气候变化领域集成服务门户(CCPortal): A hybrid hydrologic modelling framework with data-driven and conceptual reservoir operation schemes for reservoir impact assessment and predictions

CCPortal

DOI	10.1016/j.jhydrol.2023.129246
	A hybrid hydrologic modelling framework with data-driven and conceptual reservoir operation schemes for reservoir impact assessment and predictions
	Dong, Ningpeng; Guan, Wenhai; Cao, Jixue; Zou, Yibo; Yang, Mingxiang; Wei, Jianhui; Chen, Liang; Wang, Hao
发表日期	2023
ISSN	0022-1694
EISSN	1879-2707
卷号	619
英文摘要	Reservoirs have been built worldwide to address the water-related issues. To fully understand their potential impacts on the hydrologic regime, explicitly parameterizing reservoir operation in hydrologic models is often required. In this study, two data-driven reservoir operation schemes based on extreme gradient boosting (XGBoost) and artificial neural network (ANN) are respectively developed to predict the reservoir release and storage in hydrologic models for reservoirs with historic in-situ inflow, storage, release data. Then, a hybrid modelling framework is proposed by coupling a high-resolution (3 km) hydrologic model with (1) the developed data-driven reservoir operation schemes and (2) a calibration-free conceptual reservoir operation scheme designed for data-scarce reservoirs. This allows quantitative assessment of the cumulative impacts of dam operation on the hydrologic regime under different reservoir data availability. The framework is applied to the Upper Yangtze River Basin (UYRB) in China that is one of the most regulated river basins across the country due to extensive reservoir construction. Results indicate that both data-driven reservoir operation schemes can well reconstruct the reservoir releases and storage in the UYRB (daily NSE of similar to 0.9), and the XGBoost performs slightly better than ANN. By coupling reservoir operation, the model shows a remarkably improved performance in reconstructing the daily streamflow of the basin. The major reservoirs in the UYRB can redistribute excessive water in the wet season to the dry season and attenuate the high and low flows, leading to enhanced water security along the river. Our approach provides a practical framework for reservoir impact assessment and predictions.
英文关键词	Reservoir operation; XGBoost; Artificial neural network; Data-driven; Streamflow; Hydrologic modelling
语种	英语
WOS研究方向	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS类目	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:000992954300001
来源期刊	JOURNAL OF HYDROLOGY
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/281547
作者单位	Hohai University; China Institute of Water Resources & Hydropower Research; China Three Gorges Corporation; Helmholtz Association; Karlsruhe Institute of Technology
推荐引用方式 GB/T 7714	Dong, Ningpeng,Guan, Wenhai,Cao, Jixue,et al. A hybrid hydrologic modelling framework with data-driven and conceptual reservoir operation schemes for reservoir impact assessment and predictions[J],2023,619.
APA	Dong, Ningpeng.,Guan, Wenhai.,Cao, Jixue.,Zou, Yibo.,Yang, Mingxiang.,...&Wang, Hao.(2023).A hybrid hydrologic modelling framework with data-driven and conceptual reservoir operation schemes for reservoir impact assessment and predictions.JOURNAL OF HYDROLOGY,619.
MLA	Dong, Ningpeng,et al."A hybrid hydrologic modelling framework with data-driven and conceptual reservoir operation schemes for reservoir impact assessment and predictions".JOURNAL OF HYDROLOGY 619(2023).