干旱区生态环境知识资源中心(Arid): Evaluating the downscaling uncertainty of hydrometeorological data in snowmelt runoff simulation

Arid

DOI	10.1007/s00477-021-02143-5
	Evaluating the downscaling uncertainty of hydrometeorological data in snowmelt runoff simulation
	Hu, Haoxin; Zeng, Xiankui; Cai, Xing; Gui, Dongwei; Wu, Jichun; Wang, Dong
通讯作者	Zeng, XK (corresponding author), Nanjing Univ, Sch Earth Sci & Engn, Key Lab Surficial Geochem, Minist Educ, Nanjing, Peoples R China.
来源期刊	STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
ISSN	1436-3240
EISSN	1436-3259
出版年	2021-12
英文摘要	Snowmelt runoff is the main water source in cold and arid areas, and hydrological models provide a useful tool for water resource management in such areas. The downscaling of hydrometeorological data is an important method of obtaining input data for hydrological models. In this study, the snowmelt runoff of the Cele River basin was simulated using the snowmelt runoff model (SRM), and large-scale precipitation and temperature data were downscaled and used as the input of the SRM. To evaluate the downscaling uncertainty in snowmelt runoff modeling, four commonly used downscaling methods were selected, and Bayesian stacking was used to reduce the downscaling uncertainty by combining the predictions from these downscaling methods. Additionally, Markov chain Monte Carlo simulation was conducted to calibrate the model parameters and generate predictions for the four SRMs, corresponding to the four downscaling methods. Four metrics were used to evaluate the performances of the four SRMs and Bayesian stacking in daily and monthly runoff predictions. The results demonstrated that the performances of the four downscaling methods in runoff prediction differed, and none of the downscaling methods were superior to the others in runoff prediction for all evaluation metrics. Model parameter and downscaling method contributed 45.62% and 54.38% of the predictive uncertainty in runoff prediction, respectively. Thus, downscaling could lead to non-negligible uncertainty in snowmelt runoff modeling. Bayesian stacking achieved good and reliable performance in daily and monthly runoff predictions and effectively reduced the uncertainty of snowmelt runoff modeling.
英文关键词	Downscaling Snowmelt runoff model Bayesian stacking Uncertainty
类型	Article ; Early Access
语种	英语
开放获取类型	Bronze
收录类别	SCI-E
WOS记录号	WOS:000725472700001
WOS关键词	SUPPORT VECTOR ; PRECIPITATION ; MODEL ; TEMPERATURE ; REGRESSION ; MACHINE ; FLUXES
WOS类目	Engineering, Environmental ; Engineering, Civil ; Environmental Sciences ; Statistics & Probability ; Water Resources
WOS研究方向	Engineering ; Environmental Sciences & Ecology ; Mathematics ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/374681
作者单位	[Hu, Haoxin; Zeng, Xiankui; Cai, Xing; Wu, Jichun; Wang, Dong] Nanjing Univ, Sch Earth Sci & Engn, Key Lab Surficial Geochem, Minist Educ, Nanjing, Peoples R China; [Gui, Dongwei] Chinese Acad Sci, Xinjiang Inst Ecol & Geog, Cele Natl Stn Observat & Res Desert Grassland Eco, Urumqi, Peoples R China
推荐引用方式 GB/T 7714	Hu, Haoxin,Zeng, Xiankui,Cai, Xing,et al. Evaluating the downscaling uncertainty of hydrometeorological data in snowmelt runoff simulation[J],2021.
APA	Hu, Haoxin,Zeng, Xiankui,Cai, Xing,Gui, Dongwei,Wu, Jichun,&Wang, Dong.(2021).Evaluating the downscaling uncertainty of hydrometeorological data in snowmelt runoff simulation.STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT.
MLA	Hu, Haoxin,et al."Evaluating the downscaling uncertainty of hydrometeorological data in snowmelt runoff simulation".STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT (2021).