Globally, sea‐level rise is expected to impact on many coastal regions and settlements. While mitigation of global greenhouse gas emissions remains an important task, adaptation is now seen as a critical component of the policy equation. Local government is a key player in adaptation planning and managing risk through their mandated role in land use planning and development control. Yet, managing the predicted impacts of climate change is proving to be a complex and difficult task for planners and policy makers. This paper reports on a case of local government deliberation on possible planning responses to address future sea‐level rise impacts in New South Wales, Australia. Using structured, expert opinion of planners and other technical experts engaged in a collaborative network in the Sydney region, we explore the feasibility of implementing planning and policy measures at the local and regional scales to respond to inundation risk as a result of sea‐level rise and storm surge events. Our research shows how local governments employ specific scale‐oriented strategies to engage private and public actors at different scales to manage legal, financial, and technical risks in coastal adaptation.

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There is an expectation that many cities in Australia may experience higher temperatures and reduced rainfall in the future. Hence, to support water policy analysis there is a need for predicting water demand and how it changes in response to climate change. This paper describes a methodology to develop a model that explains month-to-month variability in water demand due to climate and weather, on the basis of using statistical methods. To achieve this, we use time series data of historical water use volumes and climate data from relevant weather stations. Applying this model together with hypothetical climate change scenarios, but without community adaptation scenarios, the results indicate that water demand will change moderately and, as may be expected, it is most sensitive to evaporation and temperature, followed by rainfall. With such scenarios, demand is shown to increase by as much as 10-20%, but more realistic climate sequences are required to provide reliable estimates. Given availability of data, this statistical method can be used to support policy analyses in other cities.