气候变化领域集成服务门户(CCPortal): Ensemble streamflow forecasting over a cascade reservoir catchment with integrated hydrometeorological modeling and machine learning

CCPortal

DOI	10.5194/hess-26-265-2022
	Ensemble streamflow forecasting over a cascade reservoir catchment with integrated hydrometeorological modeling and machine learning
	Liu, Junjiang; Yuan, Xing; Zeng, Junhan; Jiao, Yang; Li, Yong; Zhong, Lihua; Yao, Ling
发表日期	2022
ISSN	1027-5606
EISSN	1607-7938
起始页码	265
结束页码	278
卷号	26 期号:2
英文摘要	A popular way to forecast streamflow is to use bias-corrected meteorological forecasts to drive a calibrated hydrological model, but these hydrometeorological approaches suffer from deficiencies over small catchments due to uncertainty in meteorological forecasts and errors from hydrological models, especially over catchments that are regulated by dams and reservoirs. For a cascade reservoir catchment, the discharge from the upstream reservoir contributes to an important part of the streamflow over the downstream areas, which makes it tremendously hard to explore the added value of meteorological forecasts. Here, we integrate meteorological forecasts, land surface hydrological model simulations and machine learning to forecast hourly streamflow over the Yantan catchment, where the streamflow is influenced by both the upstream reservoir water release and the rainfall-runoff processes within the catchment. Evaluation of the hourly streamflow hindcasts during the rainy seasons of 2013-2017 shows that the hydrometeorological ensemble forecast approach reduces probabilistic and deterministic forecast errors by 6 % compared with the traditional ensemble streamflow prediction (ESP) approach during the first 7 d. The deterministic forecast error can be further reduced by 6 % in the first 72 h when combining the hydrometeorological forecasts with the long short-term memory (LSTM) deep learning method. However, the forecast skill for LSTM using only historical observations drops sharply after the first 24 h. This study implies the potential of improving flood forecasts over a cascade reservoir catchment by integrating meteorological forecasts, hydrological modeling and machine learning.
语种	英语
WOS研究方向	Geosciences, Multidisciplinary ; Water Resources
WOS类目	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:000747077100001
来源期刊	HYDROLOGY AND EARTH SYSTEM SCIENCES
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/280977
作者单位	Nanjing University of Information Science & Technology; Chinese Academy of Sciences; Institute of Atmospheric Physics, CAS
推荐引用方式 GB/T 7714	Liu, Junjiang,Yuan, Xing,Zeng, Junhan,et al. Ensemble streamflow forecasting over a cascade reservoir catchment with integrated hydrometeorological modeling and machine learning[J],2022,26(2).
APA	Liu, Junjiang.,Yuan, Xing.,Zeng, Junhan.,Jiao, Yang.,Li, Yong.,...&Yao, Ling.(2022).Ensemble streamflow forecasting over a cascade reservoir catchment with integrated hydrometeorological modeling and machine learning.HYDROLOGY AND EARTH SYSTEM SCIENCES,26(2).
MLA	Liu, Junjiang,et al."Ensemble streamflow forecasting over a cascade reservoir catchment with integrated hydrometeorological modeling and machine learning".HYDROLOGY AND EARTH SYSTEM SCIENCES 26.2(2022).