Modelling economic decision-making under uncertainty typically involves approaches that assume perfect foresight, or that future information is knowable and awaiting discovery (white-swan problems). By contrast, grey-swan problems where some information may be known probabilistically, and other information remains uncertain (no probability measures), is less commonly modelled in the literature. Climate change is a classic grey-swan problem that will shape complex economic, social and environmental decisions for generations to come. Within the global debate on water scarcity and inequality strategies for (re)allocating water resources in response to climate change impacts are of particular concern. Analysts need to be able to inform decision makers on how to best adapt given uncertain futures. This paper contrasts the outputs from (re)allocating water resources via expected value and stochastic state contingent modelling approaches;where both are specified to represent white- and grey-swan problems. The models represent how water resources (re)allocate under current climate settings and two alternative future climate change scenarios: i) a reduction in total water availability/use, and ii) the increased occurrence of bad (e.g., drought) events. Expected value models mask innovation and adaptation reactions by decision-makers in response to external stimuli (e.g., increased droughts), and under-represent water (re)allocation outcomes. Conversely, state contingent models more clearly represent and report decision-maker reactions that can be more readily interpreted and linked to stimuli including policy interventions.