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Dialogue with stakeholders has been recognized as an effective educational strategy for addressing complex topics such as climate change. We report here on the Carbon, Energy, and Climate fishbowl discussion series developed by Michigan State University Extension to assist the state's agricultural community in understanding and adapting to the changing climate. Facilitated dialogue reduced barriers to communication and promoted cooperative learning for target audiences and the project team, generating useful information on the current status of climate change adaptation within Michigan's agriculture sector and revealing needs to be addressed by future Extension programming. Using a dialogue-based approach such as the one we describe can highlight challenges and opportunities Extension faces in addressing various complex issues with diverse audiences.
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Greedy Copula Segmentation of Multivariate Non-Stationary Time Series for Climate Change Adaptation
An assumption of stationarity in climate-related processes is often made in the risk assessment of civil infrastructure systems. Such an assumption is difficult to justify in a changing climate. In this study, to optimally adapt to a changing climate, given time series data, we propose a computationally efficient algorithm called Greedy Copula Segmentation, GCS, that could potentially be used in a climate change adaptation (CCA) strategy. The GCS algorithm partitions a multivariate time series into disjoint segments such that each of the segments is described by a stationary copula process, but independence is assumed across segments. An optimal strategy for climate change adaptation, which we will refer to as GCS-CCA, considers the last or most recent segment as containing the most informative data for near future climate pattern prediction. By only using such informative data to build a probabilistic model for the near future, our method effectively accounts for climate change. We provide an algorithmic formulation for greedy segmentation and validate the performance of our GCS-based strategy by applying it to an illustrative benchmark problem and a realistic drought example.
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- Award ID(s):
- 1663044
- PAR ID:
- 10311140
- Date Published:
- Journal Name:
- Proceedings of the 2nd International Symposium on Disaster Resilience & Sustainable Development (virtual)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
