干旱区生态环境知识资源中心(Arid): Reconstruction of missing streamflow series in human-regulated catchments using a data integration LSTM model

Arid

DOI	10.1016/j.ejrh.2024.101744
	Reconstruction of missing streamflow series in human-regulated catchments using a data integration LSTM model
	Tursun, Arken; Xie, Xianhong; Wang, Yibing; Liu, Yao; Peng, Dawei; Rusuli, Yusufujiang; Zheng, Buyun
通讯作者	Xie, XH
来源期刊	JOURNAL OF HYDROLOGY-REGIONAL STUDIES
EISSN	2214-5818
出版年	2024
卷号	52
英文摘要	Study region: Yellow River Basin in China, where streamflow dynamics were significantly impacted by human activities. Study focus: We introduced a deep learning-based method, i.e., Data Integration (DI) with Long Short -Term Memory (LSTM), which leverages Global Flood Awareness System (GloFAS) streamflow data. Multiscale (Catchment, River) attributes were incorporated into the DI LSTM to represent human disturbances on land surface. We employed this method to reconstruct daily streamflow series in 60 human-regulated catchments across the Yellow River Basin, and identified the sensitivity of the DI LSTM model to the multiscale attributes. New hydrological Insights for the Region: Our findings revealed that the DI LSTM model achieved favourable performance in streamflow estimation, with the highest Kling-Gupta efficiency (KGE) reaching up to 0.9, outperforming the Regular LSTM model, which was forced by meteorological variables. Multiscale attributes can enhance the DI model performance, particularly in large catchments with significant human activities. A two-step validation demonstrated the high accuracy of the reconstructed streamflow data across the Yellow River Basin, as the KGEs for streamflow estimation in 40 catchments are over 0.6. In summary, the DI LSTM model shows great potential for reconstructing streamflow in human-regulated catchments in arid regions. The reconstructed daily streamflow data contribute valuable insights for monitoring changing hydrological conditions, especially in regions lacking extensive streamflow monitoring networks.
英文关键词	Data integration LSTM Multiscale static attributes Streamflow reconstruction Human activity Yellow River Basin
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:001210967000001
WOS关键词	MEMORY ; NETWORKS
WOS类目	Water Resources
WOS研究方向	Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404628
推荐引用方式 GB/T 7714	Tursun, Arken,Xie, Xianhong,Wang, Yibing,et al. Reconstruction of missing streamflow series in human-regulated catchments using a data integration LSTM model[J],2024,52.
APA	Tursun, Arken.,Xie, Xianhong.,Wang, Yibing.,Liu, Yao.,Peng, Dawei.,...&Zheng, Buyun.(2024).Reconstruction of missing streamflow series in human-regulated catchments using a data integration LSTM model.JOURNAL OF HYDROLOGY-REGIONAL STUDIES,52.
MLA	Tursun, Arken,et al."Reconstruction of missing streamflow series in human-regulated catchments using a data integration LSTM model".JOURNAL OF HYDROLOGY-REGIONAL STUDIES 52(2024).