Improving Decision Support During High Impact Weather Through Data Analysis and Visual Communication

Abstract

Rain-on-snow is linked to many of the largest floods in the Western United States. Forecasting runoff in snow-covered areas prone to flooding is complicated due to the difficult nature of predicting and observing the rain-snow transition elevation and the variation in runoff efficiency and magnitude when snow is present. Looking at forecasts, reservoir operators must constantly weigh decisions to store water for economic and ecological benefits (managing water as a resource) or to release water to mitigate downstream flooding potential (managing water as a hazard). Rain-on-snow events will continue to increase in frequency and magnitude as the climate warms. This change will multiply uncertainties and risks in operational decision-making related to extreme weather. To meet these mounting challenges, this dissertation explores the feasibility for an empirically-based Snowpack Runoff Decision Support system, which considers the likelihood of snowmelt runoff through risk quantification. The research is coupled with a literature review to identify and apply the best practices for visual communication of weather hazards. The dissertation aims to develop a conceptual snowpack runoff decision support framework tested at a regional scale in collaboration with relevant decision-makers over the period 2006-2023. To facilitate broad and efficient communication, this approach also incorporates the guiding principles from graphic design and social science for visual communication of the snowpack's potential to modulate rain-on-snow events.