Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2021WR030007 |
A Hidden Climate Indices Modeling Framework for Multi-Variable Space-Time Data | |
B. Renard; M. Thyer; D. McInerney; D. Kavetski; M. Leonard; S. Westra | |
2021-12-13 | |
发表期刊 | Water Resources Research |
出版年 | 2021 |
英文摘要 | Risk assessment for climate-sensitive systems often relies on the analysis of several variables measured at many sites. In probabilistic terms, the task is to model the joint distribution of several spatially-distributed variables, and how it varies in time. This paper describes a Bayesian hierarchical framework for this purpose. Each variable follows a distribution with parameters varying in both space and time. Temporal variability is modelled by means of hidden climate indices (HCIs) that are extracted from observed variables. This is to be contrasted with the usual approach using predefined standard climate indices (SCIs) for this purpose. In the second level of the model, the HCIs and their effects are assumed to follow temporal and spatial Gaussian processes, respectively. Both inter-variable and inter-site dependencies are induced by the strong effect of common HCIs. The flexibility of the framework is illustrated with a case study in Southeast Australia aimed at modeling ‘hot and dry’ summer conditions. It involves three physical variables (streamflow, precipitation and temperature) measured on three distinct station networks, with varying data availability and representing hundreds of sites in total. The HCI model delivers reliable and sharp time-varying distributions for individual variables and sites. In addition, it adequately reproduces inter-variable and inter-site dependencies, whereas a corresponding SCI model (where hidden climate indices are replaced with standard ones) strongly underestimates them. It is finally suggested that HCI models may be used as downscaling tools to estimate the joint distribution of several variables at many stations from climate models or reanalyses. |
领域 | 资源环境 |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/344167 |
专题 | 资源环境科学 |
推荐引用方式 GB/T 7714 | B. Renard,M. Thyer,D. McInerney,et al. A Hidden Climate Indices Modeling Framework for Multi-Variable Space-Time Data[J]. Water Resources Research,2021. |
APA | B. Renard,M. Thyer,D. McInerney,D. Kavetski,M. Leonard,&S. Westra.(2021).A Hidden Climate Indices Modeling Framework for Multi-Variable Space-Time Data.Water Resources Research. |
MLA | B. Renard,et al."A Hidden Climate Indices Modeling Framework for Multi-Variable Space-Time Data".Water Resources Research (2021). |
条目包含的文件 | 条目无相关文件。 |
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