Statistical modeling of rates and trends in Holocene relative sea level
Statistical modeling of rates and trends in Holocene relative sea level
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DOI：
10.1016/j.quascirev.2018.10.032
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发表时间：
2019-01-15
影响因子：
4
通讯作者：
Kopp, Robert E.
中科院分区：
文献类型：
--
作者：
Ashe, Erica L.;Cahill, Niamh;Kopp, Robert E.
描述相对海平面(RSL)的时空变异性并估计地方、区域和全球RSL趋势需要对RSL数据进行统计分析。在过去十年中，考虑到数据在空间和时间上的稀疏性以及地理年代学和海拔高度的不确定性所需的正式统计处理方法有了很大进步。时间序列模型采用了更灵活、更了解实际情况的规范，对不确定性进行了更严格的量化。时空模型已经从简单的区域平均发展到更丰富地表示RSL跨空间和时间的关联结构的框架。更复杂的统计方法能够严格量化空间和时间的变异性，将地理上不同的数据组合在一起，并将RSL场分离为与不同驱动过程相关的各种分量。我们回顾了文献中使用的统计建模和分析选择的范围，重新制定了它们，以便在共同的分层统计框架中进行比较。该分层框架将每个模型分成不同的级别，明确地将测量和推理的不确定性与过程的可变性分开。将模型放在分层框架中使我们能够突出建模和分析选择之间的相似性和差异性。我们通过将文献中当前使用的一些建模和分析选择的应用结果与分层框架内的常见数据集进行比较来说明它们的含义。鉴于RSL表现出的复杂的空间和时间变异性模式，我们推荐使用非参数方法来建模时态和时空RSL。(C)2018爱思唯尔有限公司。保留所有权利。
Characterizing the spatio-temporal variability of relative sea level (RSL) and estimating local, regional, and global RSL trends requires statistical analysis of RSL data. Formal statistical treatments, needed to account for the spatially and temporally sparse distribution of data and for geochronological and elevational uncertainties, have advanced considerably over the last decade. Time-series models have adopted more flexible and physically-informed specifications with more rigorous quantification of uncertainties. Spatio-temporal models have evolved from simple regional averaging to frameworks that more richly represent the correlation structure of RSL across space and time. More complex statistical approaches enable rigorous quantification of spatial and temporal variability, the combination of geographically disparate data, and the separation of the RSL field into various components associated with different driving processes. We review the range of statistical modeling and analysis choices used in the literature, reformulating them for ease of comparison in a common hierarchical statistical framework. The hierarchical framework separates each model into different levels, clearly partitioning measurement and inferential uncertainty from process variability. Placing models in a hierarchical framework enables us to highlight both the similarities and differences among modeling and analysis choices. We illustrate the implications of some modeling and analysis choices currently used in the literature by comparing the results of their application to common datasets within a hierarchical framework. In light of the complex patterns of spatial and temporal variability exhibited by RSL, we recommend non-parametric approaches for modeling temporal and spatio-temporal RSL. (C) 2018 Elsevier Ltd. All rights reserved.