Environmental decision makers increasingly rely on scientific information to inform decisions. Although this integration of science and policy offers the potential to support more informed decisions, scientific results are often not provided in a manner usable to decision makers. When faced with highly uncertain conditions, such as climate change, communicating scientific information in a usable manner becomes even more important. This research presents methods for representing uncertainty for decision support, and seeks to evaluate two questions. First, do implicit representations of uncertainty influence the process of evaluating courses of action and potential outcomes related to water planning? And second, do implicit representations of uncertainty influence how decision makers perceive the relationship of human actions on climate change? Using the existing DCDC WaterSim model, representations of uncertainty that visualize the relationship between projected outcomes of policy decisions and uncertainty (represented as the impact of climate change on water supply) are developed. These visualizations will be evaluated through a human subject test where decision makers interact with uncertainty representations. Participants will be drawn from the water planning community, as well as DCDC and CAPLTER community partners. It is hypothesized that this research will show that individuals use uncertainty representations to identify policy choices that provide the least impact on ground water and will feel more confident than those using representations without uncertainty represented. Moreover, participants who use uncertainty will select policy choices that result in lower groundwater loss, reflecting an increase in their understanding of how policy impacts sustainable water usage.