干旱区生态环境知识资源中心(Arid): An Uncertainty-Based Regional Comparative Analysis on the Performance of Different Bias Correction Methods in Statistical Downscaling of Precipitation

Arid

DOI	10.1007/s11269-021-02844-0
	An Uncertainty-Based Regional Comparative Analysis on the Performance of Different Bias Correction Methods in Statistical Downscaling of Precipitation
	Rahimi, Reyhaneh; Tavakol-Davani, Hassan; Nasseri, Mohsen
通讯作者	Rahimi, R (corresponding author), San Diego State Univ, Urban Water Grp, San Diego, CA 92182 USA.
来源期刊	WATER RESOURCES MANAGEMENT
ISSN	0920-4741
EISSN	1573-1650
出版年	2021
卷号	35 期号:8 页码:2503-2518
英文摘要	Statistical downscaling of General Circulation Models (GCM) simulations is widely used for projecting precipitation at different spatiotemporal scales. However, the downscaling process is linked with different source of uncertainty including structural/parametric uncertainty of the model and output uncertainty. This research proposes a novel framework to assess the parametric uncertainty of downscaling model, and used this framework to assess the performance of different bias correction methods linked to the regression-based statistical downscaling model. The used downscaling framework in the current paper is Statistical Downscaling Model (SDSM). The conventional bias correction method linked with SDSM is the Variance InFlation method (VIF), this paper substitutes this method with three different bias correction methods including Local Intensity Scaling (LOCI), Power Transformation (PT), and Quantile Mapping (QM) to assess the associated parametric and global uncertainty of each method in different climate by using a new approach. The proposed method is applied to six different stations located in Iran and United States with different climate status. Average Relative Interval Length (ARIL), P-level, and Normalized Uncertainty Efficiency (NUE) are used as uncertainty indicators to evaluate the results. Results represent that in every assessed climate class, LOCI, and PT, work better than conventional VIF in both amount and occurrence modules of SDSM framework. More precisely, LOCI works better in station that has wet summer, while PT performs well in the stations where there is no or very limited precipitation in summer. Substituting LOCI with VIF, result in increasing the value of NUE by at least factor of 3 in occurrence and amount model which means the significant reduction in structural uncertainty. Also applying PT in arid regions improves the NUE indicator at least by factor 2 in occurrence and amount model and by factor 3 in output uncertainty assessment, and results in less parametric and output uncertainty. Results illustrate the important role of bias correction approaches in reducing structural, and output uncertainty and improving the statistical efficiency of the downscaling model.
英文关键词	Uncertainty analysis Bias correction SDSM Downscaling Climate change
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000655960100001
WOS关键词	CLIMATE-CHANGE IMPACT
WOS类目	Engineering, Civil ; Water Resources
WOS研究方向	Engineering ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/351983
作者单位	[Rahimi, Reyhaneh; Tavakol-Davani, Hassan] San Diego State Univ, Urban Water Grp, San Diego, CA 92182 USA; [Nasseri, Mohsen] Univ Tehran, Sch Civil Engn, Coll Engn, Tehran, Iran
推荐引用方式 GB/T 7714	Rahimi, Reyhaneh,Tavakol-Davani, Hassan,Nasseri, Mohsen. An Uncertainty-Based Regional Comparative Analysis on the Performance of Different Bias Correction Methods in Statistical Downscaling of Precipitation[J],2021,35(8):2503-2518.
APA	Rahimi, Reyhaneh,Tavakol-Davani, Hassan,&Nasseri, Mohsen.(2021).An Uncertainty-Based Regional Comparative Analysis on the Performance of Different Bias Correction Methods in Statistical Downscaling of Precipitation.WATER RESOURCES MANAGEMENT,35(8),2503-2518.
MLA	Rahimi, Reyhaneh,et al."An Uncertainty-Based Regional Comparative Analysis on the Performance of Different Bias Correction Methods in Statistical Downscaling of Precipitation".WATER RESOURCES MANAGEMENT 35.8(2021):2503-2518.