Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019WR024951 |
Revealing Hidden Climate Indices from the Occurrence of Hydrologic Extremes | |
Renard, Benjamin1,2; Thyer, Mark2 | |
2019-09-06 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2019 |
卷号 | 55期号:9页码:7662-7681 |
文章类型 | Article |
语种 | 英语 |
国家 | France; Australia |
英文摘要 | Describing the space-time variability of hydrologic extremes in relation to climate is important for scientific and operational purposes. Many studies demonstrated the role of large-scale modes of climate variability such as the El Nino-Southern Oscillation (ENSO) or the North Atlantic Oscillation (NAO), among many others. Climate indices have hence frequently been used as predictors in probabilistic models describing hydrologic extremes. However, standard climate indices such as ENSO/NAO are poor predictors in some regions. Consequently, this paper describes an innovative method to avoid relying on standard climate indices, based on the following idea: the relevant climate indices are effectively unknown (they are hidden), and they should therefore be estimated directly from hydrologic data. In statistical terms, this corresponds to a Bayesian hierarchical model describing extreme occurrences, with hidden climate indices treated as latent variables. This approach is illustrated using three case studies. A synthetic case study first shows that identifying hidden climate indices from occurrence data alone is feasible. A second case study using flood occurrences at 42 east Australian sites confirms that the model correctly identifies their ENSO-related climate driver. The third case study is based on 207 sites in France, where standard climate indices poorly predict flood occurrence. The hidden climate indices model yields a reliable description of flood occurrences, in particular their clustering in space and their large interannual variability. Moreover, some hidden climate indices are linked with specific patterns in atmospheric variables, making them interpretable in terms of climate variability and opening the way for predictive applications. |
英文关键词 | hydrologic extremesclimate indicesBayesian hierarchical modellingspace-time variabilityfloods |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000487412000001 |
WOS关键词 | LONG-TERM PERSISTENCE ; HIERARCHICAL BAYESIAN MODEL ; NINO-SOUTHERN-OSCILLATION ; RAINFALL TIME-SERIES ; FREQUENCY-ANALYSIS ; MARKOV MODEL ; NORTH-ATLANTIC ; RIVER FLOWS ; FLOOD RISK ; VARIABILITY |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
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/186956 |
专题 | 资源环境科学 |
作者单位 | 1.Irstea, UR Riverly, Lyon, France; 2.Univ Adelaide, Sch Civil Environm & Min Engn, Adelaide, SA, Australia |
推荐引用方式 GB/T 7714 | Renard, Benjamin,Thyer, Mark. Revealing Hidden Climate Indices from the Occurrence of Hydrologic Extremes[J]. WATER RESOURCES RESEARCH,2019,55(9):7662-7681. |
APA | Renard, Benjamin,&Thyer, Mark.(2019).Revealing Hidden Climate Indices from the Occurrence of Hydrologic Extremes.WATER RESOURCES RESEARCH,55(9),7662-7681. |
MLA | Renard, Benjamin,et al."Revealing Hidden Climate Indices from the Occurrence of Hydrologic Extremes".WATER RESOURCES RESEARCH 55.9(2019):7662-7681. |
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