Extensive interactions with water and watershed managers have revealed that decision makers have varying perspectives about hydroclimatic variability and opportunities for using forecasts to inform resource management decisions. In many cases, uncertainty about the accuracy of climate forecasts presents a formidable barrier to more effective use of forecast products. In addition, decision makers may be sensitive to only some aspects of forecast performance relative to actual conditions. Further, improper interpretation can impede effective use of even the highest quality forecasts. Many decision makers also have difficulty placing forecast information in appropriate historical and regional contexts. These issues are addressed in the context of seasonal forecasts, including ensemble streamflow predictions generated using the National Weather Service (NWS) River Forecast Center procedures and seasonal climate predictions issued by the NWS Climate Prediction Center.

Quantitative evaluations of the seasonal forecasts demonstrate that simple approaches, using “hit or miss” criteria (e.g., probability of detection, false alarm rate) or traditional summary statistics (e.g., root mean squared error, correlation) neglect important aspects of forecast performance and can even be misleading, potentially affecting resource management decisions. On the other hand, distributions-oriented evaluation criteria are more informative and allow decision makers to target those aspects of forecast performance that are important for their situation. For example, using the criteria of “discrimination”, predictions of seasonal streamflow volumes for Colorado River tributaries are shown to convey useable information not revealed by other criteria, even with lead-times of several months. From a decision maker’s perspective, it is important that forecast evaluations be frequently updated and target the regions, seasons, lead times, and criteria important to specific decision making situations. From an operational perspective, more information needs to be archived than has been the traditional practice, especially for probabilistic predictions.

From the perspective of resource managers, risks of using new forecast products can be reduced as individuals accurately interpret the products and properly gauge prediction uncertainty. Effective communication of prediction uncertainty, in ways understandable by diverse audiences, requires further attention to product formats. Several alternative formats for deterministic and probabilistic forecasts are presented, as are formats for expressing historical regional variability and potential variability unrelated to specific forecast methodologies. Feedback from resource managers has been solicited through workshops, interviews, and online surveys. Tutorial materials intended to improve forecast interpretation have been well received.