干旱区生态环境知识资源中心(Arid): Impacts of Using State-of-the-Art Multivariate Bias Correction Methods on Hydrological Modeling Over North America

Arid

DOI	10.1029/2019WR026659
	Impacts of Using State-of-the-Art Multivariate Bias Correction Methods on Hydrological Modeling Over North America
	Guo, Qiang; Chen, Jie; Zhang, Xunchang John; Xu, Chong-Yu; Chen, Hua
通讯作者	Chen, J
来源期刊	WATER RESOURCES RESEARCH
ISSN	0043-1397
EISSN	1944-7973
出版年	2020
卷号	56 期号:5
英文摘要	Bias correction techniques are widely used to bridge the gap between climate model outputs and input requirements of hydrological models to assess the climate change impacts on hydrology. In addition to univariate bias correction methods, several multivariate bias correction methods were proposed recently, which can not only correct the biases in marginal distributions of individual climate variables but also properly adjust the biased intervariable correlations simulated by climate models. Due to the diversities of climate regime and climate model bias, hydrological simulation for watersheds under different climate conditions may show various sensitivities to the correction of intervariable correlations. Therefore, it is of great importance to investigate (1) whether the correction of intervariable correlations has impacts on the hydrological modeling and (2) how these impacts vary with watersheds under different climate conditions. To achieve these goals, this study evaluates behaviors and their spatial variability of multiple state-of-the-art multivariate bias correction methods in hydrological modeling over 2,840 watersheds distributed in different climate regimes in North America. The results show that, compared to using a quantile mapping univariate bias correction method, applying multivariate methods can improve the simulation of snow proportion, snowmelt, evaporation, and several streamflow variables. In addition, this improvement is more clear for watersheds with arid and warm temperate climates in southern regions, while it is limited for northern snow-characterized watersheds. Overall, this study demonstrates the importance of using multivariate bias correction methods instead of univariate methods in hydrological climate change impact studies, especially for watersheds with arid and warm temperate climates.
英文关键词	multivariate bias correction methods hydrological modeling intervariable correlation climate regimes North America
类型	Article
语种	英语
开放获取类型	Green Published
收录类别	SCI-E
WOS记录号	WOS:000537736400042
WOS关键词	CLIMATE-CHANGE IMPACTS ; PART 2 ; PRECIPITATION ; SIMULATIONS ; TEMPERATURE ; OUTPUTS ; UNCERTAINTY ; SENSITIVITY ; DEPENDENCE ; RUNOFF
WOS类目	Environmental Sciences ; Limnology ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/324632
作者单位	[Guo, Qiang; Chen, Jie; Chen, Hua] Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan, Peoples R China; [Guo, Qiang; Chen, Jie] Wuhan Univ, Hubei Prov Key Lab Water Syst Sci Sponge City Con, Wuhan, Peoples R China; [Zhang, Xunchang John] ARS, USDA, Grazinglands Res Lab, El Reno, OK USA; [Xu, Chong-Yu] Univ Oslo, Dept Geosci, Oslo, Norway
推荐引用方式 GB/T 7714	Guo, Qiang,Chen, Jie,Zhang, Xunchang John,et al. Impacts of Using State-of-the-Art Multivariate Bias Correction Methods on Hydrological Modeling Over North America[J],2020,56(5).
APA	Guo, Qiang,Chen, Jie,Zhang, Xunchang John,Xu, Chong-Yu,&Chen, Hua.(2020).Impacts of Using State-of-the-Art Multivariate Bias Correction Methods on Hydrological Modeling Over North America.WATER RESOURCES RESEARCH,56(5).
MLA	Guo, Qiang,et al."Impacts of Using State-of-the-Art Multivariate Bias Correction Methods on Hydrological Modeling Over North America".WATER RESOURCES RESEARCH 56.5(2020).