Speaker:  Nathan Bonham,  PhD,  CU Boulder, hydrology, water resources, and environmental fluid mechanics

Title: Empowering participatory decision making under deep uncertainty: Novel algoriths and interactive tools applied to Colorado River post-2026 negotiations

Abstract
Decision Making Under Deep Uncertainty (DMDU) is an emerging field of model-based decision support methods. Deep uncertainty exists when decision-makers disagree on how to prioritize conflicting performance goals and when there is no consensus on which assumptions to make about uncertain future conditions, such as climate and human population. DMDU tests policy alternatives in many plausible futures, prioritizes policies that perform well in many futures (robust policies), and discovers conditions causing poor performance (vulnerability analysis). There is growing demand for participation by stakeholders in decision-making. In DMDU-based decision support, participation includes the modelling process since methodological decisions can have unexpected (and undesirable) impacts on policy recommendations.

This presentation addresses four challenges to participatory DMDU: large computing requirements, choosing robustness metrics considering tradeoffs, choosing policies considering conflicting goals and choosing methods for vulnerability analysis that are interpretable for decision-making. These barriers are addressed with a subsampling framework that can reduce future computing requirements, an interactive tool to explore robustness tradeoffs, a negotiation framework to identify compromise policies and a novel taxonomy of vulnerability analysis methods to improve interpretability.

The research contributions are demonstrated using a case study of reservoir operation policy in the Colorado River Basin. Since 2000, extended periods of low inflow have depleted storage in Lakes Mead and Powell, threatening hydropower infrastructure and water deliveries. Current policies expire in 2026, thereafter a new policy takes effect. This research evaluates a set of Lake Mead policies in 500 futures of streamflow and demand conditions. The performance and robustness of these policies are discussed throughout the presentation to demonstrate the novel algorithms and decision support tools.​​​​​​​