Climatic Change

, Volume 119, Issue 2, pp 495–509 | Cite as

Understanding and enhancing climate information use in water management



This paper expands our understanding of water manager's climate information (CI) use and of the effectiveness of interactive research efforts in improving use by quantitatively measuring usability both within and outside the interactive research model. Using a mixed method approach (i.e., interviews and surveys), data was collected across five states and hundreds of water managers to understand the production of CI by scientists at two Regional Integrated Sciences and Assessments (RISAs) employing an interactive approach and the use of that information by water managers in the corresponding RISA regions. This study finds that RISAs are effective in three important ways: first, in co-producing usable information and achieving a high rate of information use among RISA clients; second, in overcoming barriers to information use arising from negative perceptions about the usability and reliability of CI; and, finally, in fostering innovation. RISA information use is contingent on sustained scientist-client interaction and is enabled by users' willingness and capacity making RISAs most effective in reaching the largest, most capable users. These users and those who use CI from other sources do so as a strategy to manage risk. This research suggests areas for enhancing RISA CI uptake: structuring RISAs as consortia, cultivating relationships with knowledge brokers and capitalizing on existing knowledge networks, and increasing public education and outreach. Beyond the interactive research models, findings suggest CI uptake may be enhanced by building capabilities for long-term water planning at water systems and bolstering public science citizenship and climate literacy.

Supplementary material

10584_2013_703_MOESM1_ESM.docx (26 kb)
Online Resource 1Results of logistic regression model of RISA Use regressed on water source, natural log population served, collaboration, information sources, distance, endangered species/instream (IS) flows, and drought. (DOCX 25 kb)
10584_2013_703_MOESM2_ESM.docx (24 kb)
Online Resource 2Final Regression Model with Log Odds, Standard Errors, and Confidence Intervals (DOCX 24 kb)


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.School of Natural Resources and EnvironmentUniversity of MichiganAnn ArborUSA

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