干旱区生态环境知识资源中心(Arid): A novel statistical downscaling approach for analyzing daily precipitation and extremes under the impact of climate change: Application to an arid region

Arid

DOI	10.1016/j.jhydrol.2022.128730
	A novel statistical downscaling approach for analyzing daily precipitation and extremes under the impact of climate change: Application to an arid region
	Zhang, Q.; Li, Y. P.; Huang, G. H.; Wang, H.; Li, Y. F.; Liu, Y. R.; Shen, Z. Y.
通讯作者	Li, Y
来源期刊	JOURNAL OF HYDROLOGY
ISSN	0022-1694
EISSN	1879-2707
出版年	2022
卷号	615
英文摘要	Statistical downscaling of daily precipitation is important for assessing the impact of climate change. However, some issues (e.g., the underestimation of extremes) limit statistical downscaling methods to project daily pre-cipitation accurately. This study develops an integrated hybrid statistical downscaling model combined with bias correction and Bayesian model averaging (named as SDBC-BMA) for improving the ability of downscaling methods. The performance of SDBC is compared with regression based statistical downscaling methods (RSDM) and hybrid statistical downscaling methods (HSDM) based on different performance metrics and extreme indices. SDBC-BMA is applied to the Amu Darya River Basin (ADRB) to demonstrate its feasibility and capability, where three GCMs and two CMIP6 scenarios are considered. Several findings can be summarized: (1) compared to RSDM and HSDM, SDBC is more robust with a higher computation ability of daily precipitation and a lower mean bias (i.e., +0.10 mm/day); (2) compared with the simple model average, the biases of extreme indices and mean daily precipitation obtained from SDBC-BMA could decrease by 49 % and 88 %, respectively; (3) the mean precipitation values would increase by 56 +/- 41 mm/year (under SSP245) and 126 +/- 55 mm/year (under SSP585) during 2076-2100, higher than mean precipitation in the historical period (1979-2005); (4) extreme indices under multi-GCMs (except for consecutive wet days) would increase by 20 % (under SSP245) and 22 % (under SSP585), implying that extreme events in ADRB are more frequent in the 21st century. The increasing risk of precipitation extremes would exacerbate threats to agricultural and ecological security.
英文关键词	Amu Darya River basin Bayesian model averaging Climate change CMIP6 Statistical downscaling
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000895954000002
WOS关键词	WATER-RESOURCES ; TEMPERATURE ; REGRESSION ; MODEL ; PERFORMANCE ; PROJECTIONS
WOS类目	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向	Engineering ; Geology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393513
推荐引用方式 GB/T 7714	Zhang, Q.,Li, Y. P.,Huang, G. H.,et al. A novel statistical downscaling approach for analyzing daily precipitation and extremes under the impact of climate change: Application to an arid region[J],2022,615.
APA	Zhang, Q..,Li, Y. P..,Huang, G. H..,Wang, H..,Li, Y. F..,...&Shen, Z. Y..(2022).A novel statistical downscaling approach for analyzing daily precipitation and extremes under the impact of climate change: Application to an arid region.JOURNAL OF HYDROLOGY,615.
MLA	Zhang, Q.,et al."A novel statistical downscaling approach for analyzing daily precipitation and extremes under the impact of climate change: Application to an arid region".JOURNAL OF HYDROLOGY 615(2022).