Many-Objective Robust Decision Making for Water Supply Portfolio Planning Under Deep Uncertainty

Abstract: Portfolios of market-based instruments have been shown to improve the reliability of water supplies, using simulations that utilize a single best estimate of distributions of data to evaluate performance. However, the estimates of problem information and likelihoods could be incorrect, especially when planning for climate change, which can modify streamflow availability, or projecting the trajectories of future water demands. These conditions are termed deep uncertainty, in which decision makers cannot fully conceptualize or agree upon the full range of risks to their system. This presentation will advance a new interactive framework that combines robust decision making (RDM) with many-objective optimization using evolutionary algorithms (MOEA) to confront deep uncertainty for water planning. The framework is demonstrated using a case study that examines a single city's water supply in the Lower Rio Grande Valley (LRGV) in Texas, USA. We use a MOEA to develop a tradeoff set of water supply portfolios for the LRGV, and develop a suite of values for key uncertainties using RDM that represent an ensemble of “states of the world”. Each solution is tested under the ensemble o