Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1002/joc.4955 |
Nonstationary extreme flood/rainfall frequency analysis informed by large-scale oceanic fields for Xidayang Reservoir in North China | |
Zeng, Hang1,2,3; Sun, Xun4,5,6; Lall, Upmanu5,7; Feng, Ping3 | |
2017-08-01 | |
发表期刊 | INTERNATIONAL JOURNAL OF CLIMATOLOGY
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ISSN | 0899-8418 |
EISSN | 1097-0088 |
出版年 | 2017 |
卷号 | 37期号:10 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China; USA |
英文摘要 | Climate is a primary driver for extreme rainfall and flood events. In this paper, the temporally changing flood risk associated with the annual maximum 30-day rainfall (30-day AMR) and the annual maximum daily inflow (AMDI) related to the Xidayang Reservoir catchment is analysed from a climatic context. This is the largest catchment in Daqing River Basin in North China and is highly prone to floods related to the East Asian summer monsoon. Two climate factors, the average May-June-July sea surface temperature anomalies in areas of the northern Indian Ocean and western Pacific Ocean, are identified. They show a high negative correlation with the AMDI and 30-day AMR. Bayesian nonstationary models are then developed for the AMDI and 30-day AMR using the climate predictors identified as covariates. We compared three types of models of AMDI and 30-day AMR: (1) time-invariant, (2) linear temporal trend and (3) climate informed, and found that the climate-informed models exhibit the best performance according to Deviance Information Criterion (DIC) and 90th percentile Bayesian coverage rate for both AMDI and 30-day AMR. A significant decreasing trend is identified in the AMDI and the 30-day AMR, which is found to be associated with the climate predictors. Leave one out cross validation (LOOCV) is used to demonstrate that these models have decent skill in predicting year-to-year variability in flood risk. This can help to provide flood/rainfall dynamic management measures for reservoirs in Daqing River Basin using information from before the beginning of monsoon season, thus facilitating adaptation to a changing climate. |
英文关键词 | extreme flood rainfall frequency analysis climate-informed model Bayesian inference large-scale oceanic fields North China |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000406706200005 |
WOS关键词 | HAIHE RIVER-BASIN ; CLIMATE-CHANGE ; DOWNSCALING MODEL ; GORGES DAM ; FLOOD RISK ; PRECIPITATION ; FUTURE ; VARIABILITY ; STREAMFLOW ; RAINFALL |
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/37668 |
专题 | 气候变化 |
作者单位 | 1.Changsha Univ Sci & Technol, Sch Hydraul Engn, Changsha, Hunan, Peoples R China; 2.Key Lab Water Sediment Sci & Water Disaster Preve, Changsha, Hunan, Peoples R China; 3.Tianjin Univ, State Key Lab Hydraul Engn Simulat & Safety, Tianjin, Peoples R China; 4.East China Normal Univ, Key Lab Geog Informat Sci, Minist Educ, Shanghai, Peoples R China; 5.Columbia Univ, Columbia Water Ctr, Earth Inst, New York, NY USA; 6.East China Normal Univ, Sch Geog Sci, Shanghai 200241, Peoples R China; 7.Columbia Univ, Dept Earth & Environm Engn, New York, NY USA |
推荐引用方式 GB/T 7714 | Zeng, Hang,Sun, Xun,Lall, Upmanu,et al. Nonstationary extreme flood/rainfall frequency analysis informed by large-scale oceanic fields for Xidayang Reservoir in North China[J]. INTERNATIONAL JOURNAL OF CLIMATOLOGY,2017,37(10). |
APA | Zeng, Hang,Sun, Xun,Lall, Upmanu,&Feng, Ping.(2017).Nonstationary extreme flood/rainfall frequency analysis informed by large-scale oceanic fields for Xidayang Reservoir in North China.INTERNATIONAL JOURNAL OF CLIMATOLOGY,37(10). |
MLA | Zeng, Hang,et al."Nonstationary extreme flood/rainfall frequency analysis informed by large-scale oceanic fields for Xidayang Reservoir in North China".INTERNATIONAL JOURNAL OF CLIMATOLOGY 37.10(2017). |
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