Seasonal Climate Signals for Drought and Flood Management Practitioners
Anticipation of such seasonal anomalies holds promise for improving flood, drought, and hurricane planning and management, and some localities have effectively used seasonal climate forecasts to improve emergency management of disasters. However, prior research suggests that even when signals are clear and extreme events appear strongly correlated with seasonal climate signals, the application of climate information in emergency management poses difficulties. In many settings, technical and institutional capacity to take advantage of information may be limited, and geographic distances complicate the operation of networks of actors engaged in emergency management and disaster planning.
With NOAA and NSF support, we are examining the obstacles to and opportunities for the use of climate forecasts in flood and drought planning and management in the U.S. We seek to improve understanding of the 1) characteristics that make scientific and technical information on the seasonal risk of flood and drought events more useable by emergency managers and other decision-makers; 2) strengths and weaknesses of policy and practice networks for utilizing seasonal forecast information; and 3) influence of group decision processes on the formation and consideration of objectives and tradeoffs in decision making about employing seasonal climate forecasts in weather-related emergency management. Our approach relies on a mix of case studies, in-person decision experiments and a national-level survey of stakeholders engaged in flood and drought planning and emergency management.
In this presentation, we focus on the decision experiment part of our work, in which we employ both structured and unstructured group decision making exercises with stakeholders in emergency management to simulate network operations. We utilize a two by three factorial design (yielding six possible treatments) related to the decision environment and presentation of forecast uncertainty. To explore the utility of the decision environment component, the first two factors divide our simulation participants into a group that receives training on structured decision making and a group that does not. The uncertainty element of our design divides our participants into a group that receives forecast information expressed as a narrative analog (e.g., “the forecast is for a winter such as what we had in 2004”), a group that receives numerical information (e.g., 95 percent confidence intervals), and a group that receives visual information (e.g., slider bar or box plots). To examine the influence of each of these treatments, we have administered pre- and post-group session self-rating questionnaires to compare participants' comfort with decision making regarding forecast use.