Establishing a Framework for Robust Planning in the Å·ÃÀ¿Ú±¬ÊÓƵ River Basin

Our current research builds on previous analyses using Many Objective Robust Decision Making in the Å·ÃÀ¿Ú±¬ÊÓƵ River Basin (CRB). First, Alexander 2018 coupled the Å·ÃÀ¿Ú±¬ÊÓƵ River Simulation System with the Borg Multi-Objective Evolutionary Algorithm (MOEA) to identify an archive of non-dominated Lake Mead operation policies. Eight objectives quantify reliability, resiliency, and vulnerability of water storage and supply for the Lower Basin and Upper Basin states. The Borg MOEA intelligently alters the values

Funding Agency

Bureau of Reclamation Lower Å·ÃÀ¿Ú±¬ÊÓƵ River Basin Region

Research Team

  • Nathan Bohnam, Ph.D. student
  • Joesph Kasprzyk and Edie Zagona, Principal Investigators

This research applied and extended the concepts of  Many Objective Robust Decision Making (MORDM) to develop a long-term planning framework in the Å·ÃÀ¿Ú±¬ÊÓƵ River Basin (CRB) that identifies and analyzes management strategies robust to uncertainties in future hydrology and demand. This work is motivated by and could assist the upcoming renegotiation of the Å·ÃÀ¿Ú±¬ÊÓƵ River Interim Guidelines for Lower Basin Shortages and Coordinated Operations for Lake Powell and Lake Mead. Reclamation and stakeholders will explore and negotiate new guidelines to be established by 2026. The modeling and analytical framework developed under this project incorporates multi-objective optimization, robustness evaluation, statistical analysies to identify performance vulnerabilities, and includes graphical interactive visualization tools.

A previous project funded by Reclamation identified Lake Mead operation policies via a Many Objective Evolutionary Algorithm (MOEA) coupled with Reclamation’s Å·ÃÀ¿Ú±¬ÊÓƵ River Simulation System (CRSS). This research builds on the previous work by establishing the analytical and visual tools to explore the robustness and vulnerability of Lake Mead policies. In robustness analysis, we stress tested candidate policies in many plausible future scenarios and evaluated them on the ability to perform well across the future scenarios. For vulnerability analysis, we implemented statistical data-mining algorithms in a process called scenario discovery to identify scenarios of future conditions that cause policies of interest to fail performance criteria. Together with the previous work, this research and development established a functional application of the MORDM framework in the CRB for Reclamation planning processes.

This 2-year project that began in August 2019 consised of four phases: 1) literature review of advancements in MORDM, 2) uncertainty characterization of hydrology, demand, and initial reservoir elevations, 3) robustness analysis, 4) scenario discovery. 

This project has been completed.