Spatial-temporal Analysis and Prediction of Precipitation Extremes: A Case Study in the Weihe River Basin, China
文献类型：期刊论文
| 作者 | Qiu Dexun1,2; Wu Changxue2; Mu Xingmin1,2,3; Zhao Guangju1,2,3; Gao Peng1,2,3 |
| 刊名 | CHINESE GEOGRAPHICAL SCIENCE
 |
| 出版日期 | 2022-04-01 |
| 卷号 | 32期号:2页码:358-372 |
| 关键词 | precipitation extremes space-time cube (STC) ensemble empirical mode decomposition (EEMD) long short-term memory (LSTM) auto-regressive integrated moving average (ARIMA) Weihe River Basin China |
| ISSN号 | 1002-0063 |
| DOI | 10.1007/s11769-022-1271-7 |
| 通讯作者 | Mu Xingmin(xmmu@ms.iswc.ac.cn) ; Gao Peng(gaopeng@ms.iswc.ac.cn) |
| 英文摘要 | Extreme precipitation events bring considerable risks to the natural ecosystem and human life. Investigating the spatial-temporal characteristics of extreme precipitation and predicting it quantitatively are critical for the flood prevention and water resources planning and management. In this study, daily precipitation data (1957-2019) were collected from 24 meteorological stations in the Weihe River Basin (WRB), Northwest China and its surrounding areas. We first analyzed the spatial-temporal change of precipitation extremes in the WRB based on space- time cube (STC), and then predicted precipitation extremes using long short-term memory (LSTM) network, auto-regressive integrated moving average (ARIMA), and hybrid ensemble empirical mode decomposition (EEMD)-LSTM-ARIMA models. The precipitation extremes increased as the spatial variation from northwest to southeast of the WRB. There were two clusters for each extreme precipitation index, which were distributed in the northwestern and southeastern or northern and southern of the WRB. The precipitation extremes in the WRB present a strong clustering pattern. Spatially, the pattern of only high-high cluster and only low-low cluster were primarily located in lower reaches and upper reaches of the WRB, respectively. Hot spots (25.00%-50.00%) were more than cold spots (4.17%-25.00%) in the WRB. Cold spots were mainly concentrated in the northwestern part, while hot spots were mostly located in the eastern and southern parts. For different extreme precipitation indices, the performances of the different models were different. The accuracy ranking was EEMD-LSTM-ARIMA > LSTM > ARIMA in predicting simple daily intensity index (SDII) and consecutive wet days (CWD), while the accuracy ranking was LSTM > EEMD-LSTM-ARIMA > ARIMA in predicting very wet days (R95P). The hybrid EEMD-LSTM-ARIMA model proposed was generally superior to single models in the prediction of precipitation extremes. |
| WOS关键词 | EMPIRICAL MODE DECOMPOSITION ; TIME-FREQUENCY ANALYSIS ; LONG-TERM TRENDS ; SPATIOTEMPORAL VARIATIONS ; NON-STATIONARITY ; NEURAL-NETWORK ; CLIMATE-CHANGE ; RAINFALL ; EVENTS ; RUNOFF |
| 资助项目 | National Key Research and Development Program of China[2017YFE0118100-1] |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:000771673000012 |
| 出版者 | SPRINGER |
| 资助机构 | National Key Research and Development Program of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/172716]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Mu Xingmin; Gao Peng |
| 作者单位 | 1.Chinese Acad Sci & Minist Water Resources, Inst Soil & Water Conservat, State Key Lab Soil Eros & Dryland Farming Loess P, Yangling 712100, Shaanxi, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Northwest Agr & Forestry Univ, Inst Soil & Water Conservat, State Key Lab Soil Eros & Dryland Farming Loess P, Yangling 712100, Shaanxi, Peoples R China |
| 推荐引用方式 GB/T 7714 | Qiu Dexun,Wu Changxue,Mu Xingmin,et al. Spatial-temporal Analysis and Prediction of Precipitation Extremes: A Case Study in the Weihe River Basin, China[J]. CHINESE GEOGRAPHICAL SCIENCE,2022,32(2):358-372. |
| APA | Qiu Dexun,Wu Changxue,Mu Xingmin,Zhao Guangju,&Gao Peng.(2022).Spatial-temporal Analysis and Prediction of Precipitation Extremes: A Case Study in the Weihe River Basin, China.CHINESE GEOGRAPHICAL SCIENCE,32(2),358-372. |
| MLA | Qiu Dexun,et al."Spatial-temporal Analysis and Prediction of Precipitation Extremes: A Case Study in the Weihe River Basin, China".CHINESE GEOGRAPHICAL SCIENCE 32.2(2022):358-372. |
入库方式： OAI收割
来源：地理科学与资源研究所
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