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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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This content will become publicly available on January 1, 2026
A proposed method for analyzing historical adaptation pathways of coupled natural-human systems
Historical adaptation pathways (HAP) analyses identify sequences of multi-causal factors that shape climate change adaptation actions. Such analyses can be valuable for understanding why systems respond differently to climate risks, assessing important adaptation drivers and constraints, and identifying potential path dependencies. This paper synthesizes existing (and still emerging) HAP methods in order to present a more standardized and generalized approach to studying historic adaptations. The proposed method combines inductive and deductive approaches and draws on established practices from grounded theory to increase validity, including process tracing, memoing, construct definition, and member checking. This approach is designed to provide historical and contextual information that can be incorporated into a decision model or be shared with stakeholders and community members. In addition, future comparative studies based on this replicable approach could allow for theorization as to the casual mechanisms that engender successful adaptation. The approach is illustrated using a coastal adaptation case study in South Carolina, USA, with one of the main insights being that the island would not exist in its current form without the actions taken by concerned citizens, whose efforts ultimately helped combat the erosion caused (in part) by local jetties. Several areas for methodological improvement and theoretical development are also noted, as the aim of this work is both to enable cross-study comparisons of future HAP research – which can inform adaptation practice – and to provide a method that can be improved upon in future iterations.
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- Award ID(s):
- 2034239
- PAR ID:
- 10597676
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Environmental Science & Policy
- Volume:
- 163
- ISSN:
- 1462-9011
- Page Range / eLocation ID:
- 103969
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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