Exploring climate change uncertainties to support adaptive management of changing flood-risk

• Climate change uncertainties pose challenges for flood risk managers.
• Relying on single number ‘best estimates’ can have maladaptive outcomes.
• Modelling of a range of scenarios is constrained by cost and complexity.
• A simplified approach is presented to evaluate climate change uncertainties.
• Considering various futures can encourage different flood management strategies.

Increasing intensity and frequency of extreme precipitation events projected as a consequence of global warming pose significant challenges for decision-making. Climate change creates a dynamic risk, but flood risk management decision-making based on single ‘best estimate’ scenarios is entrenched within decision-making frameworks and professional operating practices. This conceals uncertainties and focuses attention on enhancements to existing ‘protection’ structures, giving a false sense of security to those living within presumed ‘safe’ areas. A more nuanced, risk-based approach to flood frequency changes is needed to reflect climate change uncertainties, but this is constrained by the high cost and complexity of modelling. We present a quick and relatively low-cost methodology to explore the implications of alternative climate change scenarios for flood frequency, and apply it for illustrative purposes, to the Hutt River located in New Zealand's lower North Island. Annual exceedance probabilities increase under all scenarios but with considerable differences between alternative emissions scenarios and climate models. We evaluated the salience of this information for planning responses with flood management and planning practitioners. We found that ‘mind-sets’ changed to consider a greater range of response options according to their lock-in potential in existing and Greenfield urban settlements. Tools to rapidly explore alternative futures can therefore support evaluation of a wider range of response options at the exploratory stages of decision-making, which helps avoid planning responses that are predicated on historical experience and a single ‘best estimate’ scenario. This encourages responses that better reflect the changing nature of the risk.