2018 RiverWare User Group Meeting Colorado River
NCAR/UCAR Center Green Campus Auditorium
  Thursday, February 1, 8am–5pm
  Friday, February 2, 8am–Noon
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Using Multi-objective Optimization to Explore Operating Policies in the Colorado River Basin

  Elliot Alexander—Bureau of Reclamation; Joseph Kasprzyk and Edith Zagona—University of Colorado;
James Prairie, Carly Jerla and Alan Butler—Bureau of Reclamation

The long term reliability of water deliveries in the Colorado River Basin has degraded due to the imbalance of growing demand and dwindling supply. The Colorado River meanders 1,450 miles across a watershed that covers seven US states and Mexico and is an important cultural, economic, and natural resource for 35 - 40 million people. Its complex operating policy is based on the “Law of the River,” which has evolved since the Colorado River Compact in 1922. Recent (2007) refinements to address shortage reductions and coordinated operations of Lakes Powell and Mead were negotiated with stakeholders in which hundreds of scenarios were explored to identify operating guidelines that could ultimately be agreed on. This study explores a different approach to search for a set of diverse, strong performing operations to support Reclamation’s policy analysis. The Colorado River Simulation System (CRSS), a long-term water management simulation model implemented in RiverWare, is combined with the Borg multi-objective evolutionary algorithm (MOEA) to solve an eight objective problem formulation. Basin-wide performance metrics are closely tied to system performance through incorporating critical reservoir pool elevations, duration, frequency and quantity of shortage reductions in the objective set. For example, an objective to minimize the frequency that Lake Powell falls below the minimum power pool elevation of 3,490 feet for Glen Canyon Dam protects a vital economic and renewable energy source for the southwestern US. The decision variables correspond to operating tiers in Lakes Powell and Mead that drive the implementation of various shortage, surplus and release policies, thus affecting system performance. The result will be a set of non-dominated solutions that can be compared with respect to their trade-offs based on the various objectives. These could support Reclamation’s policy analysis by eliminating dominated solutions and revealing system insights that could remain hidden under conventional analysis.

Click HERE for a PDF version of the presentation slides.

☼ CADSWES.colorado.edu ☼ Last edited February 5, 2018

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