Feb. 26 Webinar: SARP/RISA Project Supports Colorado Water Managers

  • 15 February 2018
  • Author: Sean Bath
  • Number of views: 1723
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Trout Lake, Colorado (Wikipedia Commons)

Please join us on February 26 for a webinar featuring work funded by the Sectoral Applications Research Program (SARP) and conducted by the Western Water Assessment (WWA, a NOAA-funded RISA) titled, “Exploring the Use of Multiobjective Evolutionary Algorithims (MOEAs) for Long-term Planning by Front Range, Colorado, Water Managers.”

Over the past several years there have been increasing calls for decision support tools in the area of climate, and acknowledgement that changing extremes add to an already challenging decision environment for water managers. Recurring droughts, floods, and concerns over extreme events in the future have created a strong interest among water managers in the Front Range of Colorado in how to plan for these extremes. Traditional methods of identifying alternatives for water supply management may not fully capture the range of existing preferred alternatives, meaning that utilities may miss some of the solutions that appropriately balance among tradeoffs. In this project, a WWA team coproduced and tested a newly developed multi-objective decision tool, balancing conflicting management objectives for water planning under climate extremes and determining how policy alternatives perform under severe climate uncertainty.

About the Webinar
"Exploring the Use of Multiobjective Evolutionary Algorithms (MOEAs) for Long-term Planning by Front Range, Colorado, Water Managers"
Featuring Dr. Rebecca Smith, Civil and Environmental Engineering, University of Colorado Boulder

Original webcast:
Monday, February 26, 2018
1:00 PM MST / 3:00 PM EST
Recording Link: http://cirescolorado.adobeconnect.com/pymd35229ddn/

SARP logoWWA logoCIRES and CU logo

Abstract
Many promising tools and methods developed in water resources systems analysis research have seen little uptake outside of academia. This may be due to a lack of effective communication about the research to water managers, or it may be because the tools are not ultimately useful or usable in practice. Current predominant research frameworks do not provide insight into these issues or facilitate the incorporation of industry needs into research agendas.

We developed a structured process to address this disconnect called the Participatory Framework for Assessment and Improvement of Tools (ParFAIT). We applied ParFAIT specifically to Multiobjective Evolutionary Algorithm (MOEA)-assisted optimization. MOEAs are bottom-up decision support tools that researchers have proven can increase learning and improve outcomes in academic water resources planning studies, but which have seen very limited uptake in real-world water management.

This presentation describes our application of ParFAIT during which we worked with Front Range, Colorado, water utilities to co-produce an MOEA testbed, evaluated the tool’s potential to enhance utilities’ long term planning processes, and applied statistical methods to advance the usefulness of MOEA results.


About the Speaker
Dr. Rebecca Smith

Rebecca Smith recently completed her PhD at the University of Colorado Boulder. The focus of her research was bridging the gap between academic applications of Multiobjective Evolutionary Algorithms (MOEAs) and the decision making needs of water utilities in their planning processes. Over the course of her studies, Rebecca collaborated with researchers from the CU’s Civil Engineering department and Environmental Studies Program, the Western Water Assessment (WWA), and the Center for Advanced Decision Support for Water and Environmental Systems (CADSWES), and she also worked with water managers from a variety of utilities in Colorado. Rebecca now works as a civil/hydrologic engineer with the Lower Colorado River Region of the Bureau of Reclamation in their Boulder, CO, offices. Her roles include coordinating research activities and contributing to technical analyses in support of Colorado River Basin planning studies.

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