气候变化领域集成服务门户(CCPortal): Attribution of Runoff Variation in Reservoir Construction Area: Based on a Merged Deep Learning Model and the Budyko Framework

CCPortal

DOI	10.3390/atmos15020164
	Attribution of Runoff Variation in Reservoir Construction Area: Based on a Merged Deep Learning Model and the Budyko Framework
	Zhang, Lilan; Chen, Xiaohong; Huang, Bensheng; Chen, Liangxiong; Liu, Jie
发表日期	2024
EISSN	2073-4433
起始页码	15
结束页码	2
卷号	15 期号:2
英文摘要	This study presents a framework to attribute river runoff variations to the combined effects of reservoir operations, land surface changes, and climate variability. We delineated the data into natural and impacted periods. For the natural period, an integrated Long Short-Term Memory and Random Forest model was developed to accurately simulate both mean and extreme runoff values, outperforming existing models. This model was then used to estimate runoff unaffected by human activities in the impacted period. Our findings indicate stable annual and wet season mean runoff, with a decrease in wet season maximums and an increase in dry season means, while extreme values remained largely unchanged. A Budyko framework incorporating reconstructed runoff revealed that rainfall and land surface changes are the predominant factors influencing runoff variations in wet and dry seasons, respectively, and land surface impacts become more pronounced during the impacted period for both seasons. Human activities dominate dry season runoff variation (93.9%), with climate change at 6.1%, while in the wet season, the split is 64.5% to 35.5%. Climate change and human activities have spontaneously led to reduced runoff during the wet season and increased runoff during the dry season. Only reservoir regulation is found to be linked to human-induced runoff changes, while the effects of land surface changes remain ambiguous. These insights underscore the growing influence of anthropogenic factors on hydrological extremes and quantify the role of reservoirs within the impacts of human activities on runoff.
英文关键词	climate change; Budyko framework; LSTM; reservoir operation; runoff variation attribution
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS类目	Environmental Sciences ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:001168334100001
来源期刊	ATMOSPHERE
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/308470
作者单位	Tsinghua University; Sun Yat Sen University
推荐引用方式 GB/T 7714	Zhang, Lilan,Chen, Xiaohong,Huang, Bensheng,et al. Attribution of Runoff Variation in Reservoir Construction Area: Based on a Merged Deep Learning Model and the Budyko Framework[J],2024,15(2).
APA	Zhang, Lilan,Chen, Xiaohong,Huang, Bensheng,Chen, Liangxiong,&Liu, Jie.(2024).Attribution of Runoff Variation in Reservoir Construction Area: Based on a Merged Deep Learning Model and the Budyko Framework.ATMOSPHERE,15(2).
MLA	Zhang, Lilan,et al."Attribution of Runoff Variation in Reservoir Construction Area: Based on a Merged Deep Learning Model and the Budyko Framework".ATMOSPHERE 15.2(2024).