rural land-use. Red squares indicate that a management intervention was implemented, affecting the nearest downstream EHMP station (green circles).
It is important to note that version 2 of the MSE Tool does not include any process-oriented models and that the AAC/NAC approach was developed to deal with the empirical biophysical model currently implemented in this version. In the next version of the MSE Tool a hybrid approach between process and empirical models will be implemented. This approach should, in principle merge the advantages of both empirical and process models: fast runs and more reliable values for NAC/AAC.
3.1
Nominal Abatement Coefficient (NAC) and Actual Abatement Coefficients (AAC)
A NAC is defined as the proportional change of the impacts of each management action on TN and TB at estuarine and marine EHMP stations, and represents the maximum abatement coefficient possible for a given intervention in a given environment. The values were assigned to the management actions based on information contained in the literature, and were also based on expert judgement elicited during the interviews (Chapter 1.3.2). NAC values will be refined in the next version of the MSE Tool by using process models to calibrate the effectiveness of management actions.
56
In contrast the AAC is a more abstract concept. The principle of the AAC comes from nutrient transformation and attenuation during transport, a well-known process described in the literature (Barlow et al. 2009; Semple and Dunlop 1998). Dilution theory suggests that locating a high generating land-use (e.g. dairy farm) near streams at the bottom of the catchment would have a significantly greater impact on receiving waters after material is exported out of the catchment than in streams in the upper catchment (Barlow et al. 2009). We assume that the NAC of management actions will also be attenuated according to its distance to the nearest EHMP station. We use an attenuation function from the literature (White et al. 1992) to represent the attenuation of the management action with distance. White et al. propose the use of a weighing factor (wi) that represents the non-conservative transport of a pollutant: wi = where α = decay constant, and d = flow path length between the centre of the grid cells to the monitoring station. The project team used the weight proposed by White et al. to calculate the AAC, where: AAC =
(Equation 5)
The decay constant α was estimated from data that show amelioration discharges from STPs increase exponentially in concentration with increasing distance downstream due to additional loads entering the mainstream. Data are from the Beaudesert STP located 105km adopted middle thread distance (AMTD) (Semple and Dunlop 1998). The resulting α was 0.0581 and this was used as the coefficient for all management actions as a first approximation. The process of dilution was translated into proportion decay simply by multiplying the NAC with wi. As stated above, no process-oriented model was applied in version 2, which did not allow for the analysis of processes such as residence time and flow speeds that are necessary to more adequately calculate decay constants. As a result, NAC and AAC values should be used only to compare results qualitatively in the context of regional strategies for MSE. Further work is definitely necessary to both calibrate and validate NAC and AAC values for different management treatments, and these values can be updated in the management model as new information is made available. In version 3 we will develop more robust values of NAC and AAC by deriving the coefficients through calibrating outputs from catchment and receiving water models with EHI and/or EHMP report card scores. At the moment both AAC and NAC provide relative measures of effectiveness of management actions, using weights that allow management actions to be more effective the closer they are applied to monitoring stations. The list of management actions, costs and NAC differ according to the project configuration chosen by the user. The AAC calculation remains the same for all management actions. The list of management actions, NAC, time lags, costs and references used for the “literaturebased configuration” is provided in Table 4. The list of management actions, NAC, time lags, costs and references used for the “expert-based configuration” project is provided in Table 5.
57
3.2
Scoping the management actions used in the MSE Tool
3.2.1
Rural management Actions
Only about one quarter of the original native vegetation in SEQ remain intact, and often much less along
stream and river corridors in some catchments. Increases in sediment and nutrient runoff are a result of extensive clearing in upper parts of the catchments, cultivation on the floodplains, over-grazing, and local development of urban centres (Bunn et al. 2010; Olley et al. 2000). Modelling suggests that 60 percent of the sediments in Moreton Bay originate from 30 percent of the catchments (mostly Brisbane River and Logan River catchments) (Bunn et al. 2007; Douglas et al. 2003). Hillslope erosion is likely to be a major process only in steeper catchments or in areas with intensively cropped floodplains. At the local level, channel erosion (gully and stream bank erosion) is clearly the dominant source of sediments entering SEQ waterways (Bunn et al. 2010; Caitcheon et al. 2001; Olley et al. 2000; Olley et al. 2009; Sarker et al. 2008). Erosion intensifies in major rain events, where heavier sediments can travel short distances initially clogging local streambeds. Heavier sediment moves slowly downstream and lighter sediments travel great distances throughout the system in subsequent rain events, reaching estuaries and Moreton Bay (Sarker et al. 2008). Channel erosion is best managed by preventing stock access to streams, protecting vegetation cover in areas prone to erosion, re-vegetating bare banks to reduce subsurface seepage and the consequent amount of sediments reaching the waterways (Bunn et al. 2007; Bunn et al. 2010; Caitcheon et al. 2001; Olley et al. 2009). Hillslope erosion is best managed through revegetation, which promotes a physical barrier to slow the overland flow enhancing sediment deposition before reaching waterways (Argent, 2008: 32). Vegetation cover also helps maintain soil structure by promoting deposition of eroded sediment before it reaches the stream (Bunn et al. 2007; Caitcheon et al. 2001). Riparian vegetation buffers play an important environmental role in reducing TN inputs to streams by reducing overland flow through infiltration into storage within the buffer (Argent 2008; Bunn et al. 2007). To address the issues above, the project team created the following management actions that can be applied in rural areas (details are presented in Table 4): • Rural stormwater (Stormwater Quality Improvement Devices – SQIDs are used to reduce sediment and nutrient runoff to waterways). • Rural riparian revegetation. • Best farming practices (fencing).
3.2.2
Urban management actions
The main drivers affecting water quality in urban areas are stormwater, especially in greenfield areas during the initial urban development phase, but also in already established urban areas (operational phase). During the development phase the land is in transition and there is a peak in sediment and nutrient runoff measured at waterways close to the development (Weber 2008). During the operational phase there is an increase in impervious surface areas associated with urbanisation, also increasing flows of urban stormwater to the waterways. In addition, during the operation phase of urban development, there is more pressure at existing sewage treatment plants (STP) due to the increase of sewage connections to the grid. This often requires upgrades on STP and/or construction of new STP. In order to address these issues, the following management actions were included in the catchment-to-coast MSE Tool (Table 4):
58
•
•
•
Best urban design (WSUD – Water Sensitive Urban Design): WSUD is a planning and design approach that aims to ensure urban development is sensitive to natural hydrological and ecological cycles by conserving water supplies, minimising wastewater, and managing stormwater quality and flows (Water by Design 2009). Examples of WSUD apparatus are rainwater tanks and double reticulation. Urban stormwater (grassed riparian buffers): grassed riparian buffers in urban areas are used to increase pervious surface area, which reduce the effects of urban stormwater by maintaining higher soil infiltration and storage capacity (Goonetilleke et al. 2003). STP upgrade: reduces the TN concentration to 2 mg/L, assuming all STPs deliver 5 mg/L of TN at the outfall.
Management actions to be considered in future versions of the catchment-tocoast MSE Tool During the expert opinion interviews to inform the management model (Chapter 1.3.2; Table 1) experts identified contour tillage as a management action that can help reduce soil loss and associated negative effects on the health of waterways. The literature also indicates that nitrogen runoff from fertilisers, stock dung and natural causes is a significant problem throughout SEQ’s rural lands, and contributes over 60% of the region's total nitrogen concentrations (with the balance coming from point sources and diffuse urban sources) (Sarker et al. 2008). Experts also regarded “stream/gully rehabilitation” and “water recycling” as important management actions that should be included in the Tool. Gully/Stream rehabilitation will deal specifically with the main sources of sediments reaching Moreton Bay. Recycled water can be used for irrigation of schools, top up lakes for noncontact recreation and supplement irrigation of public spaces (parklands, sporting fields)(Gardner 2003). It can also improve the effectiveness of WSUD through treatment of water from WSUD devices and houses (Brodie 2009). Investigating of some of these management actions has commenced (Table 5) by including them as a separate project configuration in the catchment-to-coast MSE Tool (“expert-based configuration”). However, there was insufficient time to review their associated costs and NACs in the current version of the Tool.
59
Table 4. Management actions, Nominal Abatement Coefficients (NAC), time lags, costs and references used to calculate costs and NAC for the “literature-based configuration” project.
Management
Detailed
Actions and
management
land-use
actions
Rural
SQID
NAC TN 0.8
Days till
Days till
Capital
Operating
first effect
full effect
cost
cost
($)/km
TB 0.8
360
30
2
$1,000
Stormwater
Equation
Notes
References
$100 (per
year 1 = install +
SQID = Stormwater Quality Improvement Device
pers. comm.
year)
operating cost,
($/time)
Sqid Pty Ltd
subsequent years =
(rural)
operating cost only
*Riparian
0.4
0.7
180
720
$25,000
$0
year 1 = install cost
revegetation
which includes first
(available in al
year maintenance;
land uses)
subsequent years no
Cost from rural riparian revegetation
(Alam et al. 2006a)
charge Best Farming
year 1 = install +
(Rolfe et al.
Practices
operating cost,
2004)
(rural)
subsequent years=
Fencing
0.7
0.7
180
720
$14,000
$1,000
operating cost STP upgrade (STP)
Deliver TN
0.6
0.6
1080
360
concentrations
$9,350,00
$216,000
0
< 2 mg/L in
(per year)
the outfall Best Urban
WSUD
Grassed
Stormwater
riparian buffer
(urban)
60
Upgrade Burpengary East STP (capacity
(BDA Group
operating cost,
11.6ML/day) from 4.8mg/L to 2mg/L for TN.
2005)
subsequent years = operating cost
0.2
0.8
360
720
Design (urban) Urban
year 1 = install +
0.5
0.5
180
180
$3,825,00
Per 25
years 1- 3 split install
Cost is for residential Greenfield flat topography.
(Water by
0
years
cost over each years
Yearly cost $1530/ha/year on a 25 year lifecycle.
Design 2009)
$5,000
$0
year 1 = install cost,
Based on grassed re-vegetation, a buffer strip
(Alam et al.
subsequent years = no
along one side of a waterway, presuming 1km of
cost
waterway per km2 = $5,600.
2006a)
Table 5. Management actions, Nominal Abatement Coefficients (NAC), time lags, costs and references used to calculate costs and NAC for “expert-based configuration” project.
Management
Examples of
Actions
detailed management
NAC TN
TB
Days
Days till
Capital cost
till first
full
($)/km
effect
effect
360
1080
2
Operating
Equation
Notes
References
cost ($/time)
actions
Gully / Channel rehabilitation
Water troughs
0.7
0.7
Porous weirs
$74,000
$1,000 (per
year 1 = install + operating
(Olley et al.
(per linear
year)
cost, subsequent years =
2009)
km)
operating cost only
Fencing Revegetation
Vegetation used
year 1 = install cost which
Cost from rural riparian revegetation from
(Alam et al.
to stabilise
includes first year
Alam et al., 2006
2006a)
gullies/banks
maintenance; subsequent
0.4
0.7
180
720
$25,000
$0
years no charge Best Farming
Minimum tillage
0.3
0.3
180
720
Practices
$20,000/k
$0
m2
year 1 = install cost;
Based on expert opinion. Cost not verified in
subsequent years no
the literature.
charge STP upgrade
Deliver TN
0.6
0.6
1080
360
$9,350,000
$216,000
concentrations of 2mg/L in the
year 1 = install + operating
Upgrade Burpengary East STP (capacity
(BDA Group
cost, subsequent years =
11.6ML/day) from 4.8mg/L to 2mg/L for TN
2005)
(per year)
operating cost
Per 25 years
year 1-yr 3 split install cost
Cost is for residential Greenfield flat
(Water by
over each years
topography. Yearly cost $1530/ha/year on a
Design 2009)
outfall Best Urban
WSUD
0.7
0.5
360
720
$1,912,500
Design
25 year lifecycle. We use of it: $765.00 after comments from users. Urban
Grassed riparian
Stormwater
buffer
0.5
0.4
180
180
$5000
$0
year 1 = install cost,
Based on grassed re-vegetation, a buffer
(Alam et al.
subsequent years = no cost
strip along one side of a waterway, assuming
2006a)
1km of waterway per km2 = $5,600.
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4.
TRIPLE BOTTOM LINE ASSESSMENT
The MSE Tool uses a triple bottom line approach, which serves to highlight the very different concerns involved with the environmental, social and economic aspects of SEQ (Davies 2004: 298). The indicators used in the tool are the following: • The report card model (Chapter 4.1), which calculates report card grades for freshwater, estuarine and marine regions of SEQ. It uses outputs form the biophysical model (Chapter 1), and generates scores ranging from A (excellent) to F (fail). • The social perception model (Chapter 4.2), which uses outputs from the report card to calculate social perception scores (-4 to 4) of 22 groups in SEQ. • The economic model (Chapter 4.3), which consists of three sub-models: • Costs of management actions, which calculates estimated costs of installation and ongoing yearly maintenance for selected management actions using a 1 km2 grid. • Economic benefits model, which calculate the economic benefit associated to improvements in water quality. • Cost effectiveness, which calculates costs per improvements in water quality. The TBL approach enables users to evaluate trade-offs of different management strategies implemented at local and regional scales.
4.1
Report Card Model
The report card is an important tool for communicating to local and State government agencies, industry and the community how well the Healthy Waterways and Partners are currently protecting and improving the health of SEQ’s waterways and Moreton Bay (South East Queensland Healthy Waterways Partnership 2007b). The report card provides grades that range from A to F for SEQ’s waterways (Table 6), and are part of the EHMP. Report card grades are calculated for the freshwater, estuarine and marine regions in SEQ (Appendix 3). In this chapter we describe how the report card grades are calculated in the MSE Tool.
4.1.1
Freshwater report card
Calculation of freshwater report card grades are based on certain measurable quantities, such as land-use and riparian revegetation, which would be affected by management actions. In a simulation, health scores are predicted for each station, and these scores are used to compute the report card grades for each upper catchment region. Then, the historical indicator data set is used as a population from which to draw samples. Given a station with health score , we look in the historical data set for a station-year combination whose annual health value is closest to this score. Then the indicator data from that historical record is used for the station . The indicator data can then be used to drive conditions at the downstream estuary stations. For details on calculation of freshwater report cards, see Chapter 2.1).
63
Table 6. Meaning of Report Card Grades.
Grade
Meaning
A
Excellent
B
Good
C
Fair
D
Poor
F
Fail
4.1.2
Estuarine and marine waterways report card
Report Card Grades are calculated by combining the outcomes of two separate data analysis processes and expert assessment of the data outputs. The most important process (accounting for approximately 70% of the grade) is the Ecosystem Health Index (EHI) value for each reporting region, a process applied to the indicators that are measured at the estuarine and marine monitoring sites. The data collected from these sites can be analysed and presented spatially. The second process (accounting for approximately 30% of the grade) is to calculate a Biological Health Rating (BHR), a process that incorporates indicators that do not conform to spatial analysis and visualisation (South East Queensland Healthy Waterways Partnership 2007b). The report card model of the MSE Tool calculates only the EHI and does not attempt to calculate the BHR as it depends on expert opinion. The Report Card model of the MSE Tool is implemented by using calculation of the estuarine and marine report card grades provided in EHMP (2008) and Pantus and Dennison (2005). Compliance thresholds for the water quality variables in the estuaries and marine regions are presented in Tables 8 and 9. Threshold values to generate report card scores in estuaries and marine regions are presented in Tables 10 and 11. Moreton Bay has been divided into 8 reporting zones. These reporting zones were differentiated by water depth and predictions of water residence times. The estuarine systems have been divided into enclosed coastal/lower estuarine, mid estuarine and upper estuarine areas, again to reflect the residence times and mixing rates between waters types.
64
Table 7. Compliance thresholds for water quality in Estuaries
Water types
Chla (µg/L)
TN (µg/L)
TP (µg/L)
Turbidity (NTU)
DO (% sat.)
All Upper
4
0
1.5
>4
0
3.5
>4
0
1.2
>4
0
Coastal Pumicestone Passage Outer – Mid-Estuarine Deception
Western Bays –
Bramble
Enclosed Coastal
Waterloo Central Bay
Central Bay – Open Coastal
Eastern Bay Northern
Eastern Northern – Open Coastal
Central
Eastern Central – Open Coastal
Southern
Eastern Southern – Open Coastal
Eastern Banks
Eastern Central – Open Coastal
Southern Bay
Southern Moreton Bay –
65
Marine
Water types
Reporting
Chla
TN
Secchi
δ15N
(µg/L)
(µg/L)
(m)
(‰)
Regions
Lyngbya (% cover)
Enclosed Coastal Broadwater – Enclosed
Broadwater
< 2.5
< 0.19
> 1.5
>4
0
5
>4
0
Coastal Offshore
Open Coastal
Seaway
The eight Moreton Bay reporting regions are the following:
• • • •
Deception Bay Central Bay Eastern Bay Bramble Bay
• • • •
Eastern Banks Waterloo Bay Southern Bay Broadwater
The 19 estuarine reporting regions are the following:
• • • • • • • • • •
Maroochy Estuary Noosa Estuary Mooloolah Estuary Caboolture Estuary Pine Estuary Cabbage Tree Estuary Tingalpa Estuary Eprapah Estuary Brisbane Estuary Pumicestone Passage
• • • • • • • • •
Oxley Estuary Bremmer Estuay Logan Estuary Albert Estuary Pimpama Estuary Coomera Estuary Nerang Estuary Tallebudgera Estuary Currumbin Estuary
Generating an Ecosystem Health Index (EHI) The Ecosystem Health Index is a measure of how much of a waterway’s area complies with the defined water quality objectives as assigned in the Queensland Water Quality (QWQ) Guidelines (EPA 2006). Five indicators are used for determining EHIs in Moreton Bay (Table 9) and five in the estuaries (Table 8). The indicators chosen are those less correlated so as to limit bias in the EHI calculation. Three key stages are involved in the generation of EHIs: • Annual medians of each of the indicators used in the EHI are calculated for each of the 253 monthly sites. Using these values, a spatial interpolation is calculated for each indicator based on Pantus and Dennison (2005). • Compliance scores for all of the EHI indicators in each of the systems are generated using the QWQ Guidelines (EPA, 2006). Values of < 1 showing compliance and values of > 1 showing non-compliance.
66
•
An EHI is then calculated for the entire area of a reporting zone by averaging the compliance of all the indicators. The resulting EHI values are then mapped.
We use the method described in Pantus and Dennison (2005) to generate the estuary and marine report card scores in the MSE Tool. To calculate the EHI value of a region R, Pantus and Dennison apply a city block dissimilarity measure (k=1) and use area (d=2) as the spatial extent:
(Equation 6)
where
is the total area of the region evaluated for indicator I, and Ap is the area of the
term represents the
spatial prediction unit p. The
proportion of a region R complying with the reference value for indicator I. Table 9. Estuary thresholds to generate report card scores.
Estuary Thresholds
Report Card Grade
< 0.25
F
> 0.25 < 0.36
D
> 0.36 < 0.55
C
> 0.55 < 0.90
B
> 0.90
A
67
Table 10. Bay thresholds to generate report card scores
Moreton Bay Thresholds
Report Card Grade
0.5 0.7 0.8 0.9
A
4.2
Social perception model
The EHMP and associated report card grades do not include any information or analysis of the social and economic connections to waterways health; a measure of the ability of an ecosystem to be productive, its biological diversity and its resilience to change (South East Queensland Healthy Waterways Partnership 2007c: 7). The objective of the social perception model is to assist managers to evaluate possible changes in social perception associated to catchment strategies.
4.2.1
Approach
In order to get an indication of the breadth and range of social value indicators, we used an expert panel and assumed that it was representative of the wider community. The panel members were selected so that each of the social groups was represented by at least two of the experts. The expert panel was convened in February 2010 in a workshop to collectively elicit their social perceptions about changes in report card grades. The panel was provided with the Healthy Waterways definition of each report card grade (Table 11) and a preliminary list of the social value indicators and the social groups (see below). Expert panel members (n=10) were asked to complete an assessment of baseline social value indicators for each of the report card grades. Each panel member scored their responses on an ordinal scale from positive four to negative four for each social group they represented for all social value indicators, and for each report card grade (see Table 11 for details). The maximum score of four was reserved for the most extreme positive impact and negative four for the most extreme negative impact. Zero scores were used to indicate that the social value indicator was not applicable for that particular social group. We encouraged panel members to indicate either positive or negative impacts and to minimise the use of zeros. Once scoring was complete, the panel members were asked to self-judge their level of competency for scoring each of the social groups on a scale from one to five. It is important to note that the social group results are imagined by experts, not truly representative of those groups.
Social value indicators typology The social and ecological value indicators are categories under the headings: water, health, biodiversity, ecosystem services and social capital (Table 12). During the workshop the expert panel discussed this initial set of value indicators where some social values were added or altered until general consensus was achieved. This exercise ensured that each social value was understood by the panel for the assessment exercise.
68
Table 11. Environmental goals for freshwater, estuarine and marine systems in the Ecosystem Health Report Card grade system, report card grades and the description of each of the grades.
Ecosystem
Environmental Goals
Grade
Standard
Description
Freshwater
Protect/restore riparian
A
Excellent
Conditions meet all set
vegetation and habitat
ecosystem health value indicators, all key processes are
Protect fish and
functional and all critical
macroinvertebrates
habitats are in near pristine condition.
Minimise nuisance algal blooms and growth of aquatic weeds Minimise sediments and nutrients Estuarine
Protect/restore estuarine
B
Good
Conditions meet all set
habitats; seagrass,
ecosystem health value
mangroves, saltmarsh
indicators in most of the
and riparian vegetations
reporting region, most key processes are functional and
Protect fish and
most critical habitats are intact.
macroinvertebrates Minimise nuisance algal blooms and growth of aquatic weeds Minimise sediments and nutrients Marine
Protect/restore marine
C
Fair
Conditions meet some of the set
habitats; seagrass,
ecosystem health value
mangroves, saltmarsh
indicators in most of the
and riparian vegetations
reporting region, some key processes are functional and
Protect fish and
some critical habitats are
macroinvertebrates
impacted.
Minimise nuisance algal blooms Minimise sediments and nutrients D
Poor
Conditions are unlikely to meet set ecosystem health value indicators in most of the reporting region, many key processes are not functional and many critical habitats are impacted.
F
Fail
Conditions do not meet set ecosystem health value indicators, most key processes and are not functional and most critical habitats are severely impacted.
69
Table 12. Social value indicators considered by the expert panel in the scoring exercise
Water
Health
Habitat
Ecosystem Services
Social capital
Clarity
Food health
Estuaries
Nutrient cycling
Aesthetics
Avail for use
Water health
Coastal
Waste cycling
Social interaction
Flows
Wetlands
Stream/coast buffering
Lifestyle & wellbeing
Pollutant free
Streams
Artificial buffering
Cultural & spiritual
Colour/smell
Iconic species
Existence & intrinsic
free Bequest & Endowment
Social groups A list of social groups was presented to the expert panel during the workshop. The list was thoroughly reviewed and discussed and several social groups were added or altered during the exercise until general consensus was achieved. This ensured that each social group was understood by the panel prior to the scoring process. Social groups are further grouped by the following categories: residents, tourists, fishers, indigenous, recreation, business and interest groups (Table 13). Table 13. Social groups defined by the expert panel – each column lists a different category
Residents
Tourists
Fishers
Indigenous
Recreational
Business
Interest Groups
Urban
Domestic
Recreational
Local
General
Tourism /
NGOs /
recreational
Environmental
(long-time)
associated Urban (short-time)
International
Commercial
Non-local
Swimmers
connection
Development
Scientific /
related
research
to land/sea Rural (long-
Boaters
time) Rural
Agriculture / aquaculture
Bird-watchers
(short-time) Coastal i.e.
Scuba diving /
within 1km
snorkelling
coastline) includes islanders) Water sports
4.2.2 Calculating the social perception index The pattern of response declined alongside decreasing status but with notably declines in the lowest grades (D and F) (Figure 15). The rates of change differed between social groups. These large declines show a change from relative ambivalence to concern, alerting catchment mangers to the need to maintain relatively positive report card grades (A-C).
70
Figure 17. Perceptions (median) and maximum and minimum scores of Water Clarity value, from two different social groups deteriorate when Report Card scores shift from A to F. (A) long-term urban residents and (B) Business – Tourism/Recreational related.
The median scores from the expert panel for each social indicator/social group grade were calculated and the social perception model of these expert medians directly, as follows: , where
uses a look-up table
(Equation 7)
- Social perception grade for social indicator m and social group n. m - social indicator matrix row index n - social group matrix column index RCG – report card grade
4.2.3 Conclusions This approach is novel in both its direct modelling application and the pragmatic way in which the baseline scores are displayed in the MSE Tool UI. Managers are able to evaluate plausible social impacts associated with their decisions, and are able to follow changes in social perception over time. This is particularly useful because some management actions are likely to provide different social perception in the short- and long-term. We expect to refine the ‘baseline’ value indicators in future versions of the MSE Tool by including data from a much wider survey with public respondents, rather than experts, drawn from the demographics across the SEQ catchments.
4.3
Economic model component
Cost-effectiveness analysis (CEA) is a branch of economics concerned with the evaluation of alternative courses of action towards a pre-defined target. The approach focuses on comparing the available options in terms of their relative costs, and will usually aim to identify the least-cost option (Holland et al. 2010). Despite the
71
potentially high costs of interventions designed to improve the quality of water from catchments to coasts, there have been only few studies attempting to apply costeffectiveness analysis to the evaluation of alternative strategies in integrated ecologicaleconomic frameworks (Roebeling et al. 2009). An advantage of the approach is that it does not require monetary evaluation of the benefits associated to the alternatives considered. It is thus a useful way to combine economic analysis with other criteria where the analyst cannot, or does not, want to assess the benefits associated with alternatives. Application of the CEA methodology requires a definition of the following: • alternative courses of action that are to be compared, and their associated costs; • identification of the target towards which effectiveness of interventions is being assessed; and • the expected effects of alternative interventions on the target. This was done at the scale of the SEQ region, focusing on the major types of strategies that are currently being used to improve water quality at the catchment scale. Report card grades (Chapter 4.1) are used as inputs to the economic model.
4.3.1
Management interventions and associated costs
The management actions considered in the MSE Tool differ according to land-use areas (Table 4). The selection of management actions included in the Tool was based on a literature review where a batch of selected options was chosen and validated by the Healthy Waterways in collaboration with the management bodies directly concerned. Rural and urban diffuse sources, and point sources are the three most commonly accepted issues affecting water quality (Alam et al. 2006b; Robinson et al. 2008). For rural diffuse sources, the main options include riparian revegetation along the waterways, treating stormwater runoff using low-cost sediment traps, and using best farming practices, specifically fencing along waterways to keep livestock away from streambanks. This increases streambank stability and lowers sediment and nutrient loadings (Rolfe et al. 2004). For urban diffuse sources, two alternatives are available: a) best urban design in areas where new urban development is taking place (Water by Design 2009), and b) urban stormwater management via grassed riparian buffers along waterways. These buffers are effective in trapping and reducing sediment runoff and improving water quality (Rolfe et al. 2004; Taylor 2005). For point sources, the option sewage treatment plant (STP) upgrade was chosen, which involves infrastructure modifications to reduce total nitrogen and total phosphorous concentrations (BDA Group 2005). For details of what each management action aims to achieve and related references, see Chapter 3.2. Default financial costs of management actions used in the model were calculated from known costs of the most common categories of actions taken to maintain or improve the quality of freshwater in the catchment areas. Information on these costs was obtained from several studies focused on SEQ in recent years (see Alam et al. 2006a; Olley et al. 2009; Robinson et al. 2008; Rolfe et al. 2005; Windle and Rolfe 2006). Average costs from these studies were then implemented as default cost parameters in the model. Given the nature of the management interventions considered, and the scale of the analysis, opportunity costs associated with the adoption of these interventions were assumed inconsequential and ignored. For each intervention type, two categories of costs were considered: establishment costs and annual maintenance costs (Table 4). Total and individual management action costs are displayed in the UI. Default parameters included in the model are given as follows (see Table 4):
72
•
•
•
•
Rural riparian revegetation has installation costs of $25,000 per linear km in each 1 km2 grid square and allows for a 10 m strip of vegetation planted on each side of the stream i.e. 20 m wide by 1 km long (Alam et al. 2006b; Olley et al. 2009) and annual maintenance costs of $1000 per km; Rural stormwater has installation cost of $1,000 per km and annual maintenance costs of $100 per km. The SQID (Stormwater Quality Improvement Device), a gross pollutant trap, works by collecting heavy sediments such as soil and litter from stormwater runoff (pers. Com. SQID Pty Ltd); Best farming practices (fencing) has installation cost of $14,000 per km and annual maintenance costs of $1000 per km (Olley et al. 2009; Rolfe et al. 2004). Urban stormwater - grassed riparian buffers have installation costs of $5,000 per km (Alam et al. 2006b) and there are no annual maintenance costs (as installation costs include the first years maintenance costs, and ongoing maintenance is not required).
Two capital works options were provided (costs were based on specific projects at the Caboolture catchment): • Best Urban Design is costed with a default value of $3,825,000 total with the cost in the model spread over two years (Water by Design 2009). • STP upgrade with installation cost at $9,120,000 and annual maintenance costs $207,000 (BDA Group 2005). Costs for STP upgrades are based on tertiary filtration to reduce nitrogen. The capacity of the STP and the target concentration of TN at the outfall are necessary to calculate installation and maintenance costs. We used Burpengary East STP with current capacity of 11.6ML/day and a reduction from 4.8mg/L to 2mg/L for total nitrogen at the outfall to calculate the default costs for STPs in SEQ.
Installation and operational costs are calculated every time the user implements each management action. Costs are displayed instantaneously in the UI and the user is able to remove the action if, for example, the budget is overspent. After locking in to the actions and running the model, a summary table is displayed in the economic model assessment tab (Appendix 4)
4.3.2 Defining effectiveness A cost-effectiveness approach was adopted in the TBL assessment part of the MSE Tool to enable users to assess and compare the relative merits of alternative intervention strategies with respect to the allocation of costs and to the distribution of benefits associated with interventions (Ledoux and Turner 2002; Roebeling et al. 2009). The cost effectiveness model uses outputs from the report card model to assess the overall status of water quality in SEQ waterways (Chapters 1 and 4.1). The project team made assumptions about the average improvement in overall water quality associated with changes in scores (Table 14). A piecewise function was then used to qualitatively capture the change in global water quality that could be associated with the shift from one grade to another. The assumptions underlying these steps were validated during the open-loop workshop (Chapter Error! Reference source not found.).
73
Table 14. The meanings attributed to each of the Ecosystem Health report card grades (left) and assumed changes in overall water quality associated with improvements in the scores as calculated by the report card system (right).
Percent in water quality index B to A: 10 percent C to B: 15 percent D to C: 20 percent F to D: 25 percent F
Partial Monetary Benefits per household due to water quality improvements in SEQ A partial monetary measure of benefits associated with improvement in water quality was included in the analysis. Windle and Rolfe (2006) concluded that non-use values were more important than use values in SEQ, and found the willingness to pay (WTP) per household for a one percent increase in water quality for SEQ to be $3.42 using choice modelling. For rivers, the benefits to individual households of increasing water quality was therefore estimated by multiplying $3.42 by marginal changes in the overall quality of water as described above. As a marginal value, it is only applicable for relatively small improvements, and may overstate benefits of large improvements in water quality (see Parsons et al. 2003). However, the values are robustly derived (Windle and Rolfe 2006) and have been widely used in the region. Given that they are considered directly proportional to changes in water quality, they are used here as proxies of the latter in the cost-effectiveness framework. However, the fact that they are measured in monetary terms allows assessment of the partial benefits at the overall scale of SEQ via aggregation across catchments. We indexed this benefit amount, which was derived by surveys in 2004 up to 2010 based on the ABARE inflation figures per annum (the inflation figure used for 2008/9 and 2009/10 is 3.0%). This increases the value of benefit associated with a 1% increase in water value to $4.16 per household for the 2010 year All calculations are based on individual catchments and require data on the number of households in the catchment. Number of households per catchment was calculated from the population density prescribed in the model, based on land use. The population density for rural land use is 30 people per rural 1km2 grid square, and 2000 people per urban 1km2. These figures were checked against population estimates provided by the SEQ Healthy Waterways, which were prepared by the Office of Economic and Statistical Research in the Queensland Department of Treasury. The overall population figures in the MSE Tool fit relatively closely with the projected population for 2008 (although some smaller catchments may be slightly overstated and the largest catchments are slightly understated. The population figures are then divided by 2.5 to give the number of households based on Human settlements in SEQ for 2006 and this number is projected to stay stable until 2012 and then reduce slightly to 2.4 for the period 2016-2021 (EPA 2008). It should be noted that population growth over time is not included in this phase of the software development; expected population growth will be included in future versions of the MSE Tool.
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The benefit per household due to water quality improvements are calculated in the economic model as follows. Let B be the benefit in $ per household per annum per 1 percent improvement in water quality per catchment (from Windle and Rolfe 2006) region and B is a function of change in water quality (WQ):
, where
(Equation 8)
≡ Constant of the benefit per household in SEQ per 1 percent improvement in water quality ($4.16). Change in water quality ( , as per report card grade; Table 14) is calculated through a piecewise function, where:
(Equation 9)
With any change between more than one step (e.g. from C to A) assumed to equate aggregate change between intermediate steps (e.g. from C to B and from B to A). Change in WQ is calculated as a function of management actions plus a random effect included in the water quality indicators (see Chapters 2.1 and 2.3):
, where
(Equation 10)
A ≡ management action
ε ≡ randomness based on I ≡ water quality indicators measured at EHMP stations, used to calculate report card grades. Therefore, the benefit per household is an implicit function of management actions:
(Equation 11)
Calculating Cost Effectiveness The Cost effectiveness (CE) was defined in terms of the capacity for management actions to achieve an improvement in the overall quality of water at the scale of
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catchments. Cost (C) is a function of management action (A) costs based on installation and maintenance costs, where: (Equation 12)
, where
(Equation 13)
H=number of household in the region
Discounting As the scenarios available in the MSE Tool have a time frame of 30 years, the question of discounting of future flows of costs and benefits needs to be addressed. Adequate discounting approaches to the evaluation of environmental policies have been the focus of a growing body of literature (Ledoux and Turner 2002). The practice of discounting of future values relates to the observation that future costs and benefits are usually valued less than the equivalent costs and benefits occurring now. The model allows for both current value and discounted value estimates of costs and benefits to be calculated. A 5.5% default discount rate was applied, based on the value used by the Queensland Treasury for the evaluation of public infrastructure projects (Binney 2010). The reference period for discounting was considered to be the initial simulation year in the projection period, as it was assumed that users of the tool would be exploring possible futures with this initial period as their reference point. The present value of a future cost or benefit A is calculated as follows: , where
(Equation 14)
- Discounted value of cost or benefit A at time t. r – Discount rate (constant, 5.5%p.a.). t – Time in years at which cost or benefit A occurs. This chapter finishes the description of the MSE Tool. Appendix 4 contains a tutorial for the use of the Tool.
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5.
TRIAL MSE SIMULATION RESULTS FOR BRISBANE, LOGAN AND ALBERT CATCHMENTS
5.1
Scoping the catchment management objectives
We tested 3 management strategies plus a “do nothing strategy” (no action implemented) considering the Logan (92% rural, 4.5% natural and 3.5% urban), Albert (86.5% rural, 12% natural and 1.5% urban) and Brisbane (87.4% rural, 7.4% natural and 5.2% urban) catchments altogether. The budget used for catchment strategies were defined based on extrapolation of the strategies adopted by participants of the social experiment (Chapter 1.3.3) and are depicted in Table 17. Strategy A is characterised with low investments mainly focused in rural areas. Strategy B had mixed investments in rural and urban actions and Strategy C focused mainly in urban actions. A tutorial of the MSE Tool and a glossary of terms are presented in Appendices 4 and 5, respectively. Under an adaptive management context, the test of management strategies makes sense only when objectives are made explicit (Chapter 1.1.3). Therefore, after the budget is specified the next step is to define environmental, social and economic goals all three strategies aimed to achieve. These are described as follows: • Improve water quality (report card grades) in the freshwater and estuaries of the Logan, Albert and Brisbane catchments to achieve and maintain at least B report card grades. • Improve water quality at Bramble Bay and Central Bay marine regions to achieve and maintain at least B report card grades. • Improve social perceptions of rural residents, fishers and international tourists at the three estuaries. • Be as cost effective as possible (i.e. spend the least money to produce the improvements in water quality above).
5.2
Evaluating catchment management strategies
The MSE Tool allows the user to make multiple runs and compare the results between them for each strategy. In each run the initial seed for the biophysical models is modified in order to evaluate if the strategy is robust with changes in environmental circumstances. It shows how the strategy performs under different environmental conditions. However, for the sake of simplicity to visualise the results we present the evaluation of management strategies using a single run. In the example illustrated in this section, three alternative strategies were simulated, corresponding to different intervention patterns and associated budgets which were observed in one of the experiments. The strategies presented below are illustrative only, and provide qualitative indication about trends in environmental, social and economic responses according to these theoretical region-wide strategies implemented in catchments. The results are presented to illustrate the possibilities of the tool. The model allows users to compare the relative directions in which the water catchment system evolves depending on alternative courses of management action.
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Table 15. Specification of the budget (in “virtual” million dollars) for each management strategy tested in the MSE Tool.
Strategy A Period
Best
Best
Rural Riparian
Rural
STP
Urban
(Years)
Farming
Urban
Revegetation
Stormwater
Upgrades
Stormwater
Practices
Design
2009 - 2013 2014 - 2018 2019 - 2023 2024 - 2028 2029-2033
Total
2.0
-
33.0
2.1
126.0
1.3
164.4
0.6
283.1
-
0.7
13.3
-
297.7
0.6
-
22.7
0.7
13.3
-
37.3
0.6
-
-
0.7
13.3
-
14.6
26.2
0.7
13.3
-
40.9
Total
560.3
0.6
-
Strategy B Period
Best
Best
Rural Riparian
Rural
STP
Urban
(Years)
Farming
Urban
Revegetation
Stormwater
Upgrades
Stormwater
Practices
Design
2009 - 2013
21.2
283.1
33.7
1.5
-
1.3
340.8
2014 - 2018
6.9
377.4
0.4
0.6
-
1.3
386.6
2019 - 2023
6.2
141.5
-
0.6
118.0
1.0
267.3
2024 - 2028
6.2
283.1
-
0.7
14.4
-
304.4
2029-2033
14.8
70.7
4.6
0.8
13.3
1.3
105.5
Total
1,404.6
Total
Total
Strategy C Period
Best
Best
Rural
Rural
STP
Urban
(Years)
Farming
Urban
Riparian
Stormwater
Upgrades
Stormwater
Practices
Design
Revegetation
2009 - 2013
-
377.4
28.7
-
-
-
406.1
2014 - 2018
-
330.3
-
-
-
-
330.3
2019 - 2023
5.5
165.1
27.1
0.2
115.3
-
313.2
2024 - 2028
1.7
235.9
11.6
0.1
13.3
-
262.6
2029-2033
15.2
259.4
4.6
0.7
26.6
1.3
307.8
Total
1,620.0
5.2.1
Report card grades
Freshwater grades Strategies A and C produced improvements in report card grades in the Mid Brisbane catchment (Figure 18) in 2018 and 2029, respectively. In the other catchments the
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strategies did not produce improvements in grades when compared to “no management action” strategy.
Figure 18. Freshwater report card grades for Strategies A, B and C for the Mid Brisbane catchment.
Estuarine grades Logan: Strategy B produced the best overall improvements in report card grades in the Logan estuary after 2028. Strategies A and C were similar in their intermediate performance with relatively higher performance of Strategy A after 2028. “No management action” produced the worse results for all three catchments (Figure 19). Albert: Strategy B produced the best results for report card grades as soon as 2012 and “no management actions” produced the worst results. Strategies A and C produced intermediate results, with relative improvements in grades after 2028, when compared to the “no management action” strategy (Figure 20).
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Figure 19. Estuarine report card grades for Strategies A, B and C for the Logan estuary.
Figure 20. Estuarine report card grades for Strategies A, B and C for the Albert estuary.
Brisbane: Strategies A and B produced similar report card results and produced better results when compared to the other 2 strategies. Strategy C followed the same trend of “no management action” until 2029 when report card grades improved substantially in the estuary (Figure 21).
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Figure 21. Estuarine report card grades for Strategies A, B and C for the Brisbane estuary.
Marine grades Strategy A produced slightly better report card grades when compared to “no management action”, but overall none of the strategies produced major improvements in Bramble or Central Bay report card grades (Figure 22).
Figure 22. Marine report card grades for Strategies A, B and C for Bramble Bay.
Strategy A produced a positive trend in report card grades for the Mid Brisbane freshwater report card grades coincided with the period of highest investment (20142018) in the catchment (Table 17). Allocation of funding seemed to be appropriate to maintain higher grades in the Mid Brisbane catchment, Brisbane and Logan estuaries even with overall reduction in investments in subsequent years. Improvements in estuarine grades in the Brisbane and Logan estuaries were maintained until the end of the run. The strategy produced little improvements in marine grades.
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In strategy B the heavy investments in actions to ameliorate water quality between 2009 and 2023 did not produce improvements in catchment grades, however, these proved to be effective to improve grades in the Logan, Albert and Brisbane estuaries. Strategy B also did not produce any improvement in marine grades. Strategy C had the highest overall and initial budget of the three strategies. The high investments were not translated into improvements in catchment report cards. However, they improved grades in the Albert and Brisbane estuaries, only after 2028, which coincided with increase in investments in rural actions, such as Best Farming Practices and Rural Riparian Revegetation. No improvement in marine grades resulted from these investments. This could be associated to the spatial distribution of management actions in the catchments.
Figure 23. User management history by action for managed regions (Best Farming Practices).
5.2.2
Social perception
There is an overall relative improvement in the social perception index on a regional scale when compared to the baseline (no management action) (Figure 24). This reflects the positive outcomes associated to the investments in actions to improve waterways health in the catchments. The results per region showed that strategies A and B improved the social perception index in the Logan estuary and strategies A, B and C produced improvements in the index in the Brisbane estuary (Figure 25), when compared to the baseline strategy.
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Figure 24. Average of social perception index for each strategy for all regions.
Figure 25. Average of social perception index for each strategy for Brisbane estuary.
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5.2.3
Benefits per household and cost effectiveness
Strategies A and B were the only ones showing positive cumulative total benefits due to water quality improvements for all SEQ (Figure 26), with strategy B producing higher benefits in the Albert and Brisbane estuaries. This illustrates the case where a high cost strategy (C) achieves very limited results at the scale of the managed catchments and only manages to slow down the overall degradation of water quality at the scale of SEQ. Alternative less costly strategies (A and B) significantly improved the water quality in the managed catchments from the initial years and also improve the status of the entire SEQ waterways.
Figure 26. Cumulative total benefits ($) due to water quality improvements for all managed regions.
Cost effectiveness can be explained as the percent improvement in water quality per dollar spent (Chapter 4.3.2). Strategy A was effective in the Brisbane and Albert estuaries and not effective in the Logan estuary until 2028 (Figure 27). Strategy B was effective in the Albert and Brisbane estuaries and not effective to achieve improvement in report card grades until 2019 in the Logan estuary (Figure 28). Strategy C was effective for the Brisbane and Albert estuaries only after 2031 and not effective to improving report card grades in the Logan estuary (Figure 29).
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Figure 27. Cost effectiveness of strategy A in the Logan, Albert and Brisbane estuaries. Negative values means the strategy is not effective.
Figure 28. Cost effectiveness of strategy B in the Logan, Albert and Brisbane estuaries. Negative values means the strategy is not effective.
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Figure 29. Cost effectiveness of strategy C in the Logan, Albert and Brisbane estuaries. Negative values means the strategy is not effective.
5.3
Chapter summary
The evaluation of strategies using the MSE Tool proved to be relatively easy and quick to perform. The strategies used in the report are only illustrative but, nevertheless, their trial run demonstrated that it has great potential in assisting resource managers to make decisions and learn from their decisions under a range of budget allocations, effectiveness of actions, social perception and environmental goals. The tool has the potential to help resource managers during the planning process by facilitating and enabling them to directly see the consequences of their decisions, both at catchment and regional scales.
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6.
OVERALL DISCUSSION AND CONCLUSIONS
6.1
Overarching catchment-to-coast MSE framework
MSE has been tailored to the catchment-to-coast conditions of SEQ with a focus on the design of management strategies to improve and maintain the health of waterways, including Moreton Bay and coastal receiving waters. The catchment-to-coast MSE approach is defined by the following three elements: (i) the project team; (ii) the stakeholders; and (iii) a computer simulation tool to model different components of the natural resource management system, such as ecosystem and human responses, monitoring and observation, assessment, management decisions and actions, and associated learning. We refer to the computer simulation tool as the MSE Tool. The project team is represented by the CSIRO Wealth from Oceans Flagship team, responsible for assisting with stakeholder engagement, knowledge integration and development of the MSE Tool. Stakeholders actively contribute to the design and development of the Tool, which is used in the evaluation of management strategies and to mediate also the interaction between project team and stakeholders. The interaction between the stakeholders and the project team also helps to manage stakeholder’s expectations for the MSE tool to realistic levels. The catchment-t0-Coast CMSE approach was implemented in the following four stages: • Establish a living Vision Document that contains high-level specifications of the MSE Tool in terms of the needs of the end users and stakeholders. • Interview stakeholders to learn about their current decision-making processes and to elicit what information they need in order to make their decisions (these are included in the MSE Tool). • Develop the MSE Tool for use by stakeholders. • Undertake workshops whereby stakeholders use the tool to test alternative management strategies. The project team used information on how stakeholders interacted with the Tool to improve it. The above participatory approach permits stakeholders to therefore make significant contributions to the design of the MSE Tool, and simultaneously allows the project team to discover what information managers consider relevant when making decisions, what issues are relevant to water quality management in SEQ, and how they want to use the tool. By using inputs from prospective users in its development, the tool valueadds to their existing knowledge and expertise because it enables them to rapidly develop and evaluate local and/or regional water quality management plans. The C2CMSE approach focuses on management outcomes by implementing measures of progress against performance criteria, helping managers learn about the managed system in order to achieve their objectives. In summary, the C2CMSE approach helps managers with the following: • Allows them to make and learn from mistakes quickly and without adverse consequences. • Check whether or not objectives are achievable and when the system might get there. • Improve collaboration with other agencies, stakeholders and scientists.
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• • • •
Make trade-offs between multiple management objectives and options. Design monitoring strategies and evaluate assessment methods. That is, what to measure, how often and where. Identify critical knowledge gaps. Design around structural uncertainty by using different model structures when evaluating management strategies.
6.2
Enabling Tool for catchment-to-coast MSE
The MSE Tool version 2 enables the implementation of simulated management strategies in all SEQ catchments. The management actions available in the Tool influence water quality in the waterways with flow-on effects on environmental, social and economic outcomes. The outcomes can be visually compared against targets set by the user through the user interface. With the information at hand the user can then assess the effects of decisions and modify their strategies as new information is gathered through the interactive user interface of the Tool. The strategies can be recorded, stored and retrieved in order to evaluate the management strategy under different scenarios. The Tool allows management strategies to be simulated over multiple repetitions needed to explore their effects on an uncertain future. We developed a catchment-to-coast biophysical model where managers are able to test the effects of their actions on waterway health. The statistical biophysical model of the MSE Tool uses empirical data from 253 estuarine and marine monitoring sites of the EHMP network (see Chapter 1.1.4). The data captures relationships between variables within and between sites on monthly intervals for up to 10 years. Empirical relationships are used in the biophysical model to simulate future stochastic observations and to propagate the effects of local interventions along the waterway network. That is, essentially back casting. For valid forecasting we need process models, or a hybrid between the statistical and process models which will be developed in version 3 of the Tool. The management model of the MSE Tool is applied over a 1 km2 grid cell overlapping the hydrological network, where the user can select a range of rural and urban management options that affect water quality at EHMP stations. Each management action applied in each cell produces a pressure-response relationship that improves water quality at its nearest EHMP station. Additionally, the MSE Tool allows management actions to be applied in the upper catchments. These actions should have detectable effects on the water quality indicators at the upper catchment EHMP stations. However, unlike with the estuary stations, the upper catchment indicators are difficult to model statistically. Therefore, we related the report card grades of upper catchment stations to a selection of measurable quantities that would likely be affected by management actions. For example, and in particular, the extent of riparian vegetation along a stream will be affected by re-vegetation action. We then scanned the historical data set for a Station-Year combination whose annual health value was close to the report card grade. The indicator data for this combination was then used for that station. For the evaluation of management strategies the Tool allows for the comparison of environmental, social and economic objectives outcomes by generating data for indicators of the biophysical domain (i.e. the water quality, EHMP report card grades). These are subsequently used as inputs for models that generate indices of social satisfaction (see section 4.2). The Tool provides indicators of management costs, also
88
including estimates of investment and maintenance costs calculated from the set of the management actions selected by the user. The economic model also calculates economic benefits per household and cost effectiveness of the strategy.
6.3
Evaluating strategies with the catchment-tocoast MSE Tool
The evaluation of strategies using the MSE Tool proved to be relatively easy and quick to perform. The strategies used in the report are only illustrative but, nevertheless, their trial run demonstrated that it has great potential in assisting resource managers to make decisions and learn from their decisions under a range of budget allocations, effectiveness of actions, social perception and environmental goals. The tool has the potential to help resource managers during the planning process by facilitating and enabling them to directly see the consequences of their decisions, both at catchment and regional scales. The spatial distribution of management actions and the mosaic of management actions used in the strategies influenced water quality differently. Further work is needed to improve the algorithms used in the current MSE model because accuracy of the effectiveness of management action is still not completely understood in the region. This is an area of current active research (Sheldon 2010) and we expect to include improved algorithms in future versions of the MSE Tool. In version 3 the project team intends to partner with catchment and receiving waters modellers to develop more robust coefficients for the effectiveness of management actions on water quality indicators (Chapter 3.1). The social perception modelling approach is novel in both its direct application and the pragmatic way in which the baseline scores are displayed in the MSE Tool UI. Managers are able to evaluate plausible social impacts associated to their decisions and track the social perception index. This is particularly useful because some management actions are likely to provide different social perceptions in the short (1-5 years)- and long-term (> 5 years). We expect to refine the ‘baseline’ value indicators in future versions of the social perception model by including survey data from a wider selection of public respondents, rather than experts, drawn from the demographics across the SEQ catchments. Multiple respondents will be sought as representatives in each of the social groups. The economic model provides summaries for management action costs, as well as benefits ($) per household, which is used to calculate the cost-effectiveness of management strategies. In terms of cost-effectiveness, the management strategies displayed very different profiles, with some strategies being much more successful at minimising the costs required to achieve improved water quality (most effective strategies being ~40 times more effective than least effective strategies) at the scale of the catchments.
6.4
Expert opinion to inform the management model
Experts in water quality management in SEQ identified additional management actions to improve the utility of the C2CMSE (e.g. stream/gully rehabilitation” & “water recycling”) (Chapter 3; Table 1). The project team had already commenced investigating
89
some of these additional options and made them available in the Tool as the project “expert-based configuration” (see Table 5). This configuration should be used as a placeholder only, as there was insufficient time to review associated costs and their NAC in the current version of the MSE Tool.
6.5
Other applications of the MSE Tool
6.5.1
Hybridisation of process-based and statistical models
The receiving water quality model version 3 (RWQM3) is a process-model which couples process-based models (hydrodynamic, sediment transport and biogeochemical) of the Moreton Bay area, including numerous estuaries that deliver freshwater into the Bay and the Gold Coast. As a result its runtimes are in the order of hours to days for a 1year simulation period. The MSE Tool uses empirical statistical biophysical models that run quickly (in the order of seconds) at the expense of precision. A reasonable solution is to find ways to hybridise the C2CMSE biophysical model with the RWQM3 to deliver more precise model results within a short time-frame. This is particular useful to help frame the questions that can be answered with more precision through the RWQM3. A possible pathway in this direction is to incorporate in the C2CMSE v.3 the capacity to use model results from the RWQM3 in a central database of simulations. The MSE Tool empirical biophysical model produces results based on previous observations using the extensive dataset of the Ecosystem Health Monitoring Program (EHMP). This means that everything that was not observed (i.e. changes on climate, rainfall regimes, sea level fluctuations) will not be simulated. As a process model, the RWQM3 can produce simulated observations different from previously observed data and we can incorporate this into the MSE-SEQ. Another option is to emulate the RWQM3 statistically in the MSE Tool by means of a relationship fitted to the results from running the full model. A similar approach can be used to integrate catchment models in the Tool. These combined fast throughput methods will also facilitate an auto-tuning version of the MSE Tool. Using a genetic or similar algorithm, for selected strategies, the MSE Tool can be run multiple times to find the best parameter setting for either a combined multiple objective (fixed trade-off in objectives), or for one objective at a time, subject to constraints on others. This will save users having to fine tune parameters in management strategies, and allow them to concentrate on the important task of designing and choosing from among competing strategies and objectives. As noted above, there are obvious synergies between the need to develop fast approximate models or emulators for the MSE Tool, and for model calibration. This not only has practical consequences, in terms of combining efforts and exploiting synergies and complementary approaches, but it also offers an opportunity for significant theoretical advances. There is currently no sound basis for deciding what level of spatial resolution is adequate to support coastal management. As computational power has increased, there has been a corresponding move to higher resolution models, on the grounds that these are more realistic. There’s no doubt that higher-resolution hydrodynamic models are capable of providing more realistic and accurate hind casts and forecasts, provided they are supported by adequate bathymetry and forcing data. But it’s less clear that resolving water quality processes and variables at this level of resolution is necessary to adequately inform management. The transport model tools used in the RWQM3 will allow us to systematically assess the effect of model resolution on biophysical predictions. By coupling models at different resolution into the C2CMSE
90
framework, either directly or via statistical emulators, we will be able to assess the effect of resolution on the conclusions drawn with regard to management strategies.
6.5.2
Using the MSE Tool to design monitoring programs
In principle, the MSE Tool provides an ideal framework for designing monitoring programs. From a management point of view, the critical measure of a monitoring design is whether it supports a robust adaptive management strategy. In principle, MSE allows the direct assessment of this measure. However, because the current MSE Tool models are based on the analysis of results from the EHMP monitoring sites, it is difficult to use these models to consider monitoring approaches which differ substantially from the existing one. In principle, it should be possible to use the new RWQM V3 to consider a much broader range of monitoring designs, potentially sampling at quite different time and space scales based on new technologies such as moored time series and remote sensing. It will of course need to be demonstrated that RWQM V3 can make sensible predictions / hind casts of spatial and temporal variability in the indicators of interest. Because much of the spatial and temporal variation is driven by the interaction of highly dynamic physics with broad patterns resulting from loads and boundary conditions, there are reasons to be optimistic that the model can capture at least the key statistical properties of this variability. In order to exploit this, it will be necessary to build a flexible range of observation models, which simulate the output expected from the application of different sampling designs and technologies to model output. Similar arguments can be made about the use of the MSE Tool framework to help prioritise investment in process studies and observations to address knowledge gaps. Where gaps in process understanding are clearly identified, we can test the implications of alternative formulations in the process model, and again see if these have significant implications for management decisions and strategies.
6.5.3
Visualisation
The MSE Tool was built with the purpose of allowing managers to visualise and understand the effects, costs and benefits of different management actions applied in the region. It was purpose built to allow visualisation of data to be easy and informative. Therefore, the MSE Tool can be used to visualise model results for process models, such as the RWQM3, along with catchment models, such as SourceCatchments (the next generation of WaterCast, E2, EMSS catchment models). It is also important for the receiving water and catchment models and the MSE Tool that outputs are presented using indicators and statistical summaries that are meaningful for stakeholders. It is of course important to continue to use the report card indicators and grades that have been such an important feature of the Healthy Waterways strategy. But given current questions about the reporting methods, their sensitivity to climate variation vs. management actions, and their value for diagnosis and attribution, there is an exciting opportunity, in consultation with stakeholders, to develop and assess new performance measures, and to improve the communication of outcomes and options to stakeholders and decision-makers. The approach and model we developed for version 2 can be used to develop and assess these new performance measures.
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REFERENCES Abal EG, Dennison WC, Bunn SE (2005a) Setting. In 'Healthy waterways healthy catchments: making the connection in South East Queensland, Australia'. (Eds EG Abal, SE Bunn and WC Dennison) pp. 13-34. (Moreton Bay Waterways and Catchment Partnership: Brisbane) Abal EG, Bunn SE, Dennison WC (Eds) (2005b) 'Healthy waterways healthy catchments: making the connection in South East Queensland, Australia.' (Moreton Bay Waterways and Catchment Partnership: Brisbane) Alam K, Rolfe J, Donaghy P (2006a) An economic analysis of improved water quality. In '50th Annual Conference of the Australian Agricultural and Resource Economics Society'. Manly, Sydney. (unpublished) Alam K, Rolfe J, Donaghy P (2006b) Economic and social impact assessment of water quality improvement. Australasian Journal of Regional Studies 12, 85-101. Argent RMP, Perraud J-M; Podger, G.M.; Murray, N. (2008) 'WaterCAST Component Model Reference Manual.' eWater CRC, Canberra. Barlow KB, Christy B, Weeks A (2009) Nutrient generation and transport at the catchment scale. In '18th World IMACS / MODSIM Congress'. Cairns, Australia pp. 1823 - 1829 BDA Group (2005) 'Scoping Study on a nutrient trading program to improve water quality in Moreton Bay.' BDA Group Economics and Environment, Melbourne. Binney (2010) 'Managing what matters: the cost of environmental decline in SEQ.' SEQ Catchments. Breiman, L., 2001. Random Forests. Machine Learning, 45(1): 5-32. Brodie IM (2009) Australian examples of residential integrated water cycle planning accepted current practice and a suggested alternative. Desalination and Water Treatment 12, 324-330. Bunn SE, Abal EG, Greenfield PF, Tarte DM (2007) Making the connection between healthy waterways and healthy catchments: South East Queensland, Australia. Water Science and Technology 7, 93-100. Bunn SE, Abal EG, Smith MJ, Choy SC, Fellows CS, Harch BD, Kennard MJ, Sheldon F (2010) Integration of science and monitoring of river ecosystem health to guide investments in catchment protection and rehabilitation. Freshwater Biology 55, 223-240. Butterworth DS, Bentley N, et al. (2010) Purported flaws in management strategy evaluation: basic problems or misinterpretations? ICES Journal of Marine Science 67, 567-574. Butterworth DS, Punt AE (1999) Experiences in the evaluation and implementation of management procedures. ICES Journal of Marine Science 56, 985-998. Caitcheon G, Prosser I, Wallbrink P, Douglas G, Olley J, Hughes A, Hancock G, Scott A (2001) Sediment delivery from Moreton Bay’s main tributaries: a multifaceted
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approach to identifying sediment sources. In 'Third Australian Stream Management Conference'. Brisbane. (Eds I Rutherfurd, F Sheldon, G Brierley and C Kenyon) pp. 103-107. (Cooperative Research Centre for Catchment Hydrology). Checkland, P., 1999. Soft Systems Methodology: a 30-year retrospective. John Wiley and Sons Ltd, Chichester, 418 pp. Davies G (2004) 'Economia - New economic systems to empower people and support the living world.' (ABC Books: Sydney) de la Mare WK (2009) Variation on deliverables and personnel for the contract “Healthy Waterways Management Scenario Evaluation: Scoping Study for the Development of a 'catchment-to-coast' MSE in SE Queensland - Phase 2”. Document submitted to the Healthy Waterways Partnership on 13.08/2009. CSIRO/Wealth from Oceans Flagship: Cleveland. de la Mare WK (1996) Some recent developments in the management of marine living resources. In 'Frontiers of population ecology'. (Eds RB Floyd, AW Sheppard and PJ de Barro) pp. 599-616. (CSIRO Publishing: Collingwood) Department of Infrastructure and Planning (2008) South East Queensland. (Queensland Government: Brisbane) Dichmont CM, Deng AR, Punt AE, Venables W, Haddon M (2006) Management strategies for short-lived species: The case of Australia's Northern Prawn Fishery 1. Accounting for multiple species, spatial structure and implementation uncertainty when evaluating risk. Fisheries Research 82, 204-220. Douglas G, Palmer M, Caitcheon G (2003) The provenance of sediments in Moreton Bay, Australia: a synthesis of major, trace element and Sr-Nd-Pb isotopic geochemistry, modelling and landscape analysis. Hydrobiologia 494, 145-152. Dutra LXC, Taboada MB, Haworth RJ (in press) An integrated approach to tourism planning in a developing nation: a case study from Beloi (Timor-Leste). In 'Stories of Tourism Planning and Policy: Exploring new spaces of policy, planning and governance'. (Eds D Dredge and J Jenkins). (Earthscan) EHMP (2008) 'Ecosystem Health Monitoring Program - Annual Technical Report 2006-07 on the health of the freshwater, estuarine and marine waterways of South East Queensland.' South East Queensland Healthy Waterways Partnership, Brisbane. EPA (2006) 'Queensland Water Quality Guidelines 2006.' Environmental Protection Agency, Brisbane. EPA (2008) 'Population and settlement patterns, State of the environment Queensland 2007.' (Environmental Protection Agency, State of Queensland: Brisbane) Gardner EA (2003) Some examples of water recycling in Australian urban environments: a step towards environmental sustainability. Water Science and Technology 3/4, 23-31. Goonetilleke A, Thomas E, Ginn S, Gilbert D (2003) Modelling insights into pollutant wash-off from urban catchments in Queensland, Australia. In '8th International Conference on Environmental Science and Technology'. Athens pp. 278-285. (University of the Aegean)
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Healthy Waterways Inc (2010) 'Ecosystem Health Report Card Method.' Healthy Waterways Inc., Brisbane. Holland D, Sanchirico JN, Johnston RJ, Joglekar D (2010) ' Economic analysis for ecosystem-based management: applications to marine and coastal environments.' (RFF Press: Washington DC) Kell LT, Mosqueira I, et al. (2007) FLR: an open-source framework for the evaluation and development of management strategies. ICES Journal of Marine Science 64, 640-646. Ledoux L, Turner RKNthOCM (2002) Valuing ocean and coastal resources: a review of practical examples and issues for further action. Ocean & Coastal Management 45, 583-616. Lynam T, Bousquet F, Page CL, d'Aquino P, Barreteau O, Chinembiri F, Mombeshora B (2002) Adapting science to adaptive managers: spidergrams, belief models, and multi-agent systems modeling. Conservation Ecology 5, Article 24 [online]. McDonald AD, Fulton E, Little LR, Gray R, Sainsbury KJ, Lyne VD (2006) MultipleUse Management Strategy Evaluation for Coastal Marine Ecosystems Using InVitro. In 'Agents, icons and idols'. (Eds P Perez and D Batten) pp. 283-297. (ANU E Press: Canberra) Olley J, Prosser I, Hancock G, Caitcheon G, Marston F, Hughes A, Scott A, Stevenson J, Young B (2000) 'SEQ Sediment Sourcing Project: Report on Phases 1 and 2 (Stage 3 Project SS) for the Southeast Queensland Regional Water Quality Management Strategy.' CSIRO Land and Water. Olley J, Ward D, Pietsch T, McMahon J, Laceby P, Saxton N, Rickard B, Rose C, Pantus F (2009) 'Rehabilitation priorities Knapp Creek – Final Report.' Australian Rivers Institute, Nathan. Pantus FJ, Abal E, Pearson L, Steven A (2008) 'Healthy Waterways Management Strategy Evaluation: Science support for catchment-to-coast water quality management.' CSIRO Wealth from Oceans and Water for a Healthy Country National Research Flagships, Cleveland. Pantus FJ, Dennison WC (2005) Quantifying and Evaluating Ecosystem Health: A Case Study from Moreton Bay, Australia. Environmental Management 36, 757-771. Parsons GR, Helm, EC, Bondelid T (2003) Measuring the Economic Benefits of Water Quality Improvements to Recreational Users in Six Northeastern States: An Application of the Random Utility Maximization Model. (University of Delaware: Working paper) Pascual R, Tickell S, de la Mare W (2009) 'Healthy Waterways Partnership Management Strategy Evaluation System Vision - Version 0.91.' CSIRO Marine and Atmospheric Research, Cleveland. Peterson E E, Sheldon F, Darnell R, Bunn S E, Harch B D (2010) A comparison of spatially explicit landscape representation methods and their relationship to stream condition. Freshwater Biology, DOI: 10.1111/j.1365-2427.2010.02507.x
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Punt AE, Smith ADM, Cui G (2001) Review of progress in the introduction of management strategy evaluation (MSE) approaches in Australia’s South East Fishery. Marine & Freshwater Research 52, 719-726. Rittel HWJ, Webber MM (1973) Dilemmas in general theory of planning. Policy Sciences 4, 155-169. Robinson J, Clouston B, Suh J, Chaloupka M (2008) Are citizens' juries a useful tool for assessing environmental value? Environmental Conservation 35, 351-360. Roebeling PC, van Grieken ME, Webster AJ, Biggs J, Thorburn P (2009) Cost-effective water quality improvement in linked terrestrial and marine ecosystems: a spatial environmental-economic modelling approach. Marine and Freshwater Research 60, 1150-1158. Rolfe J, Alam K, Windle J (2004) 'The importance of riparian vegetation in improving water quality, Establishing the potential for offset trading in the Lower Fitzroy River.' Central Queensland University, Emerald. Rolfe J, Donaghy P, Alam K, O’Dea G, Miles R (2005) 'Considering the economic and social impacts of protecting environmental Values in specific Moreton bay / SEQ, Mary River basin / Great Sandy Strait region and Douglas Shire waters.' Institute for Sustainable Regional Development, Central Queensland University, Brisbane. Sainsbury KJ, Punt AE, Smith ADM (2000) Design of operational management strategies for achieving fishery ecosystem objectives. ICES Journal of Marine Science 57, 731-741. Sarker A, Ross H, Shresthab KK (2008) A common-pool resource approach for water quality management: An Australian case study. Ecological Economics 68, 461-471. Semple P, Dunlop K (1998) 'Logan, Coomera and South Moreton Bay Regional Watewater Management Study - Environmental Monitoring Program, Annual Report 1996.' Department of Environment, RE207 March 1998, Brisbane. Sheldon F (2010) 'Freshwater EHMP Trend Analysis.' Griffith University/Australian Rivers Institute, Nathan. Smith ADM (1994) Management strategy evaluation - the light on the hill. In 'Population dynamics for fisheries management'. Perth. (Ed. DA Hancock) pp. 249253. (Australian Society for Fish Biology) South East Queensland Healthy Waterways Partnership (2002) About SEQ Waterways: setting the scene. (Moreton Bay Waterways and Catchment Partnership: Brisbane) South East Queensland Healthy Waterways Partnership (2007a) 'South East Queensland Healthy Waterways Strategy 2007-2012 Final Document: Management Strategy Evaluation Action Plan.' South East Queensland Healthy Waterways Partnership, Brisbane. South East Queensland Healthy Waterways Partnership (2007b) 'South East Queensland Healthy Waterways Strategy 2007-2012 - Strategy Overview.' (Brisbane)
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South East Queensland Healthy Waterways Partnership (2007c) 'South East Queensland Healthy Waterways Strategy 2007-2012 Final Document - Moreton Bay Action Plan.' Healthy Waterways Partnership, Brisbane. South East Queensland Healthy Waterways Partnership (2009) Report Card Methods How are the grades calculated?, Brisbane. Taylor A (2005) 'Guidelines for Evaluating the Financial, Ecological and Social Aspects of Urban Stormwater Management Measures to Improve Waterway Health.' Cooperative Research Centre for Catchment Hydrology, Melbourne. Walker, W.E. et al., 2003. Defining Uncertainty: A Conceptual Basis for Uncertainty Management in Model-Based Decision Support. Integrated Assessment, 4(1): 5-17. Walters C (1986) 'Adaptive management of renewable resources.' (Macmillian Publishing Company: New York) Water by Design (2009) 'Meeting the proposed stormwater management objectives in Queensland: a business case (Version 1 Draft).' South East Queensland Healthy Waterways Partnership, Brisbane. Weber TR (2008) 'Modelling Urban Catchment Loads - Great Lakes Water Quality Improvement Plan.' BMT WBM Pty Ltd, Brisbane. White DA, Smith RA, Price CV, Alexander RB, Robinson KW (1992) A spatial model to aggregate point-source and nonpoint-source water-quality data for large areas. Computer & Geosciences 18, 1055-1073. Windle J, Rolfe J (2006) 'Non market values for improved NRM outcomes in Queensland.' Research Report 2 in the non-market valuation component of AGSIP Project #13.
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APPENDIX 1 – ENABLING TOOL FOR MANAGEMENT STRATEGY EVALUATION Introduction In order to support the C2CMSE approach in SEQ we developed a software tool that enables resource managers to perform rapid simulation and evaluation of their management strategies. The first proof-of-concept MSE Tool (version 1) allowed the evaluation of a range of management actions for the Logan-Albert catchment, and was delivered in 2008 (Pantus et al. 2008). In 2009-2010 the project team actively engaged with stakeholders and research partners to develop version 2 of the MSE Tool, which was used in the open loop workshop, followed by version 2.1 which built on the results from that workshop. The tool consists of a re-usable software framework (MSE Tool Version 2) that can execute any model that conforms to a simple standard interface. The framework takes care of the general infrastructure tasks, including model coupling, initialisation and results storage, and is responsible for all the user interactions including visualisation of the stored results. The availability of such a framework means that the models it interacts with can be smaller, simpler and consequently faster than the models used in traditional MSE software. The MSE Tool is designed to run in a standard desktop environment and allows endusers to record, visualise and modify their own management strategies. The graphical interface allows possible management actions to be applied in specific geographical locations, each of which may have a different effect on local and regional water quality. The sequences of actions, referred to as Strategies, are tested against a network of software models that simulate the expected response of the biophysical, social and economic systems at both local and regional levels. The simulated observations are then assessed against management objectives and targets, and results are displayed in the graphical user interface (UI). The MSE tool is designed to allow the response models to be upgraded as new research improves our understanding of the C2C system. The user does not interact directly with the response models, but may choose to modify the assumed initial conditions for the simulation to more closely match local values. Alternative sets of initial condition values are known as Scenarios. The recorded strategies and scenarios can be shared with researchers and with other stakeholders in order to enable collaboration and improved management across the whole of SEQ. The objective of this chapter is to discuss the requirements and constraints for the software framework, detail our design and implementation for Version 2, and give advice about how models that can be run by the framework should be constructed.
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Technical aspects of the MSE Tool The environment in which the MSE Tool is being developed is summarised in Table 2. Table 16. The development environment specification used in the MSE Tool.
Description
Choice
Comments
Operating System
Windows XP, Vista, Windows 7
With Framework 3.5 run environment
Hardware
Intel 32-bit dual core
Software development
Microsoft Studio .NET 2008 with
environment
Framework 3.5
Programming Languages
VB.Net, C#
Databases
MS Access (.mdb files), SQL
Database formats free and
Server 2008 CE(.sdf files)
portable, single user
Supports VB.Net, C#, C++
Software framework architecture The MSE Tool is designed to enforce a clear separation of responsibilities between the components, which provide reusable infrastructure to any set of models, and the specific models for use when evaluating a specific set of management actions. Discussions of the architecture will assign components to one of two categories: • The MSE Framework, which includes the user interface, model initialisation, execution and linkage, and for the storage and retrieval of results; and • The Domain Model Components, consisting of all the components dedicated to simulating the application of (and response to) management actions in a particular problem domain. The framework does not depend on any particular set of domain model components, only on the set of standard interfaces that those components are required to implement. Likewise, the domain model components also have no dependency on the framework components: only on the interface definitions. This formal interface means that the framework components can be modified in response to user feedback without the models needing to be re-written, which is essential in the C2CMSE (see Chapter 1.3). The primary components of the MSE Framework are shown in Figure 30 and described in the following sections.
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Figure 30. Software framework architecture diagram of the MSE Tool.
Architecture layers The framework components are divided into the following layers: • The Data Storage Layer components allow configuration, initialisation and results data to be stored for reuse. •
The Data Access Layer knows where the storage-components have been saved to, and is able to retrieve the stored data for use by the Logic Layer;
•
Logic Layer components, which are responsible for actually executing models and processing the results.
•
Presentation Layer components, which allow the user to control the model execution and to visualise saved strategies and results. They are tied to a single user interface technology – windows forms in the case of this version.
Domain model components The Domain Model components are all associated with the problem domain of the MSE. They are required to implement standard interfaces to allow the framework to communicate with them. There are three major types of domain component used by the MSE Tool: 1.
Domain Models are the software implementation of the MSE response model. Each domain model processes initialisation data and optional inputstate values to calculate one or more predicted output state values at the end of each time step. Each domain model is required to implement a standard interface called IModel, which the Model Execution Engine uses to drive the model through the simulation intervals. The initialisation, input and output state values are exposed as properties of the model.
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Domain models are not required to handle data access, initialisation, or results storage or to respond to user interaction. All these tasks are taken care of by the software framework components. 2. Custom Visualisation Controls are associated with domain models, and provide rich visualisation of the saved state values for that model. The framework presently supports two types of custom visualisation control: windows’ forms user controls, and standard Microsoft client report definition files (.rdlc files), paired with custom data sources. The Windows Forms Controls and Report data sources are required to implement a standard interface called IShowResults, which allows the framework to provide the stored results data that is to be displayed. 3. Initialisation Data Services are used to expose the initialisation data required by the domain models to the software framework. Each data service is responsible for managing and accessing any data storage locations (called initialisation data sources) that it requires. Data services do not have a required interface; instead, data values are exposed as immutable properties on the service class, and the software framework uses ‘reflection’ (a programming mechanism of discovering class information solely at run time) to discover what those properties are.
Model composition wrappers The software framework defines a set of wrapper models that it uses to modify or enhance the behaviour of the custom Domain Models. The following wrappers are presently available: •
The Composite Model wrapper allows multiple domain models to be composed into a single model, re-exposing all the required state-properties. It also allows the output state from a child model to be used as the input state for any other child model: this effectively links the child models. When the composite model is executed, the child-models will be executed in turn in order of dependency, so that the linked output value has been calculated before it is needed as input. Circular link-dependencies are not supported.
•
The Open Loop Dialog model wrapper constructs and triggers the open-loop dialog visualisation controls when a model requires input from the user.
•
The Time Split wrapper splits the normal execution interval into multiple smaller intervals. So if the software framework was executing models with a time step of one year, this wrapper could be used to execute its wrapped domain model with a time step of one month, accumulating the output state values until the end of the year.
•
The Saved Model wrapper retrieves and stores the value of every output state parameter on its child-model at the end of every time step. The result values are cached until the end of the run, and then exported to an XML file in an appropriate location if the run completes successfully.
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MSE configuration file The MSE Configuration file is how the MSE software framework knows which specific set of custom domain models should be executed. It stores all the following information: • Declarations of which domain models should be run, how they should be composed and wrapped, and how they should be linked together. • Declarations of which data service properties should be used to provide the initialisation data to the composed and wrapped domain models. • The default initialisation values to be used for all the remaining initialisation parameters that have not been bound to data services. • Sets of alternative initialisation values for non-data-service-bound initialisation parameters. These alternate configurations are referred to as Scenarios. • The simulation time range and step interval. • The set of maps that should be used when visualising model results. This information is saved in a human-readable XML file format, which allows the configuration to be manually constructed and edited.
Module service The module service knows how to locate the domain model components on an installed system, and uses reflection to discover which components are models and which are data services, and all the state variable names and types for each one. Once it has identified the domain components, the Module Service is responsible for creating and caching the data services that will be used to initialise the models and visualisation controls.
MSE project The MSE Project components reads a saved MSE Configuration File, and uses the information in that file to provide access to the Scenario Definitions, saved Strategy Records and saved Replicate Results for the configured model. Each MSE Project uses the Module Service to locate the domain models, visualisation controls and data sources that it needs when the configured model is executed or its results are visualised.
Model execution engine The execution engine is the core component of the MSE Tool. It is used to drive and control the MSE Simulation in a background thread allowing the UI to remain responsive while the run is in progress. The execution engine is able to operate in interactive mode, which executes a single replicate of a model that includes an Open Loop Dialog. It uses a standard call-back interface on the UI called IShowRunResults to allow the dialog and visualised results-so-far to be displayed to the user. The results of such a run are saved as a new strategy record. Alternatively, the engine can execute multiple replicates in non-interactive mode, where the results of a previously recorded strategy are used as input to any dependent models in place of the open-loop dialog. The execution engine works with a single object that implements IModel. It uses the information provided by the MSE Project component to create the domain models and wrappers specified in the MSE Configuration, and then to initialise them with the values appropriate to a single selected scenario. Depending on the contents of the
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configuration file, the executable model that the engine actually interacts with could be a raw domain model, performing a single calculation, or could be a complex composition of multiple domain models and model wrappers. The execution algorithm for the target model is: • Set the value of each Initialisation parameter-property to the value required for the current scenario. • Call IModel.Prepare to inform the model that it is about to be used in a new run, and should reset the values of its state variables. • For each interval in the simulation time step pattern: • Call IModel.ExecuteTimestep to requests that the model update its state variables to reflect the state at the end of the current time step. • Call IModel.Finish to inform the model that the run is complete.
Results file manager The Results File Manager is a Data Service Component that knows where the results of a strategy or replicate are stored, and is able to save new results, or to retrieve old results. In this version of the MSE Tool, a set of results is saved by serialising all the sampled state variable values to a plain-text fine, in a mixture of XML and JSON formats. When the results are needed for visualisation, or (in the case of strategy results) for use as model input, then the information about domain model state variables which is available from the Module Service is used to reconstitute the text file to the correct de-serialised values. The plain text file format was chosen for speed, over possible alternative options of Microsoft Access databases (.mdb files) or Microsoft SQL Server Compact Edition databases (.sdf files). Benchmark tests showed that the text files were much faster to use when saving or retrieving complete sets of run results than either of the database file formats.
Strategy record files A strategy record is a set of instructions for the locations and times at which management actions should be applied to the model. A new strategy record is created when the user chooses to run the configured models in interactive mode (open-loop) (Chapter 1.2.3), and it contains the sampled and serialised results from the Open Loop Dialog model. Once recorded, a strategy can be used with any scenario and is not tied only to the one that was used to create it. The software framework will always make a ‘default’ or base case strategy available to the user, which is equivalent to ‘no management actions’.
Replicate results file Each replicate results file is a set of sampled and serialised output parameter values from every composed domain model that is not wrapped in an Open Loop Dialog wrapper. A set of replicate results is saved every time the model is executed, in both interactive and non-interactive modes (see Chapter 1.2.3). Each set of results is for a unique combination of the scenario, strategy and run ID that were used when the model was executed, where the Run ID gets used as the random seed for any stochastic models included in the model-configuration. The results files use the Run ID as their file name, and results files are grouped into directories named for the scenario and strategy combination (known as the run context). This means that
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the stochastic results for each run are all stored together. When details of results are viewed, the saved results can be accessed and de-serialised back into their memory representations before being sent to the visualisation layer.
Windows forms user interface The Windows Forms User Interface is the graphical application that the user actually interacts with. It provides a way for the user to load a MSE Configuration File into a MSE Project instance, and then allows the user to work with that MSE Project. The Windows Forms User Interface is also responsible for hosting the custom visualisation controls associated with the project-models. It uses the standard IShowResults interface to make this possible.
Domain model components for the SEQ catchmentto-coast MSE The domain model components that are used in the SEQ C2C domain represent a conceptual model of “the world”, and the linkages between those models represent the relationships between the various parts of it. Each domain model is responsible for calculating the predicted state of a single aspect of the SEQ simulation, with the complete simulation output being the results of all the domain models composed together. The diagram in Figure 31 shows the domain models, the initialisation, input and output parameters exposed by each model, and the data services that are used to provide values to the initialisation parameters when the models are executed or visualised. The individual domain models are: • The SEQ_C2C Composite, which is composed of the following: • User Management: Provides the options for implementing management actions in the whole geographic area of SEQ. It is the interactive open-loop dialog (See Chapter 1.2.3) • Decision Implementation: After users select which management actions they want to use and where, the decision implementation model specifies the rules that will affect the biophysical model. • The Biophysical Composite, which is composed from separate Estuarine and Marine Monitoring models, which predict the water quality indicator measurements that will occur at EHMP monitoring stations. The rules from the decision implementation model are used as inputs to the Estuarine Stations, and the measurements from the Estuarine Stations at the river-mouths become inputs to the marine stations. See Chapter 1 for details of these models. • The Biophysical composite uses the Time Split wrapper to break the yearly time step of the SEQ_C2C composite into monthly steps. • An Upper Catchment Report Card model, which predicts the upper catchment report card grades directly from the applied management actions (Chapter 2.1) • Estuarine and Marine Report Card Models, which transform the predicted Estuarine and Marine indicator measurements into report card grades (Chapter 4.1.2). • The Social Perception model uses report card grades as input to calculate the social perception of various social groups as outputs. (See Chapter 4.2)
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•
The Economic Model uses inputs from water quality, report card grades and decision implementation as inputs to calculate costs of management actions, economic benefits per household per 1 percent improvement in water quality, and cost effectiveness of management actions as outputs (see Chapter 4.3).
The parameter-ports on the left-hand side of the MSE Tool in the diagram (Figure 31) are all initialisation parameters. These are either connected to the various data services (shown) or will have values provided as part of each scenario (highlighted in cyan). The parameter-ports on the right hand side are all time step parameters (state variables). The output parameters are shown in white, and each of these is re-exposed by the composite model, so that the framework can save the values of these parameters at the end of each time step. Input parameters are shown in black, and each one must be connected to an output parameter.
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Figure 31. Domain model representing how the software models from South East Queensland represented in the MSE Tool are configured, and how they are linked to each other.
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APPENDIX 2 – PRIOR INFORMATION SENT TO PARTICIPANTS IN THE PRESSURE-RESPONSE INTERVIEWS HELD IN AUGUST 2010 Management intervention contributions to improve water quality downstream: expert opinion to inform the South East Queensland management strategy evaluation Context for interview The South East Queensland Management Strategy Evaluation (MSE Tool) is a computer simulation tool that allows managers to evaluate how their management strategies work under different scenarios, considering environmental, social and economic indicators and trade-offs they face when choosing their management strategies. The statistical biophysical model of the MSE Tool uses empirical data from the 253 estuarine and marine monitoring sites of the ecosystem health monitoring program (EHMP) network. The data captures relationships between variables within sites and between sites on monthly intervals for up to 10 years. Empirical relationships are used to simulate future stochastic observations and to propagate the effects of local interventions along the network. The management model of the MSE-SEQ is applied over a grid cell overlapping the hydrological network (Figure 32), where the user can select a range of rural and urban management options that affect water quality at EHMP stations. Each management intervention applied in each cell produces a pressure-response relationship to improve water quality at its nearest EHMP station. Current assumptions supporting the pressure-response model are the following (refer to Figure 1 and Table 1): 1.
One cell (1km x 1km) can support up to 4 Management Actions (MA).
2. Each MI affects only 2 biophysical variables at the EHMP station: Total Nitrogen (TN) and Turbidity (TS). 3. Each MI is characterised by Nominal Abatement Coefficients (NACs) for TN and TS. NAC represents the maximum abatement coefficient possible for a given intervention if (1) the monitoring station is located on the same cell AND (2) this cell is considered as the only contributor to the monitoring station (some sort of 1x1 km catchment). 4. Each MA has a specific Implementation Lag (IL) influencing the full effect of its NACs (Table 1). 5.
Each cell directly influences it’s the closest downstream Monitoring Station (MS).
6. The actual abatement coefficient (AAC) affecting TN and TS is based on individual cell contributions. Each contribution is independent from surrounding cells (semidistributed approach).
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7.
Local conditions are represented through a weight used to calculate the AAC at the MS. The weight uses riverbed distance (d) between the centre of a cell and its closest MS is used to weight the effect of MA to its actual abatement coefficient (AAC). If the monitoring station is located at the same cell as the MA and this cell is considered as the only contributor to the monitoring station, then NAC=AAC.
Figure 32. MSE-SEQ management model is applied over the stream network. Brown, green and yellow squares indicate urban, forest and rural land-use. Red square indicates a management intervention was implemented, affecting the nearest EHMP station (green circles) downstream.
For the open-loop version of the MSE Tool we developed NACs for 6 MI (Table 1) and AAC was calculated using the inverse distance between the cell where MI was applied and its nearest EHMP station multiplied by NAC (AAC=1/d x NAC). For the final delivery of the MSE Tool version 2 we would like to the following from interviewing you: •
Your opinion about an updated list of MI and their respective NAC
•
Your opinion about the calculation of the AAC.
The information you provide will be used for both the final delivery version 2 and also to help design the next version of the MSE Tool.
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Table 17. Nominal Abatement Coefficients (NAC) influencing TN and TB and implementation lag for management interventions that were used in the MSE Tool.
Management Actions
Detailed
NAC
management
Days till
Days till full
first effect
effect
actions
TN
TB
Rural Stormwater
SQID
-0.8
-0.8
360
30
Rural Riparian
Revegetation
-0.4
-0.7
180
720
-0.7
-0.7
180
720
-0.9
-0.7
360
30
-0.2
-0.8
360
720
-0.5
-0.5
180
180
Revegetation Best Farming
Fencing
Practices STP upgrade
Deliver TN concentrations of 2mg/L in the outfall
Best Urban Design
WSUD
Urban Stormwater
Grassed riparian buffer
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APPENDIX 3 – 2009 ECOSYSTEM HEALTH MONITORING PROGRAM (EHMP) REPORT CARD METHODS
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APPENDIX 4 - TUTORIAL OF THE MSE TOOL Configuration of the C2CMSE Import MSE Configuration The user first needs to import the MSE project specification from the File tab in the “Manager” window (Figure 33A). Two options are available: “literature-based configuration” and “expert-based configuration”.
Figure 33. Manager window.
Configuring scenarios After the configurations are imported the user has the option of selecting an existing scenario or creating a new one (Figure 33B). There are two scenarios currently available in the MSE Tool, and these are: Scenario 1: Always Effective (Effective governance and social support).
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Scenario 2: Decreasing Effectiveness (Weak governance and weak social support).
Configuring the Strategy The next step is to retrieve an existing strategy, which can be tested against different scenarios (Figure 33C). Alternatively, the user can click the button “Record New Strategy” which opens the interactive open-loop MSE window (Figure 34).
Figure 34. Interactive open-loop MSE window.
Implementation
of
the
management
strategy
during
the
interactive
run
(open‐loop)
In the interactive open-loop MSE Window the user selects and locates management actions that are part of the desired strategy (Figure 35). A land-use grid (A) overlaps SEQ catchments and differentiates between natural (green square), rural (yellow square) and urban (brown square) environments (see details in Chapter 3.3). The Control menu (B) located at the top allows the user to navigate from left to right to select a management option, to zoom in/out, to move around the map, and to return to the default view. Management options are located on the vertical left-hand side menu (C). One click on a button selects the desired option. Cumulative costs associated with the selected management strategy are displayed in the costing table (D), located in the upper right-hand corner of the window.
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Figure 35. Interactive open-loop MSE initial window where the user can select and implement management actions in the region.
When the user clicks on a management option, costs are displayed in the dialog box located on the top right-hand side of the map (Figure 36A). When a management option is selected the user can locate the intervention area on a cells that contains a land-use which allows the action to be placed (B). A small icon should appear. The cumulative budget can be visualised in the costing table (A). More information is available by expanding the table using the small anchors (C). Finally, when the user is happy with the choices made, the selection must be confirmed by clicking on the “Continue” button, located in the bottom right-hand corner (D).
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Figure 36. Interactive open-loop MSE initial window where the user can visualise costs associated with each management action with break-down capital and operating.
Management implementation If the user selects the “Management Implementation” Tab (Figure 37A) to assess how the mosaic of management actions affected the EHMP station (B) by improving water quality indicators (C). Results are displayed graphically (D).
Freshwater, estuarine and Moreton Bay biophysical responses When the data processing ends the user can visualise the biophysical response to the implemented management actions. The Response Window includes 3 Tabs, corresponding to upper catchment, estuary and marine biophysical responses (Figure 38A). The first tab displays detailed information on estuarine environmental indicators. The top menu under the Tab enables the user to choose between two data visualisation modes (B). In order to visualise results for a single station the user has to select one monitoring stations on the map by clicking on them (C). As soon as a station is selected its water quality indicators are shown on the chart next to the map (D). The display period starts at the beginning of the simulation run (1/7/2008 by default) and ends on the user requested assessment date.
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Figure 37. Results display of the management implementation option.
Figure 38. Results display of the biophysical response model.
The “Upper Catchment Monitoring” and “Marine Monitoring” Tabs (Figure 38) will display similar results as displayed in Figure 33. The top menu again enables the user to choose between two data visualisation modes. The monitoring station on the map is selected by clicking on it. As soon as the monitoring station is selected its water quality indicators are shown on the chart next to the map.
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Comparing report card grades The user can access Report Card grades at upper catchments, estuaries and Marine regions during the assessment period by selecting any of the three respective assessor tabs (Figure 39A). The top menu allows the user to choose between three data visualisation modes in similar fashion to the visualisation adopted by the Healthy Waterways report card, but also with graphical and tabular options (B). The user can select the regions whereby the report card grades will be displayed (C). Scores are calculated for all upper catchments, estuarine and marine regions during the assessment period.
Figure 39. Results display of the report card assessment model.
Social perception assessment If the user selects the “Social Perception” Tab (Figure 40A), the social responses to the intervention during the assessment period can be visualised. The top button displays and compares in different regions (B) the perceptions of social values (C) by different groups (D). The user can add new plots (E). The box-and-whiskers plot shows the evolution of these perceptions and their range of variation every year, based on the Report Card scores.
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Figure 40. Results display of the social perception model. The black vertical line in middle of each bar is the median value of all respondents, the top whisker is the maximum value of all panel members and the bottom whisker is the minimum of all panel members.
Economics assessment If the user selects the “Economics Model” Tab (Figure 41A), management costs and benefits information during the assessment period are displayed. The user can select between three different reports (B). The Management Cost Tab provides a summary of how much money was spent so far and how much money is committed to the future (C). The chart below the budget summary shows the evolution of investments and maintenance costs broken down by management actions (D). The following chart (E) includes the cost per household comparison by catchment region. The user can visualise also the Cumulative Management Costs (Total and NPV) and a table containing management cost details (Figure 42A and E) whilst in the management costs tab.
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Figure 41. Results display for “Management Costs” in the economic model tab.
The “Benefits” of the economic model (Figure 43A) displays the percentage water quality improvement trend (B), benefit per household due to water quality improvement by catchment region (C) and cumulative benefits (Figure 44A and B). The Cost Effectiveness Report provides a chart (Figure 45A) that summarises the evolution of the benefits per household due to water quality improvements per management action per household and a summary table (B) containing management costs per household, percentage of water quality improvement, benefit per household and a measure of effectiveness (household benefit per household cost).
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Figure 42. Results display for “Management Costs” in the economic model showing cumulative management costs and management cost details.
Figure 43. Results display for “Benefits” in the economic model tab showing percentage water quality improvements by region and benefit per household due to water quality improvement by region.
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Figure 44. Results display for “Benefits” in the economic model tab showing cumulative benefits per households in selected management regions and cumulative total benefits to households in all regions.
Figure 45. Results display for “Cost Effectiveness” in the economic model tab.
Finally, the user can click on the “Next” button (Figure 45C) to move to the next implementation phase, where the strategy can continue to be implemented or modifies according to results from the previous assessment period. By default the user runs the interactive open-loop MSE for a period of 30 virtual years and assessments can be made at 1, 2 and 5 years intervals or until the end of the run. At the end of the run the user can record and re-name the strategy, and if required retrieve it.
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Visualising previously recorded strategies A return to the “Manager” Window (Figure 33) allows the user to visualise a previously recorded strategy (Figure 46). The user can select the strategy to be visualised on the left hand side of the window (A), select which regions to display the management actions implemented (B). The results are presented graphically showing the number of cells in which the management action was implemented in all managed regions (C).
Figure 46. User management history by actions for all selected regions.
The user can choose to visualise management actions details of recorded strategies (Figure 47A). The details for each of the recorded strategies can be selected on the left hand side of the window by clicking on the strategy (B). The user can choose the region to configure (C) whereby results will be displayed. Alternatively, the user can select to visualise details of all managed regions (D). The total number of management actions and their associated costs are displayed in a table (E). Installation and maintenance costs can be visualised by clicking in the “+” button. The user can visualise the details of management actions in each assessment period (F).
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Figure 47. Detailed management actions for previously recorded strategies.
Visualising results of previously recorded strategies Back to the “Manager” Window (Figure 33) allows the user to visualise the results of previously recorded strategies and compare their outcomes through summaries of report card grades, social perception and economic results (Figure 48). This functionality was used extensively in chapter 1.
Figure 48. Summary results of previously recorded strategies.
Another return to the “Manager” Window (Figure 33) allows the user to add more runs to previously recorded strategies, delete previously recorded runs, and choose the regions for results to be displayed.
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APPENDIX 5 – GLOSSARY OF TERMS Social perception model terms Social group People who share some social relation, for example demographic, economic or social characteristics such as age, sex, education level, race religion, income level, lifestyle, beliefs, etc. In the MSE-system the following social groups are assessed: Indigenous (local): Local indigenous people. Indigenous (non-local): Indigenous people who identify a connection to land and sea country but do not currently live on those lands. NGOs / Environmental individuals/groups: Non-Government Organisations, and other individuals or groups with an interest in environmental matters. Recreational general: Groups and individuals who use waterways and coastal areas for a variety of general recreational activities. Recreational water sports: Groups and individuals who use waterways and coastal areas for a variety of recreational water sports. Rural Residents (long-time): Residents in rural areas for a period (nominally) of over 10 years. Rural Residents (short-time): Residents of rural areas, for a period period (nominally) of under 10 years. Residents (coastal i.e. with): Residents living within one 1km coastline and includes island residents (which in some cases are slightly more than 1km from the coastline). Scientific/research: The individuals or groups working in a scientific or research context. Tourism operators: Tourism operators in the catchment or region, and in surrounding areas. Tourists (domestic): Tourists visiting SEQ from within Australia. Tourists (international): Tourists visiting SEQ from overseas. Urban Residents (long-time): Residents in urban areas for a period (nominally) of over 10 years. Urban Residents (short-time): Residents in urban areas for a period (nominally) of under 10 years.
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Social aspects/values Qualities valued by the social groups. For the MSE-system we grouped social values in three categories. The first is related to people’s perception about water quantity and quality and its capacity to support life, in addition to aesthetic value. The second encompasses perceptions of ecosystem services and, the third reflects social capital. A detailed account of each of the social values is described below: Aesthetics: The pleasing aspect of the way the waterways and environments surrounding waterways appears. Biodiversity – coastal: The variability among living organisms in coastal areas from all sources including terrestrial, marine, and other aquatic ecosystems and the ecological complexes of which they are part; this includes diversity within and among species and diversity within and among habitats and ecosystems. Biodiversity – estuaries: The variability among living organisms in estuarine areas from all sources including terrestrial, marine, and other aquatic ecosystems and the ecological complexes of which they are part; this includes diversity within and among species and diversity within and among ecosystems. Biodiversity – wetlands: The variability among living organisms in wetland areas from all sources including terrestrial, marine, and other aquatic ecosystems and the ecological complexes of which they are part; this includes diversity within and among species and diversity within and among ecosystems. Clarity (W) Water: Water clearness allows good visibility. Colour/smell free (W) Water: Water is free of unwanted colours and smells. Flows (W) (Water): Water levels and flows allow sufficient availability of water to meet various needs. Food Health: Strength, feeling well, and having a good functional capacity (from food e.g. seafood); also connotes an absence of disease (e.g. from seafood). Iconic species: Typically, charismatic megafauna such as turtles, dugongs, whales, dolphins, seabirds and migratory waterbirds. Pollutant free (W) Water: Water is free of chemical and biological pollutants. Water Health: Strength, feeling well, and having a good functional capacity (from water); also connotes an absence of disease (from water).
Ecosystem services The benefits people obtain from ecosystems. These include provisioning services such as food and water; regulating services such as flood and disease control; cultural services such as spiritual, recreational, and cultural benefits; and supporting services such as nutrient cycling that maintain the conditions for life on Earth. The concept “ecosystem goods and services” is synonymous with ecosystem services. The ecosystem services included in the MSE-system are the following: Air purification: A regulating ecosystem function that removes pollutants from the air via interaction of the atmosphere with waterways, such as regulation of greenhouse gases (especially carbon dioxide).
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Artificial buffering: The physical reductions of impacts, e.g. tidal surge/flooding, of streambank and coastal erosion by the presence of artificial structures such as seawalls. Bequest or endowment (inter-generational equity): The value of the environmental (particularly waterways) bestowed to future generations. Community organisations: Good social relations related to community organisations are “social cohesion, mutual respect, good gender and community relations, and the ability to help others.” Cultural: The nonmaterial benefits people obtain from ecosystems through spiritual enrichment, cognitive development, reflection, recreation and aesthetic experience, including, for example, knowledge systems, social relations, and aesthetic values. Family & friends interaction: Good social relations related specifically to family and friends, specifically “social cohesion, mutual respect, good gender and family relations, and the ability to help others and provide for children.” Intrinsic: The value of someone or something for itself, irrespective of its utility for someone else. Local community well-being: A context- and situation-dependent (local community) state, comprising basic material for a good life, freedom and choice, health, good social relations, and security. Networks: It is the value social networks have in bonding similar people and bridging between diverse people, with norms of reciprocity. The core point is that social networks have a value as they can increase productivity of individuals or groups. Nutrient cycling: A regulating ecosystem function that cycles nutrients via interaction with waterways. Personal well-being: A context- and situation-dependent (personal) state, comprising basic material for a good life, freedom and choice, health, good social relations, and security. Social Capital: same as networks. Spiritual: Values linked to the non-physical, intangible matters lacking form or substance that are related to the spirit, mind or soul. Stream/coast buffering: The reduction of impacts, e.g. tidal surge/flooding, of streambank and coastal erosion by the presence of wetlands, mangroves, saltmarshes and other vegetation and natural structures. Waste cycling: A regulating ecosystem function that cycles wastes via interaction with waterways.
Economic model terms Achieved percent improvement: The positive change in water quality percent that are calculated by changes in the report card grade.
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Annual maintenance: Costs to maintain the management action. For example, upgrading a sewage treatment plant requires regular purchase of chemicals, but also to pay salaries for employees. Benefit per household: The dollar benefits attributable to each household to a 1% rise (or fall) in water quality. Current Value: The financial values in current terms i.e. not discounted. Discounted benefits: Benefit values discounted by the nominated discount rate. Discounted costs: Management costs discounted by the nominated discount rate (see discount rate). Discount rate: A rate used to calculate the present value by applying a discount rate (5.5%) to a capital sum, and in this case the capital sums may be either costs or benefits.2 Installation costs: The capital costs required implementing a management action. For example, the installation costs for riparian revegetation include the costs to buy seedlings, fencing, etc. Net Present Value: The discounted value of a financial sum at some point in the future due to financial flows over a number of years. WSUD: Water Sensitive Urban Design – is a planning and design approach that aims to ensure urban development is sensitive to natural hydrological and ecological cycles by conserving water supplies, minimising wastewater, and managing stormwater quality and flows.
Management action terms Best Farming Practices: Farming practices aimed at reducing sediment and nutrient runoff. Depending on the farming system, best practices can include: fencing of stream banks and ponds to keep cattle away from streams, low impact grazing, limited use of fertilisers or contour ploughing. Best Urban Design: Engineering measures aimed at reducing urban runoff and increasing water re-use. Based on Water Sensitive Urban Design (WSUD) principles, best urban design includes: domestic re-use of waste waters, creation of urban pervious surfaces, collect of rainwater and stormwater or creation of urban wetlands. Riparian revegetation: Establishment of plant communities on stream banks to reduce erosion, maintain biodiversity and improve riparian aesthetics. Typically, riparian revegetation includes a mix of tree, shrub and grass communities. Costs vary significantly depending on whether interventions take place in rural, urban or natural environments. Rural Stormwater: Engineering measures aimed at filtering waters from rural drainage systems before they reach river streams. Based on Stormwater Quality Improvement Device (SQUID) concepts, interventions include: artificial wetlands, gross pollutant traps, sediment basins or a combination of these. Sewage Treatment Plant (STP) upgrade: Upgrade of sewage treatment plants (STP) to deliver Total nitrogen and Total Phosphorous concentrations of 2mg/L at the STP outfall.
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Urban Stormwater: Engineering measures aimed at filtering waters or reducing runoff from urban areas. Interventions include: grassed buffers, gross pollutant traps or retrofitting of existing stormwater network with retention tanks.
Biophysical model terms Catchment: A geographical area naturally draining its surface waters to a single point (outlet) located on a hydrological network. Chlorophyll a: Photosynthetic pigment found in all green plants, and the main pigment in algae. Concentrations of Chlorophyll a are provided in µg/L. • Chlorophyll a in freshwater regions: Chlorophyll a is used to estimate the amount of algae growing in a stream for a period of 3-4 weeks under ambient conditions. • Chlorophyll a in estuaries/marine regions: Chlorophyll a is measured to provide an indication of phytoplankton biomass. Ecosystem Health Monitoring Program (EHMP): Marine, estuarine and freshwater monitoring program managed by the Healthy Waterways Ltd on behalf of its various partners. The EHMP is implemented in 19 major catchments of South East Queensland, 18 river estuaries and across Moreton Bay. Ecosystem Health Index (EHI): Measure of how much of a waterway’s area complies with the defined water quality objectives assigned by the Queensland Water Quality Guidelines. The guidelines are set by the Queensland Department of Environment and Resource Management (DERM, previously known as EPA). Dissolved oxygen: Dissolved oxygen (DO) concentration is a critical indicator of a water body's ability to support healthy ecosystems, measured both in freshwater and estuarine/marine regions of the EHMP. Aquatic microorganisms use oxygen to breakdown organic materials, such as manure, sewage effluents and decomposing algae. DO is measured as a concentration in mg/L and recalculated using temperature to return percentage saturation in the EHMP. Drainage basin: Same as catchment. Catchment is the term used by the Healthy Waterways Partnership. Drainage sub-basin: Same as sub-catchment. Empirical model: Model based on observed data. An example is the statistical biophysical model used in the SEQ MSE-system that uses data relationships acquired through the EHMP. Estuarine Monitoring Stations: Monitoring station located in estuaries, i.e. streams affected by tides. Estuarine/Marine Regions: Water clarity measures light penetration through a Secchi disk Freshwater regions: Water clarity is a measure of light scattering calculated with a turbidity meter which giving results in NTU (Nephelometric Turbidity Unit). Estuarine Region: Encompasses the streams that are subjected to tidal influence where fresh and saltwater may be mixed.
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Lyngbya: Marine blue - green algae (cyanobacteria) that is not poisonous to fish but may cause poisoning to animals that eat the fish that eat the algae. Blooms of Lyngbya can cause bad smell and human skin irritation. Management action: Human intervention in the system to achieve management objectives. Management intervention: same as management action. Marine Monitoring Stations: Stations located in the marine region (Moreton Bay and coastal waters outside Moreton Bay) where regular water quality and biological samples are collected on a regular basis. Marine Region: Includes Moreton Bay from the mouth of rivers to coastal waters at Noosa and Gold Coast catchments. Monitoring station: A monitoring station part of the Ecosystem Health Monitoring Program (EHMP). There are 389 freshwater, estuarine or marine monitoring stations as part of the EHMP, where 135 are freshwater monitoring stations and 253 are estuarine/marine monitoring stations. Each report card is located on a report card region divided in water types. Nitrogen stable isotope signature (δ15N): The ratio of 15N to 14N stable isotopes within a substance (e.g. aquatic plants). δ15N can be used to identify changes to the natural cycling of nitrogen in streams due to both point and diffuse sources. Nutrients: Substance that provides food or nourishment, such as usable proteins, vitamins, minerals or carbohydrates. Nutrients can stimulate the growth of macro and micro algae (including Lyngbia). Blooms of these can displace endemic species, reduce water clarity, negatively affecting benthic communities. Phosphorus and nitrogen, in the form of Total Nitrogen (TN) and Total Phosphorous (TP) are the nutrients measured in the EHMP. Total nitrogen: Total Nitrogen (TN) is a measure of dissolved and particulate forms of nitrogen. Dissolved nitrogen is the sum of nitrate, nitrite, ammonia or organic nitrogen (dissolved proteins and urea). Total phosphorous: Total Phosphorous (TP) includes the amount of phosphorus in solution (reactive) and in particle form. TP is the commonly the in the primary productivity of surface water bodies. Main sources of phosphorous include agricultural drainage, wastewater, and certain industrial discharges. pH: Measure of the acidity of a substance. The pH scale ranges 0 (acidic) to 7 (neutral) through to 14 (alkaline). Process-based model: Mechanistic knowledge-based modelling approach based derived from a conceptual understanding on how a given system works. For example, a process-based catchment model can have a combination of processes that are modelled to predict hydrologic behaviour of catchments, such as runoff model that calculates water volume after losses are calculated from filtration and evaporation models. Random Forest model: Statistical model used in the MSE-system that generates predictions of water quality variables in the bay from measurements of water quality variables in the river mouths. The statistical model is specified by parameters that have been estimated from prior analysis of water quality data on a historical data set (EHMP from 2000 to 2008).
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The MSE Tool random forest model is a collection of 500 decision trees (tree-like graph or model of decisions and their possible consequences), each fit to random samples of historical water quality data. Prediction from a random forest is achieved by averaging the predictions on each of the decision trees. Random forests have been shown to have good predictive accuracy at the expense of interpretability. Report Card Grade: Ecosystem Health Report Card Grades (‘A’ to ‘F’) are generated for 19 catchments and 18 estuaries in South East Queensland and Moreton Bay. Parameters for freshwater, estuarine and marine ecosystems are assessed against guidelines resulting in the determination of a single grade for each system. • Excellent (A): Conditions meet all set ecosystem health values; all key processes are functional and all critical habitats are in near pristine condition. • Good (B): Conditions meet all set ecosystem health values in most of the reporting region; most key processes are functional and most critical habitats are intact. • Fair (C): Conditions meet some of the set ecosystem health values in most of the reporting region; some key processes are functional and some critical habitats are impacted. • Poor (D): Conditions are unlikely to meet set ecosystem health values in most of the reporting region; many key processes are not functional and many critical habitats are impacted. • Fail (F): Conditions do not meet set ecosystem health values; most key processes are not functional and most critical habitats are severely impacted. Report Card: The Report Card presents an ‘A’ to ‘F’ health rating for the waterways of South East Queensland (SEQ) and Moreton Bay. It provides a ‘snapshot’ of the ecosystem health in each of the three report card regions. Report Card Regions: Areas located in upper catchments, estuaries and marine regions used to calculate the annual report card scores. Each report card region contains distinctive water types with distinctive water quality objectives (thresholds). Riparian vegetation: Plant communities located on the margins of streams. See riparian revegetation. Simulated stochastic observations: Reproduce future observations based on past observations that are randomly determined. Sub-catchment: Term applied to the smaller catchments, which make up and provide water into a larger catchment. Total Suspended Solids: A measure of the material suspended in the water column. Total suspended solids (TSS) cause reduction in water clarity, contribute to increase sedimentation rates smothering benthic species potentially reducing aquatic habitats. TSS also carries nutrients that cause algal blooms and other toxic pollutants. Upper Catchment Region: upper reaches of catchments where water collects as runoff after rainfall or is released from groundwater reserves. Water Quality: Includes the biological, physical and chemical characteristics of a water body. Water quality is normally a concept affected by natural processes and human activities. However, from a natural resources management standpoint it measures how good the water is in terms of meeting its environmental values. Hence ‘good’ or ‘bad’ water quality can be used.
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Model terms Closed-Loop: The closed loop MSE-system is a fully operational and automated simulator. The closed-loop approach allows users to explore future scenarios under environmental, social and economic uncertainties. In this configuration, the MSEsystem modifies its management decisions based on environmental, social and economic responses generated by interconnected sub-models. In order to build and refine the learning and decision-making sub-models, an open-loop version is also available for users to train the MSE-system (see open-loop). Management Action: A set of pre-defined actions that will positively influence total nitrogen and total sediment loads at a given monitoring site (see EHMP). There are currently six Management Actions that are implemented in the MSE-system and these are: (1) Riparian revegetation; (2) Rural Stormwater; (3) Best Farming Practices; (4) Best Urban Design; (5) Urban Stormwater; and (6) Sewage Treatment Plant (STP) upgrades (see Management Action Terms) Model: A software component which provides output data based on a configured set of input data. To be used in the MSE, a model must be bale to describe its input and output data in the form of parameters. This allows the model to be connected to other models and data services when the MSE is configured MSE configuration: Specifies or defines the MSE-system and how the models are connected to each other and to the initialisation data. MSE Tool: The software framework responsible for integrating model for MSE. It is a set of integrated composite models that exchange information through Object Oriented Programming. Open-Loop: The open-loop MSE mode allows users to use the MSE Tool as a flight simulator in order to learn from different management strategies under various environmental, social and economic uncertainties. In turn, this information can be used to train the learning and decision-making sub-models when the MSE-system is used in a closed-loop configuration (see closed-loop). Run: Run a model means launching or resuming a simulation in the MSE-system according to the MSE configuration previously chosen by the user. Time step: A mechanism allowing a model to propagate through time. At each time step, outputs from the previous time step (iteration) are used as inputs and the model calculates new outputs. MSE-system uses a monthly time step. Water Quality Assessor: A sub-model assessing biophysical responses of each monitoring station and producing report card scores (A to F). XML: Extensible Markup Language. It is a way to create common information formats and share both the format and the data.
Glossary Sources Barbier, E.B.; Acreman, M. and Knowler, D. (1997). Economic valuation of wetlands: A guide for policy makers and planners, Ramsar Convention Bureau Gland, Switzerland, 143pp. Bleanney, A (2007). Risk Awareness and Incident Response Capability in Water
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Catchments in North Eastern Tasmania, Australia – A Community Based Audit. Journal of Tasmanian Community Resource Auditors Incorporated vol. 3(3), pp. 5-64. CSA Glossary (2010). Available online at: http://www.csa.com/discoveryguides/models/gloss.php. Accessed on 20/04/2010. CoastalResearch.nl (2010). Available online at: http://www.coastalresearch.nl/glossary/5/view. Accessed on 19/04/2010. EHMP (2008) Ecosystem Health Monitoring Program - Annual Technical Report 2006-07 on the health of the freshwater, estuarine and marine waterways of South East Queensland. Brisbane, South East Queensland Healthy Waterways Partnership. Available at: http://www.healthywaterways.org/_uploads/ehmp/filelibrary/200607_methods.pdf Glossary for WordNet system (2010). Available online at: http://wordnetweb.princeton.edu/perl/webwn?s=social%20group. Accessed on 19/04/2010. GreenFacts (2010). Available online at: http://www.greenfacts.org/glossary/index.htm Millenium Ecosystem Assessment (2003) 'Ecosystems and human well-being: a framework for assessment ' World Resources INstitute, Washington. Minnesota Pollution Control Agency Glossary (2009) Available online at: http://www.pca.state.mn.us/gloss/index.cfm. Accessed on 19/04/2010. Social Capital Research: http://www.socialcapitalresearch.com/definition.html. Accessed on 21/04/2010.
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