Oz Sahin

Paradigm shift to enhanced water supply planning through augmented grids, scarcity pricing and adaptive factory water

This paper details a system dynamics model developed to simulate proposed changes to water governance through the integration of supply, demand and asset management processes. To effectively accomplish this, interconnected feedback loops in tariff structures, demand levels and financing capacity are included in the model design, representing the first comprehensive life-cycle modelling of potable water systems. A number of scenarios were applied to Australias populated South-east Queensland region, demonstrating that introducing temporary drought pricing (i.e. progressive water prices set inverse with availability), in conjunction with supply augmentation through rain-independent sources, is capable of efficiently providing water security in the future. Modelling demonstrated that this alternative tariff structure reduced demand in scarcity periods thereby preserving supply, whilst revenues are maintained to build new water supply infrastructure. In addition to exploring alternative tariffs, the potential benefits of using adaptive pressure-retarded osmosis desalination plants for both potable water and power generation was explored. This operation of these plants for power production, when they would otherwise be idle, shows promise in reducing their net energy and carbon footprints. Stakeholders in industry, government and academia were engaged in model development and validation. The constructed model displays how water resource systems can be reorganised to cope with systemic change and uncertainty. System dynamics model integrates supply, demand and financial dimensions.Diverse supply source portfolios that are grid connected alleviates water scarcity.Desalination reduces need for restrictions compared to a rain-dependent portfolio.Pressure retarded osmosis technology integration into desalination component of water supply networks for renewable energy.Scarcity pricing is an effective strategy for reducing demand while simultaneously generating the additional revenues.

Improving cross-sectoral climate change adaptation for coastal settlements: insights from South East Queensland, Australia

Climate change impacts affecting coastal areas, such as sea-level rise and storm surge events, are expected to have significant social, economic and environmental consequences worldwide. Ongoing population growth and development in highly urbanised coastal areas will exacerbate the predicted impacts on coastal settlements. Improving the adaptation potential of highly vulnerable coastal communities will require greater levels of planning and policy integration across sectors and scales. However, to date, there is little evidence in the literature which demonstrates how climate policy integration is being achieved. This paper contributes to this gap in knowledge by drawing on the example provided by the process of developing cross-sectoral climate change adaptation policies and programmes generated for three coastal settlement types as part of the South East Queensland Climate Adaptation Research Initiative (SEQCARI), a 3-year multi-sectoral study of climate change adaptation options for human settlements in South East Queensland, Australia. In doing so, we first investigate the benefits and challenges to cross-sectoral adaptation to address climate change broadly and in coastal areas. We then describe how cross-sectoral adaptation policies and programmes were generated and appraised involving the sectors of urban planning and management, coastal management, emergency management, human health and physical infrastructure as part of SEQCARI. The paper concludes by discussing key considerations that can inform the development and assessment of cross-sectoral climate change adaptation policies and programmes in highly urbanised coastal areas.

Applications of Bayesian belief networks in water resource management

Bayesian belief networks (BBNs) are probabilistic graphical models that can capture and integrate both quantitative and qualitative data, thus accommodating data-limited conditions. This paper systematically reviews applications of BBNs with respect to spatial factors, water domains, and the consideration of climate change impacts. The methods used for constructing and validating BBN models, and their applications in different forms of decision-making support are examined. Most reviewed publications originate from developed countries (70%), in temperate climate zones (42%), and focus mainly on water quality (42%). In 60% of the reviewed applications model validation was based on the expert or stakeholder evaluation and sensitivity analysis, and whilst in 27% model performance was not discussed. Most reviewed articles applied BBNs in strategic decision-making contexts (52%). Integrated modelling tools for addressing challenges of dynamically complex systems were also reviewed by analysing the strengths and weaknesses of BBNs, and integration of BBNs with other modelling tools. The application of BBNs to water resource management was rarely applied in developing countries and in tropical regions.Only 8% reviewed papers explored potential impacts of climate change on water resources.Only 11% and 6% of reviewed articles applied influence diagrams and Object-Oriented Bayesian Networks respectively.Most reviewed articles applied BBNs in strategic decision-making contexts (52%) for water resource management.Results from BBN models were rarely compared or tested against other modelling approaches to validate their performance.

Bridging the Water Supply–demand Gap in Australia: Coupling Water Demand Efficiency with Rain-independent Desalination Supply

Water supply in Australia mainly relies on precipitation and, therefore, is highly dependent on climate variability and change. Coupled with reduced rainfall reliability, population and economic growth and increasing competition for water resources augment the concern over the existing water resources and put a strain on future water security. In fact, the upward trend of water demand has already been escalating the pressure on water resources. Clearly, the anticipation of decline in water supply requires the identification of more reliable, rainfall-independent supply alternatives. With this in mind, this paper discusses the role and value of desalination in water grids. For this purpose, we present a modelling framework using System Dynamics approach to incorporate a range of factors into a simulation of future water demand and supply in Queensland, Australia; and examine desalination schemes as long-term water security option in the portfolio of supply sources. In particular, the model is used to explore the sensitivity of long term planning of water resources with respect to two specific assumptions, the discount rate and the degree of water security. The proposed approach would help decision makers to develop sustainable water supply and efficient infrastructure strategies, and thus respond to water scarcity in a timely manner.

Decision dilemmas for adaptation to sea level rise: How to, when to?

This paper is part of ongoing research designed to develop a dynamic model for assessing the vulnerability of waterfront properties to sea-level rise (SLR), and to evaluate adaptation options. SLR is one of the best recognized effects of projected climate change within recent literature, and is expected to continue for centuries. Increased storm surge height, due to SLR, may place many coastal properties in danger of erosion and inundation, and millions of people living near the sea may be forced to relocate. If SLR is a fact, decision makers will need to have better tools to understand the extent and timing of coastal hazards. Considering the complexity and dynamic nature of coastal systems interacting and changing over time, this research focuses on modeling temporal and spatial variations of coastal flooding in assessing the vulnerability of these systems to SLR.

Coastal vulnerability to sea-level rise: a spatial–temporal assessment framework

The scientific community is confident that warming of the Earth’s climate is unequivocal. Sea-level rise, which poses potential threats to coastal areas, is one of the most recognised possible impacts of this climate change. The nonlinearities, complexities, and spatial and temporal lags are common characteristics of coastal processes driven by human and natural interaction. With the acknowledgement of the complexity and dynamic nature of coastal systems, this paper introduces a spatial–temporal assessment framework, for addressing both the temporal and spatial variations, when assessing the vulnerability of natural and human systems in coastal areas. The framework is based upon a combination of system dynamics (SD) modelling and geographical information systems by taking into account spatial (x, y, z) and temporal (t) dimensions. The strategy of the adopted approach is to use the loose coupling approach by which a spatial model component is incorporated into a SD model component through a data converter.

Assessment of sea‐level rise adaptation options: Multiple‐criteria decision‐making approach involving stakeholders

Purpose – The Gold Coast is a low‐lying coastal Australian city and many residential areas are subject to 1:100 year flood events. Evidently, there is a need for the city to adapt to sea‐level rise (SLR) by developing more effective policies to reduce its destructive impacts. Thus, the purpose is to identify and evaluate preferred adaptation alternatives to reduce the vulnerability to SLR and storm surges.Design/methodology/approach – In this research, we explore stakeholders’ opinions for adaptation alternatives to adapt to the impacts of SLR. As part of exploring alternatives to improve Gold Coasts resilience to climate change effects we are undertake a multi‐criteria analysis by using the analytical hierarchy process (AHP). The goal, criteria and adaptation alternatives were derived, and based upon, adaptation programmes, existing adaptation works by local governments and an extensive literature review. The final AHP structure was developed after further consultations with three local stakeholders (po...

An integrated risk and vulnerability assessment framework for climate change and malaria transmission in East Africa.

BackgroundMalaria is one of the key research concerns in climate change-health relationships. Numerous risk assessments and modelling studies provide evidence that the transmission range of malaria will expand with rising temperatures, adversely impacting on vulnerable communities in the East African highlands. While there exist multiple lines of evidence for the influence of climate change on malaria transmission, there is insufficient understanding of the complex and interdependent factors that determine the risk and vulnerability of human populations at the community level. Moreover, existing studies have had limited focus on the nature of the impacts on vulnerable communities or how well they are prepared to cope. In order to address these gaps, a systems approach was used to present an integrated risk and vulnerability assessment framework for studies of community level risk and vulnerability to malaria due to climate change.ResultsDrawing upon published literature on existing frameworks, a systems approach was applied to characterize the factors influencing the interactions between climate change and malaria transmission. This involved structural analysis to determine influential, relay, dependent and autonomous variables in order to construct a detailed causal loop conceptual model that illustrates the relationships among key variables. An integrated assessment framework that considers indicators of both biophysical and social vulnerability was proposed based on the conceptual model.ConclusionsA major conclusion was that this integrated assessment framework can be implemented using Bayesian Belief Networks, and applied at a community level using both quantitative and qualitative methods with stakeholder engagement. The approach enables a robust assessment of community level risk and vulnerability to malaria, along with contextually relevant and targeted adaptation strategies for dealing with malaria transmission that incorporate both scientific and community perspectives.

Coastal vulnerability to Sea Level Rise: A spatio-temporal decision making tool

Due to uncertainty in the timing and magnitude of Sea Level Rise (SLR) impacts, it is difficult to determine whether taking a specific action to prepare for SLR is justified. The dilemmas confronting decision makers are: when, where and how to adapt to SLR. To address these issues, this research introduces a recently developed model linking Geographical Information Systems (GIS) with System Dynamics (SD) modelling to present realistic time series scenarios of SLR across coastal communities. The hybrid GIS-SD model provides a multifaceted assessment by going beyond detailing year specific land use impacts through linking these outputs with socio-economic SD modules. As a result, the model provides the potential to address temporal and spatial problems concurrently. The methodology formulated from this assessment process, could potentially be utilised by coastal communities to identify and evaluate effective adaptation alternatives for reducing SLR impacts, and to inform long-term decision making.

Re-engineering traditional urban water management practices with smart metering and informatics

Current practice for the design of an urban water system usually relies on various models that are often founded on a number of assumptions on how bulk water consumption is attributed to customer connections and outdated demand information that does not reflect present consumption trends; meaning infrastructure is often unnecessarily overdesigned. The recent advent of high resolution smart water meters and advanced data analytics allow for a new era of using the continuous ‘big data’ generated by these meter fleets to create an intelligent system for urban water management to overcome this problem. The aim of this research is to provide infrastructure planners with a detailed understanding of how granular data generated by an intelligent water management system (Autoflow©) can be utilised to obtain significant efficiencies throughout different stages of an urban water cycle, from supply, distribution, customer engagement, and even wastewater treatment.