The Making of a Metric: Co-Producing Decision-Relevant Climate Science for Water Management
This paper examines both the process of, and outcomes from, a case of “co-production” (Project Hyperion), wherein scientists and water managers jointly developed decision-relevant climatic metrics that are useful for water management and for evaluation of climate model fidelity. The paper opens up the black box of co-production and identifies specific strategies that enabled (and did not enable) the creation of decision-relevant metrics.
This is the first study to document in detail the climatic metrics for water management, and the co-production processes needed to arrive at such metrics. The outcomes (i.e. the co-produced decision-relevant metrics), can be used by scientists to evaluate model fidelity and identify key processes that lead to biases in decision-relevant model outputs, while also providing insight into the usability of existing datasets for specific users. The learnings on engagement approaches provide co-production scholars with insights on how to design and implement productive scientist-decision-maker interactions.
Developing use-inspired science requires the identification of climatic parameters that are both actionable for practitioners as well as tractable for modelers. In many sectors, these decision-relevant climatic metrics and the approaches that enable their identification remain largely unknown. “Co-production” of science with scientists and decision-makers is one potential way to identify these metrics, but there is little research describing specific and successful co-production approaches. This paper examines Project Hyperion, wherein scientists and water managers jointly developed decision-relevant climatic metrics. We identify successful co-production strategies by analyzing the project’s numerous back-and-forth engagements and tracing the evolution of the science during these engagements. We found that effective mediation between scientists and managers needed dedicated “boundary spanners” with significant modeling expertise. We found that translating practitioners' information needs into tractable climatic metrics required direct and indirect methods of eliciting knowledge. We identified four indirect methods that were particularly salient for extracting tacitly-held knowledge and enabling shared learning: developing a hierarchical framework linking management issues with metrics; starting discussions from the planning challenges; collaboratively exploring the planning relevance of new scientific capabilities, and using analogies of other ‘good’ metrics. Overall, the approaches and metrics we developed can help climate science to expand in new and more use-inspired directions.