Jenifer Lyn Ticehurst

A Bayesian network approach for assessing the sustainability of coastal lakes in New South Wales, Australia

Coastal lakes are ecosystems of significant value generating many ecological, social and economic benefits. Increasing demands resulting from urban development and other human activities within coastal lake catchments have the potential to result in their degradation and can lead to conflicts, for example between lake users and upstream communities. There are many techniques that can be used to integrate the variables involved in such conflicts including system dynamics, meta-modelling, and coupled component models, but many of these techniques are too complex for catchment managers to employ on a routine basis. The overall result is the potential to compromise the sustainability of these important ecosystems. This paper describes research to address this problem. It presents the development of an integrated model framework based on a Bayesian network (Bn). Bns are used to assess the sustainability of eight coastal lake-catchment systems, located on the coast of New South Wales (NSW), Australia. The paper describes the potential advantages in the use of Bns and the methods used to develop their frameworks. A case study application for the Cudgen Lake of northern NSW is presented to illustrate the techniques. The case study includes a description of the relevant management issues being considered, the model framework and the techniques used to derive input data. Results for the case study application and their implications for management are presented and discussed. Finally, the directions for future research and a discussion of the applicability of Bn techniques to support management in similar situations are proffered.

Using Bayesian Networks to complement conventional analyses to explore landholder management of native vegetation

Influencing the management of private landholders is a key element of improving the condition of Australias natural resources. Despite substantial investment by governments, effecting behavioural change on a scale likely to stem biodiversity losses has proven difficult. Understanding landholder decision-making is now acknowledged as fundamental to achieving better policy outcomes. There is a large body of research examining landholder adoption of conservation practices. Social researchers are able to employ a suite of conventional techniques to analyse their survey data and assist in identifying significant and causal relationships between variables. However, these techniques can be limited by the type of data available, the breadth of issues that can be represented and the extent that causality can be explored. In this paper we discuss the findings of a unique study exploring the benefits of combining Bayesian Networks (BNs) with conventional statistical analysis to examine landholder adoption. Our research examined the landholder fencing of native bushland in the Wimmera region in south east Australia. Findings from this study suggest that BNs provided enhanced understanding of the presence and strength of causal relationships. There was also the additional benefit that a BN could be quickly developed and that this process helped the research team clarify and understand relationships between variables. However, a key finding was that the interpretation of the results of the BNs was complemented by the conventional data analysis and expert review. An additional benefit of the BNs is their capacity to present key findings in a format that is more easily interpreted by researchers and enables researchers to more easily communicate their findings to natural resource practitioners and policy makers.

Integration modelling and decision support: A case study of the Coastal Lake Assessment and Management (CLAM) Tool

Decision Support Tools (DSTs) are designed to assist in making more informed management decisions, through prediction of the outcomes from various future scenarios and as an education resource. The many coastal lakes in New South Wales, Australia are areas where DSTs are able to assist in making management and planning decisions. A variety of economic, ecological and social demands on the lakes and their catchments finite resources are increasing conflict over their use and sustainable management. The issues are intricately linked, so that understanding trade-offs and making management decisions about coastal lakes and their catchments requires knowledge of the processes and interactions between all key components of the system. This is a complex problem requiring the integration of, often minimal, information, from various disciplines. This paper describes an approach for developing a DST to provide information about the potential impacts of management decisions on key components of a coastal lake system. Integration of the catchment components was completed using a Bayesian Decision Network (BDN). This paper uses a case study of a DST for Merimbula Lake on the east coast of Australia to illustrate the strengths of the BDN approach, and to show how the design of the DST helps to address some of the limitations inherent in the integrative method.

Using a physically based model to conduct a sensitivity analysis of subsurface lateral flow in south-east Australia

Abstract Break of slope tree plantations are intended to intercept surface and subsurface lateral flow (SLF) on hillslopes and help alleviate the water imbalance in the agricultural landscapes of south-east Australia. More information on the occurrence of SLF in this region is required to assist the efficient identification of potential plantation sites. In this article, HILLS, a two-dimensional physically based model, is used to examine the sensitivity of SLF to rainfall, soil, and topographic attributes. The most influential soil property was the depth of the impeding layer. Gradient within 33 m above where SLF is determined was the topographic criteria that most effected SLF, regardless of the overall topographic shape. It was clear that as the amount of annual rainfall increased, higher hillslope gradients were required to move the excess water as SLF. Cumulative errors in the model water balance created uncertainty about the accuracy of the results, however, the general trends seem reliable. SLF did not account for a significant proportion of rainfall on the hypothetical hillslope considered. Field evidence suggests that more SLF occurs in the study region than is suggested from these results. Soil, topographic and rainfall properties alone may not be adequate to explain the occurrence of this type of flow, and the presence of a watertable may also be critical.

Formal Scenario Development for Environmental Impact Assessment Studies

Abstract Scenario analysis is a process of evaluating possible future events through the consideration of alternative plausible, though not equally likely, states (scenarios). The analysis is designed to enable improved decision making and assessment through a more rigorous evaluation of possible outcomes and their implications. For environmental impact and integrated assessment studies, the process of scenario development typically involves making explicit and/or implicit assumptions about potential future conditions, such as climate change, land cover and land use changes, population growth, economic development and technological changes. Realistic assessment of scenario impacts often requires complex integrated modelling frameworks that represent environmental and socioeconomic systems to the best of our knowledge, including assumptions about plausible future conditions. In addition, scenarios have to be developed in a context relevant to the stakeholders involved, and include estimation and communication of uncertainties, to establish transparency, credibility and relevance of scenario results among the stakeholders. This paper reviews the state of the art of scenario development and analysis, proposes a formal approach to scenario development in environmental studies and discusses existing issues. Major recommendations for future research in this area include proper consideration of uncertainty involved in scenario studies, construction of scenarios of a more variable nature, and sharing of information and resources among the scenario development research community.

Model design for the hydrology of tree belt plantations on hillslopes

When selecting or developing a model to use for research it is important that the model structure and complexity meet the objectives of the research while avoiding problems from overparameterisation. In this paper a procedure is outlined for the selection or development of a model to be used to assist in locating and designing tree belt plantations on hillslopes. Sensitivity analysis and field data interpretation are used to define the important hillslope properties and processes occurring at a field site in southern New South Wales. These are combined with the research objectives to identify the model requirements for further study on tree belt plantations. A brief review of potentially suitable models available reveals that no single model meets all of the requirements. It is concluded that field data should be used to develop a simple cascading bucket model for hillslope hydrology using a top-down approach.

Realizing modelling outcomes: A synthesis of success factors and their use in a retrospective analysis of 15 Australian water resource projects

Abstract We review several papers that have afforded insights into determinants of positive outcomes (e.g. the adoption of tools, improved learning and/or collaboration) from modelling projects. From a subsequent internet search in the environmental domain we identified 33 such factors that are then invoked in a transferable survey-based method to facilitate structured reflections by model developers on 15 projects. Four factors were considered most necessary to realize overall success for any modelling project. Three factors related to aspects of stakeholder engagement in the modelling process; the other to critical thinking around problem framing and the role(s) of models. The latter factor was considered reasonably well-achieved across the projects. Harder to control were the stakeholder engagement factors which, along with project management considerations, can constrain or enable achievement of other factors. The paper provides further evidence of the critical need to consider non-technical aspects in the design and implementation of modelling projects.

Modelling for Managing the Complex Issue of Catchment-Scale Surface and Groundwater Allocation

Kinetic Analysis of the Coke Calcination Processes in Rotary Kilns.- Behavior of Elastomeric Seismic Isolators Varying Rubber Material and Pad Thickness: A Numerical Insights.- Numerical Simulation of Coastal Flows in Open Multiply-connected Irregular Domains.- System Dynamics and Agent-based Simulation for Prospective Health Technology Assessments.- Simple and Efficient Algorithms to get a Finer Resolution in a Stochastic Discrete Time Agent-based Simulation.- Numerical Study of Turbulent Boundary-layer Flow Induced by a Sphere above a Flat Plate.- Airflow and Particle Deposition in a Dry Powder Inhaler: An Integrated CFD Approach.Air pollution caused by small particles is a major public health problem in many cities of the world. One of the most contaminated cities is Mexico City. The fact that it is located in a volcanic crater surrounded by mountains helps thermal inversion and imply a huge pollution problem by trapping a thick layer of smog that float over the city. Modeling air pollution is a political and administrative important issue due to the fact that the prediction of critical events should guide decision making. The need for countermeasures against such episodes requires predicting with accuracy and in advance relevant indicators of air pollution, such are particles smaller than 2.5 microns (PM 2.5). In this work two different fuzzy approaches for modeling PM 2.5 concentrations in Mexico City metropolitan area are compared with respect the simple persistence method.