干旱区生态环境知识资源中心(Arid): An evaluation of two statistical downscaling models for downscaling monthly precipitation in the Heihe River basin of China

Arid

DOI	10.1007/s00704-019-02925-6
	An evaluation of two statistical downscaling models for downscaling monthly precipitation in the Heihe River basin of China
	Su, Haifeng 1,2; Xiong, Zhe 1; Yan, Xiaodong 3; Dai, Xingang 1
通讯作者	Xiong, Zhe
来源期刊	THEORETICAL AND APPLIED CLIMATOLOGY
ISSN	0177-798X
EISSN	1434-4483
出版年	2019
卷号	138 期号:3-4 页码:1913-1923
英文摘要	The stepwise regression model (SRM) is a widely used statistical downscaling method that could be used to establish a statistical relationship between observed precipitation and predictors. However, the SRM cannot reflect the contributions of predictors to precipitation reasonably, which may not be the best model based on several possible competing predictors. Bayesian model averaging (BMA) is a standard inferencing approach that considers multiple competing statistical models. The BMA infers precipitation predictions by weighing individual predictors based on their probabilistic likelihood measures over the training period, with the better-performing predictions receiving higher weights than the worse-performing ones. Furthermore, the BMA provides a more reliable description of all the predictors than the SRM, leading to a sharper and better calibrated probability density function (PDF) for the probabilistic predictions. In this study, monthly precipitation at fifteen meteorological stations over the period of 1971-2012 in the Heihe River basin (HRB), which is located in an arid area of Northwest China, was simulated using the Bayesian model averaging (BMA) and the stepwise regression model (SRM), which was then compared with the observed datasets (OBS). The results showed that the BMA produced more accurate results than the SRM when used to statistically downscale large-scale variables. The multiyear mean precipitation results for twelve of the fifteen meteorological stations that were simulated by the BMA were better than those simulated by the SRM. The RMSE and MAE of the BMA for each station were lower than those of the SRM. The BMA had a lower mean RMSE (- 13.93%) and mean MAE (- 14.37%) compared with the SRM. The BMA could reduce the RMSEs and MAEs of precipitation and improve the correlation coefficient effectively. This indicates that the monthly precipitation simulated by the BMA has better consistency with the observed values.
类型	Article
语种	英语
国家	Peoples R China
收录类别	SCI-E
WOS记录号	WOS:000494710000047
WOS关键词	RAINFALL ; ENSEMBLE ; SIMULATION ; OUTPUT ; SCALE
WOS类目	Meteorology & Atmospheric Sciences
WOS研究方向	Meteorology & Atmospheric Sciences
EI主题词	2019-11-01
来源机构	北京师范大学 ; 中国科学院大气物理研究所
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/310597
作者单位	1.Chinese Acad Sci, Inst Atmospher Phys, Key Lab Reg Climate Environm Temperate East Asia, 40 HuaYanLi, Beijing 100029, Peoples R China; 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China; 3.Beijing Normal Univ, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100087, Peoples R China
推荐引用方式 GB/T 7714	Su, Haifeng,Xiong, Zhe,Yan, Xiaodong,et al. An evaluation of two statistical downscaling models for downscaling monthly precipitation in the Heihe River basin of China[J]. 北京师范大学, 中国科学院大气物理研究所,2019,138(3-4):1913-1923.
APA	Su, Haifeng,Xiong, Zhe,Yan, Xiaodong,&Dai, Xingang.(2019).An evaluation of two statistical downscaling models for downscaling monthly precipitation in the Heihe River basin of China.THEORETICAL AND APPLIED CLIMATOLOGY,138(3-4),1913-1923.
MLA	Su, Haifeng,et al."An evaluation of two statistical downscaling models for downscaling monthly precipitation in the Heihe River basin of China".THEORETICAL AND APPLIED CLIMATOLOGY 138.3-4(2019):1913-1923.