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DOI | 10.1002/joc.6065 |
Non-stationary modelling of extreme precipitation by climate indices during rainy season in Hanjiang River Basin, China | |
Hao, Wenlong1; Shao, Quanxi2; Hao, Zhenchun1; Ju, Qin1; Baima, Wangdui3; Zhang, Dawei4 | |
2019-08-01 | |
发表期刊 | INTERNATIONAL JOURNAL OF CLIMATOLOGY |
ISSN | 0899-8418 |
EISSN | 1097-0088 |
出版年 | 2019 |
卷号 | 39期号:10页码:4154-4169 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China; Australia |
英文摘要 | The extreme precipitation regimes have been changing as the climate system has warmed. Investigating the non-stationarity and better estimating the changes of the extreme precipitation are valuable for informing policy decisions. In this study, two precipitation indices are employed to describe the extreme events, including maximum 5-day precipitation amount (RX5day) and the number of very heavy precipitation days (R20). The generalized additive models for location, scale and shape (GAMLSS) is employed to characterize non-stationarities in extreme precipitation events and related climate indices in 13 stations in the Hanjiang River basin (HJRB). Three models including stationary model without change (M0), non-stationary models over time (M1) and non-stationarity models with large-scale climate indices (M2) as predictors, respectively, are considered to analyse occurrence rates of extreme precipitation. The optimal model and the significant predictors were selected by the Akaike information criterion (AIC). To investigate the main predictors at regional scale, the homogeneous subregions for precipitation extremes are identified by clustering analysis. Results indicate that: (a) the non-stationarities of RX5day series and R20 series at all stations are identified in the HJRB; (b) extreme precipitation behaviour is significantly influenced by climate indices and non-stationary model 2 to describe the changes of extreme precipitation is better than non-stationary model 1, indicating the impact of large-scale climate forcing on the changes of extreme precipitation regimes; (c) the HJRB can be categorized into three homogenous regions. The optimal distributions and the main predictors of extreme precipitation events in most stations of each subregion are basically the same; (d) the dominated climate indices influencing the extreme precipitation events are different in different regions and have regional patterns. The results highlight the modelling of extreme precipitation events under non-stationarity conditions and provide information for developing strategies of mitigation and adaptation to climate change impacts on extreme precipitation. |
英文关键词 | climate indices extreme precipitation GAMLSS Hanjiang River Basin non-stationarity |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000479031900018 |
WOS关键词 | FREQUENCY-ANALYSIS ; YANGTZE-RIVER ; HEAVY PRECIPITATION ; REGIONAL FREQUENCY ; SUMMER MONSOON ; ENSO ; RAINFALL ; TRENDS ; EVENTS ; SCALE |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
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/185697 |
专题 | 气候变化 |
作者单位 | 1.Hohai Univ, Coll Hydrol & Water Resources, Nanjing 210098, Jiangsu, Peoples R China; 2.CSIRO, Data 61, Floreat, Australia; 3.Tibet Autonomous Reg Hydrol Bur, Linzhi Branch, Linzhi, Peoples R China; 4.China Inst Water Resources & Hydropower Res, Res Ctr Flood & Drought Reduct, Beijing, Peoples R China |
推荐引用方式 GB/T 7714 | Hao, Wenlong,Shao, Quanxi,Hao, Zhenchun,et al. Non-stationary modelling of extreme precipitation by climate indices during rainy season in Hanjiang River Basin, China[J]. INTERNATIONAL JOURNAL OF CLIMATOLOGY,2019,39(10):4154-4169. |
APA | Hao, Wenlong,Shao, Quanxi,Hao, Zhenchun,Ju, Qin,Baima, Wangdui,&Zhang, Dawei.(2019).Non-stationary modelling of extreme precipitation by climate indices during rainy season in Hanjiang River Basin, China.INTERNATIONAL JOURNAL OF CLIMATOLOGY,39(10),4154-4169. |
MLA | Hao, Wenlong,et al."Non-stationary modelling of extreme precipitation by climate indices during rainy season in Hanjiang River Basin, China".INTERNATIONAL JOURNAL OF CLIMATOLOGY 39.10(2019):4154-4169. |
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