Victor Sposito

Developing a multi-scale visualisation framework for use in climate change response

Climate change is predicted to impact countries, regions and localities differently. However, common to the predicted impacts is a global trend toward increased levels of carbon dioxide and rising sea levels. Governments and communities need to take into account the likely impacts of climate on the landscape, both built and natural. There is a growing and significant body of climate change research. Much of this information produced by domain experts for a range of disciplines is complex and difficult for planners, decision makers and communities to act upon. The need to communicate often complex scientific information which can be used to assist in the planning cycle is a key challenge. This paper draws from a range of international examples of the use of visualisation in the context of landscape planning to communicate climate change impact and adaptation options within the context of the planning cycle. Missing from the literature, however, is a multi-scalar approach which allows decision makers, planners and communities to seamlessly explore scenarios at their special level of interest, as well as to collectively understand what is driving these at a larger scale, and what the implications are at ever more local levels. Visualisation tools such as digital globes provide one way to bring together multi-scaled spatial–temporal datasets. We present an initial development with this goal in mind. Future research is required to determine the best tools for communicating particular complex scientific data and also to better understand how visualisation can be used to improve the landscape planning process.

A discrete simulation approach to spatial allocation of commodity production for revenue optimisation over a local region

A simulation approach is described for the spatial allocation of crops across a region in order to maximise total revenue. The model uses inputs from GIS-based land suitability analysis to provide data on yields for a range of commodities, where the land suitability for the crops can be determined by either biophysical models or multi-criteria analysis. The objective of the study was to gain some indication of the magnitude of improvement possible in revenue, based on the convergence results for the optimisation (subject to estimated production quantities and market prices). The basic structure of the model allows for scaling up to larger problems with additional inputs and finer cell resolution. The software produces a visualisation of crop spatial allocation across the region and is compatible with statistical uncertainty analysis. The results of model simulations revealed a significant increase in revenue is possible using this approach and, when projected over the full region, suggests the possibility of significant economic benefits.

Application of GIS-based computer modelling to planning for adaption to climate change in rural areas

A GIS-based computer modelling methodology was developed and applied to identify climate change adaptation issues arising in regional agricultural production systems (including forestry). Agricultural production in Australia is very susceptible to the adverse impacts of climate change due to projected shifts in rainfall and temperature. The methodology integrates land suitability analysis with uncertainty analysis and spatial (regional) optimisation to determine optimal agricultural land use at a regional scale for current and possible future climatic conditions. The approach can be used to recognise regions under threat of productivity decline, identify alternative cropping systems that may be better adapted to likely future conditions, and investigate implementation actions to improve the sub-optimal situations created by climate change. An example of how the methodology may be used is outlined through a case study involving the South West Region of Victoria, Australia. The case study provides information on the tools available to support the formulation of a regional adaptation strategy.

Evaluation of Deterministic and Complex Analytical Hierarchy Process Methods for Agricultural Land Suitability Analysis in a Changing Climate

Land suitability analysis is employed to evaluate the appropriateness of land for a particular purpose whilst integrating both qualitative and quantitative inputs, which can be continuous in nature. However, in agricultural modelling there is often a disregard of this contiguous aspect. Therefore, some parametric procedures for suitability analysis compartmentalise units into defined membership classes. This imposition of crisp boundaries neglects the continuous formations found throughout nature and overlooks differences and inherent uncertainties found in the modelling. This research will compare two approaches to suitability analysis over three differing methods. The primary approach will use an Analytical Hierarchy Process (AHP), while the other approach will use a Fuzzy AHP over two methods; Fitted Fuzzy AHP and Nested Fuzzy AHP. Secondary to this, each method will be assessed into how it behaves in a climate change scenario to understand and highlight the role of uncertainties in model conceptualisation and structure. Outputs and comparisons between each method, in relation to area, proportion of membership classes and spatial representation, showed that fuzzy modelling techniques detailed a more robust and continuous output. In particular the Nested Fuzzy AHP was concluded to be more pertinent, as it incorporated complex modelling techniques, as well as the initial AHP framework. Through this comparison and assessment of model behaviour, an evaluation of each methods predictive capacity and relevance for decision-making purposes in agricultural applications is gained.

Designing resilient regions by applying Blue-Green Infrastructure concepts

In Australia, weather extremes (droughts and floods) are an accepted component of coupled human-environment systems. Australia is the driest inhabited continent on earth and also has the greatest annual rainfall and run-off variability. Competition for water between the environment, agriculture and domestic uses is intense and the cause of much public debate. It is not unusual for parts of Australia to transition quickly from a state of extreme water scarcity to one of severe flooding. In fact, floods cause more damage in Australia than any other natural disaster. Climate change will exacerbate the situation through increased frequency and intensity of heavy rainfall events and also more intense and longer-lasting droughts. The combination of drought followed by intense rainfall increases the risk of severe flooding, with impacts on civil infrastructure (road and bridge washouts, damage to houses), and impacts on agriculture (soil erosion and destruction of crops and livestock). Structural flood mitigation activities in Australia, such as the construction of levees, was initially driven by private landholders. These measures were often not well planned or integrated at larger scales and therefore have been viewed with some suspicion. More recently, non-structural (land planning, emergency management) approaches have become the key flood mitigation measure. In contrast, The Netherlands takes a structural approach through concepts like Blue-Green Infrastructure (BGI), with the aim of “giving the flood a pathway”. In this context, structural interventions in the landscape provide alternative pathways for flood water, slowing the waters progress such that flood damage is mitigated. Our research focuses on the feasibility of implementing BGI in Australia, considering the costs and benefits in terms of the biophysical environment, infrastructure and socio-economic systems, in order to increase the resilience of rural and regional communities. The research will inform strategic and statutory planning at the regional level.