气候变化领域集成服务门户(CCPortal): A new downscaling approach and its performance with bias correction and spatial disaggregation as contrast

CCPortal

DOI	10.2166/wcc.2017.010
	A new downscaling approach and its performance with bias correction and spatial disaggregation as contrast
	Zhang S.; Chen F.; He X.; Liu B.
发表日期	2017
ISSN	20402244
起始页码	675
结束页码	690
卷号	8 期号:4
英文摘要	Bias correction and spatial disaggregation (BCSD) is widely used in coupling general circulation models (GCMs) and hydrological models. However, there are some disadvantages in BCSD, such as only one GCM being selected, correcting biases through quantile-mapping (QM), and downscaling through interpolation. Then a combined approach of canonical correlation analysis filtering, multi-model ensemble, and extreme learning machine (ELM) regressions (CEE) was advanced. The performance of CEE and BCSD was evaluated with Manas River Basin as a study area. Results show it is unreasonable to correct biases through QM as it implies that the climate remains unchanged. Multi-model ensemble provides additional information, which is beneficial for regressions. CEE performs better than BCSD in temperature and precipitation rate downscaling. In CEE, the residual in temperature forecasting can be lower than 0.05 times temperature range and that in precipitation rate can be 0.33 times precipitation rate range. The performance of CEE in temperature downscaling in plains is better than mountainous areas, but for precipitation rate downscaling, it is better in mountainous areas. Increasing rate of temperature in the basin is 0.0254 K/decade, 0.1837 K/decade, and 0.5039 K/decade, and that of precipitation rate is 0.0028 mm/(day × decade), 0.0036 mm/(day × decade), and 0.0022 mm/(day × decade) in RCP2.6, RCP4.5, and RCP8.5, respectively. © IWA Publishing 2017.
英文关键词	BCSD; CEE; Downscaling; Precipitation rate; QM; Temperature
语种	英语
scopus关键词	Climate change; Temperature; BCSD; Canonical correlation analysis; Down-scaling; Extreme learning machine; General circulation model; Precipitation rates; Spatial disaggregation; Temperature forecasting; Learning systems; air temperature; canonical analysis; downscaling; ensemble forecasting; interpolation; machine learning; mapping; precipitation (climatology); China; Manas Basin; Xinjiang Uygur
来源期刊	Journal of Water and Climate Change
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/148102
作者单位	College of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi, 832000, China; State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin, 300072, China
推荐引用方式 GB/T 7714	Zhang S.,Chen F.,He X.,et al. A new downscaling approach and its performance with bias correction and spatial disaggregation as contrast[J],2017,8(4).
APA	Zhang S.,Chen F.,He X.,&Liu B..(2017).A new downscaling approach and its performance with bias correction and spatial disaggregation as contrast.Journal of Water and Climate Change,8(4).
MLA	Zhang S.,et al."A new downscaling approach and its performance with bias correction and spatial disaggregation as contrast".Journal of Water and Climate Change 8.4(2017).