4.7 Article

Participatory Framework for Assessment and Improvement of Tools (ParFAIT): Increasing the impact and relevance of water management decision support research

Journal

ENVIRONMENTAL MODELLING & SOFTWARE
Volume 95, Issue -, Pages 432-446

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.envsoft.2017.05.004

Keywords

Participatory modeling; Water management; Optimization; Water resources systems analysis; Multiobjective Evolutionary Algorithms; Transdisciplinary

Funding

  1. National Oceanographic and Atmospheric Administration (NOAA) through their Sectoral Applications Research Program (SARP) [NA14OAR4310251]
  2. NOAA

Ask authors/readers for more resources

This paper proposes the Participatory Framework for Assessment and Improvement of Tools (ParFAIT) as a way to address low uptake of Water Resources Systems Optimization (WRSO) tools. ParFAIT is a transdisciplinary process conducted in five stages, two of which are participatory modeling (PM) exercises. Herein we describe the framework, introduce our candidate tool- Multiobjective Evolutionary Algorithm (MOEA)-assisted optimization, and present the results of our first PM workshop. MOEA-assisted optimization has been put forth as a planning and decision making aid for utilities facing a large number of decisions and highly uncertain futures. The PM workshop, designed to solicit input on a tool testbed, was held in February 2015 with representatives from six Front Range, Colorado, water utilities. Our results include an expanded characterization of the decision making landscape, feedback on water utility decisions and performance goals commonly employed in WRSO studies, and new questions that warrant future investigation by researchers. (C) 2017 Elsevier Ltd. All rights reserved.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available