Greedy copula segmentation of multivariate non-stationary time series for climate change adaptation
Greedy copula segmentation of multivariate non-stationary time series for climate change adaptation
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DOI：
10.1016/j.pdisas.2022.100221
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发表时间：
2022-03
影响因子：
6.3
通讯作者：
Taemin Heo;L. Manuel
中科院分区：
文献类型：
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作者：
Taemin Heo;L. Manuel
在处理自然灾害、气候变化和减灾工作中经常遇到非平稳气候数据。以干旱为例，经常会遇到这样的非平稳数据集（时间序列）。这项工作的目标是制定一种合理的数据驱动方法，可以考虑多个随机变量的非平稳和时间序列，这些随机变量可以具有广义的潜在概率分布和依赖结构。所提出的方法寻求将数据划分为不重叠的部分，每个部分都被视为具有某些潜在概率和依赖结构的平稳部分，而长时间序列产生多个相互独立的这样的部分。贪婪Copula分割（GCS）算法采用数据驱动的时间序列分割后的最优拟合概率分布和Copula函数。采用基准问题和单点实际干旱实例验证了所提出方法的有效性。拟议的GCS方法在气候变化适应（CCA）和灾害风险减少（DRR）中具有潜在的用途，适用于涉及非平稳时间序列数据的任何与气候相关的危害。
Non-stationary climate data are often encountered in dealing with natural hazards, climate change and disaster reduction. With drought, for instance, it is common to encounter such non-stationary data sets (time series). The objectives of this work are to formulate a rational data-driven approach that can consider non-stationary and time series on multiple random variables that can have generalized underlying probability distributions and dependence structures. The methodology proposed seeks to divide up the data into non-overlapping segments, each of which is treated as stationary with some underlying probability and dependence structure, while the long time series yields multiple such segments that are mutually independent. The Greedy Copula Segmentation (GCS) algorithm developed employs best-fit probability distributions and copula functions after data-driven time series segmentation. Validation of the proposed methodology is demonstrated using a benchmark problem as well as a single-site realistic drought example. The proposed GCS approach has potential use in climate change adaptation (CCA) and disaster risk reduction (DRR) for any climate-related hazards involving non-stationary time series data.