Integrating economic values and catchment modelling

Integrated catchment policies are widely used to manage natural resources in Australian catchments. Decision support tools available to aid integrated catchment management are often limited in their integration of environmental processes with socio-economic systems. Fully integrated models are required to support assessments of the environmental and economic trade-offs of catchment management changes. A Bayesian Network (BN) model is demonstrated to provide a suitable approach to integrate environmental modelling with economic valuation. The model incorporates hydrological, ecological and economic models for the George catchment in Tasmania. Information about the non-market costs and benefits of environmental changes is elicited using Choice Experiments, allowing an assessment of the efficiency of alternative management scenarios.Integrated catchment modelling, Bayesian networks, Uncertainty, Environmental values, Non-market valuation, Choice Modelling.,

Integrated Hydro-Economic Modelling: Challenges and Experiences in an Australian Catchment

Integrated catchment policies are widely used to manage natural resources in Australian catchments. Integration of environmental processes with socio-economic systems is often difficult due to the limitations of decision support tools. To support assessments of the environmental and economic trade-offs of changes in catchment management, fully integrated models are needed. This research demonstrates a Bayesian Network (BN) approach to integrating environmental modelling with economic valuation. The model incorporates hydrological, ecological and economic models for the George catchment in Tasmania. Choice experiments were used to elicit information about the non-market costs and benefits of environmental changes. This allows the efficiency of alternative management scenarios to be assessed.Hydro-economic modelling, Integrated catchment modelling, Ecological modelling, Valuation, Bayesian networks, Water quality, Community/Rural/Urban Development, Environmental Economics and Policy, Land Economics/Use,

Predicting water quality and ecological responses

Abstract Changes to climate are predicted to have effects on freshwater streams. Stream flows are likely to change, with implications for freshwater ecosystems and water quality. Other stressors such as population growth, community preferences and management policies can be expected to interact in various ways with climate change and stream flows, and outcomes for freshwater ecosystems and water quality are uncertain. Managers of freshwater ecosystems and water supplies could benefit from being able to predict the scales of likely changes. This project has developed and applied a linked modelling framework to assess climate change impacts on water quality regimes and ecological responses. The framework is designed to inform water planning and climate adaptation activities. It integrates quantitative tools, and predicts relationships between future climate, human activities, water quality and ecology, thereby filling a gap left by the considerable research effort so far invested in predicting stream flows. The modelling framework allows managers to explore potential changes in the water quality and ecology of freshwater systems in response to plausible scenarios for climate change and management adaptations. Although set up for the Upper Murrumbidgee River catchment in southern NSW and ACT, the framework was planned to be transferable to other regions where suitable data are available. The approach and learning from the project appear to have the potential to be broadly applicable. We selected six climate scenarios representing minor, moderate and major changes in flow characteristics for 1oC and 2oC temperature increases. These were combined with four plausible alternative management adaptations that might be used to modify water supply, urban water demand and stream flow regimes in the Upper Murrumbidgee catchment. The Bayesian Network (BN) model structure we used was developed using both a ‘top down’ and ‘bottom up’ approach. From analyses combined with expert advice, we identified the causal structure linking climate variables to stream flow, water quality attributes, land management and ecological responses (top down). The ‘bottom up’ approach focused on key ecological outcomes and key drivers, and helped produce efficient models. The result was six models for macroinvertebrates, and one for fish. In the macroinvertebrate BN models, nodes were discretised using statistical/empirical derived thresholds using new techniques. The framework made it possible to explore how ecological communities respond to changes in climate and management activities. Particularly, we focused on the effects of water quality and quantity on ecological responses. The models showed a strong regional response reflecting differences across 18 regions in the catchment. In two regions the management alternatives were predicted to have stronger effects than climate change. In three other regions the predicted response to climate change was stronger. Analyses of water quality suggested minor changes in the probability of water quality exceeding thresholds designed to protect aquatic ecosystems. The ‘bottom up’ approach limited the framework’s transferability by being specific to the Upper Murrumbidgee catchment data. Indeed, to meet stakeholder questions models need to be specifically tailored. Therefore the report proposes a general model-building framework for transferring the approach, rather than the models, to other regions.  Please cite this report as: Dyer, F, El Sawah, S, Lucena-Moya, P, Harrison, E, Croke, B, Tschierschke, A, Griffiths, R, Brawata, R, Kath, J, Reynoldson, T, Jakeman, T 2013 Predicting water quality and ecological responses, National Climate Change Adaptation Research Facility, Gold Coast, pp. 110 Changes to climate are predicted to have effects on freshwater streams. Stream flows are likely to change, with implications for freshwater ecosystems and water quality. Other stressors such as population growth, community preferences and management policies can be expected to interact in various ways with climate change and stream flows, and outcomes for freshwater ecosystems and water quality are uncertain. Managers of freshwater ecosystems and water supplies could benefit from being able to predict the scales of likely changes. This project has developed and applied a linked modelling framework to assess climate change impacts on water quality regimes and ecological responses. The framework is designed to inform water planning and climate adaptation activities. It integrates quantitative tools, and predicts relationships between future climate, human activities, water quality and ecology, thereby filling a gap left by the considerable research effort so far invested in predicting stream flows. The modelling framework allows managers to explore potential changes in the water quality and ecology of freshwater systems in response to plausible scenarios for climate change and management adaptations. Although set up for the Upper Murrumbidgee River catchment in southern NSW and ACT, the framework was planned to be transferable to other regions where suitable data are available. The approach and learning from the project appear to have the potential to be broadly applicable. We selected six climate scenarios representing minor, moderate and major changes in flow characteristics for 1oC and 2oC temperature increases. These were combined with four plausible alternative management adaptations that might be used to modify water supply, urban water demand and stream flow regimes in the Upper Murrumbidgee catchment. The Bayesian Network (BN) model structure we used was developed using both a ‘top down’ and ‘bottom up’ approach. From analyses combined with expert advice, we identified the causal structure linking climate variables to stream flow, water quality attributes, land management and ecological responses (top down). The ‘bottom up’ approach focused on key ecological outcomes and key drivers, and helped produce efficient models. The result was six models for macroinvertebrates, and one for fish. In the macroinvertebrate BN models, nodes were discretised using statistical/empirical derived thresholds using new techniques. The framework made it possible to explore how ecological communities respond to changes in climate and management activities. Particularly, we focused on the effects of water quality and quantity on ecological responses. The models showed a strong regional response reflecting differences across 18 regions in the catchment. In two regions the management alternatives were predicted to have stronger effects than climate change. In three other regions the predicted response to climate change was stronger. Analyses of water quality suggested minor changes in the probability of water quality exceeding thresholds designed to protect aquatic ecosystems. The ‘bottom up’ approach limited the framework’s transferability by being specific to the Upper Murrumbidgee catchment data. Indeed, to meet stakeholder questions models need to be specifically tailored. Therefore the report proposes a general model-building framework for transferring the approach, rather than the models, to other regions.&nbsp

Water requirements of floodplain rivers and fisheries: existing decision support tools and pathways for development

Fisheries / Rivers / Flood plains / Hydrology / Ecology / Models / Decision support tools / Environmental impact assessment / Methodology / Databases

Potentials and Limits of Bayesian Networks to Deal with Uncertainty in the Assessment of Climate Change Adaptation Policies

Bayesian networks (BNs) have been increasingly applied to support management and decision-making processes under conditions of environmental variability and uncertainty, providing logical and holistic reasoning in complex systems since they succinctly and effectively translate causal assertions between variables into patterns of probabilistic dependence. Through a theoretical assessment of the features and the statistical rationale of BNs, and a review of specific applications to ecological modelling, natural resource management, and climate change policy issues, the present paper analyses the effectiveness of the BN model as a synthesis framework, which would allow the user to manage the uncertainty characterising the definition and implementation of climate change adaptation policies. The review will let emerge the potentials of the model to characterise, incorporate and communicate the uncertainty, with the aim to provide an efficient support to an informed and transparent decision making process. The possible drawbacks arising from the implementation of BNs are also analysed, providing potential solutions to overcome them.Adaptation to Climate Change, Bayesian Network, Uncertainty

A multi-criteria decision analysis model for ship biofouling management in the Baltic Sea

Biofouling of ship hulls form a vector for the introduction of non-indigenous organisms worldwide. Through in-creasing friction, the organisms attached to ships' hulls increase the fuel consumption, leading to both higher fuel costs and air emissions. At the same time, ship biofouling management causes both ecological risks and monetary costs. All these aspects should be considered case-specifically in the search of sustainable management strate-gies. Applying Bayesian networks, we developed a multi-criteria decision analysis model to compare biofouling management strategies in the Baltic Sea, given the characteristics of a ship, its operating profile and operational environment, considering the comprehensive environmental impact and the monetary costs. The model is dem-onstrated for three scenarios (SC1-3) and sub-scenarios (A-C), comparing the alternative biofouling management strategies in relation to NIS (non-indigenous species) introduction risk, e co-toxicological risk due to biocidal coating, carbon dioxide emissions and costs related to fuel consumption, in-water cleaning and hull coating. The scenarios demonstrate that by the careful consideration of the hull fouling management strategy, both money and environment can be saved.Peer reviewe

Adaptive management of Ramsar wetlands

Abstract The Macquarie Marshes are one of Australia’s iconic wetlands, recognised for their international importance, providing habitat for some of the continent’s more important waterbird breeding sites as well as complex and extensive flood-dependent vegetation communities. Part of the area is recognised as a wetland of international importance, under the Ramsar Convention. River regulation has affected their resilience, which may increase with climate change. Counteracting these impacts, the increased amount of environmental flow provided to the wetland through the buy-back and increased wildlife allocation have redressed some of the impacts of river regulation. This project assists in the development of an adaptive management framework for this Ramsar-listed wetland. It brings together current management and available science to provide an informed hierarchy of objectives that incorporates climate change adaptation and assists transparent management. The project adopts a generic approach allowing the framework to be transferred to other wetlands, including Ramsar-listed wetlands, supplied by rivers ranging from highly regulated to free flowing. The integration of management with science allows key indicators to be monitored that will inform management and promote increasingly informed decisions. The project involved a multi-disciplinary team of scientists and managers working on one of the more difficult challenges for Australia, exacerbated by increasing impacts of climate change on flows and inundation patterns

A Bayesian Belief Network to Infer Incentive Mechanisms to Reduce Antibiotic Use in Livestock Production

Efficient policy intervention to reduce antibiotic use in livestock production requires knowledge about the rationale underlying antibiotic usage. Animal health status and management quality are considered the two most important factors that influence farmers’ decision-making concerning antibiotic use. Information on these two factors is therefore crucial in designing incentive mechanisms. In this paper, a Bayesian belief network (BBN) is built to represent the knowledge on how these factors can directly and indirectly determine antibiotic use and the possible impact on economic incentives. Since both factors are not directly observable (i.e. latent), they are inferred from measurable variables (i.e. manifest variables) which are influenced by these factors. Using farm accounting data and registration data on antibiotic use and veterinary services in specialized finisher pig production farms, a confirmatory factor analysis was carried out to construct these factors. The BBN is then parameterized through regression analysis on the constructed factors and manifest variables. Using the BBN, possible incentive mechanisms through prices and management training are discussed.Livestock Production/Industries,

Applications of Bayesian Networks as Decision Support Tools for Water Resource Management under Climate Change and Socio-Economic Stressors: A Critical Appraisal

Bayesian networks (BNs) are widely implemented as graphical decision support tools which use probability inferences to generate “what if?” and “which is best?” analyses of potential management options for water resource management, under climate change and socio-economic stressors. This paper presents a systematic quantitative literature review of applications of BNs for decision support in water resource management. The review quantifies to what extent different types of data (quantitative and/or qualitative) are used, to what extent optimization-based and/or scenario-based approaches are adopted for decision support, and to what extent different categories of adaptation measures are evaluated. Most reviewed publications applied scenario-based approaches (68%) to evaluate the performance of management measures, whilst relatively few studies (18%) applied optimization-based approaches to optimize management measures. Institutional and social measures (62%) were mostly applied to the management of water-related concerns, followed by technological and engineered measures (47%), and ecosystem-based measures (37%). There was no significant difference in the use of quantitative and/or qualitative data across different decision support approaches (p = 0.54), or in the evaluation of different categories of management measures (p = 0.25). However, there was significant dependence (p = 0.076) between the types of management measure(s) evaluated, and the decision support approaches used for that evaluation. The potential and limitations of BN applications as decision support systems are discussed along with solutions and recommendations, thereby further facilitating the application of this promising decision support tool for future research priorities and challenges surrounding uncertain and complex water resource systems driven by multiple interactions amongst climatic and non-climatic changes. View Full-Tex