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As with many basins across Reclamation and around the world, the Colorado River Basin is grappling with irreducible uncertainty in its long-term planning and management activities. In past studies, such as the 2012 Basin Study, the Upper and Lower Colorado Regions of Reclamation have used multiple water demand and policy scenarios along with multiple projections of future water supply derived from observed hydrology, paleohydrology, and climate change projections. Results of the analyses show that each combination of these factors leads to a different and wide range of possible hydrologic and system performance futures. Compounding these different ranges is the fact that it is not possible to confidently assign probabilities to demand and hydrologic information. Given this challenge, Reclamation is advancing its application of robust planning techniques that contributed to the 2012 Basin Study.
Recently-developed RiverWare and RiverSMART capabilities are enabling Reclamation to explore the use of cutting-edge planning methods in studies leading up to the renegotiation of the 2007 Colorado River Interim Guidelines for Lower Basin Shortages and Coordinated Operations for Lakes Powell and Mead. To efficiently search for operating policies that quantify system performance tradeoffs (e.g. maintaining Lake Mead storage vs. minimizing water delivery reductions), Reclamation has used the Borg-RiverWare wrapper to pair the Colorado River Simulation System model with a Multiobjective Evolutionary Algorithm (MOEA). After the MOEA-assisted optimization generates a set of policies that perform well in observed hydrology, they are re-simulated in a wider range of potential hydrologic futures using a RiverSMART Sequence DMI. This new DMI functionality allows users to define another dimension in a RiverSMART study that sequences through a set of policies with existing trace-based DMIs for hydrology and water demand. Once the set of MOEA policies has been evaluated across many potential supply and demand futures, performance can be characterized using robustness criteria, and the policies' abilities to cope with difficult futures can be objectively compared. This optimization and robustness process is part of the planning approach called Many Objective Robust Decision Making, which provides detailed information about how well the system can perform and eliminates the need to try to determine the probability of any specific future scenario. |
