气候变化领域集成服务门户(CCPortal): Multi-site multivariate downscaling of global climate model outputs: an integrated framework combining quantile mapping; stochastic weather generator and Empirical Copula approaches

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DOI	10.1007/s00382-018-4480-0
	Multi-site multivariate downscaling of global climate model outputs: an integrated framework combining quantile mapping; stochastic weather generator and Empirical Copula approaches
	Li X.; Babovic V.
发表日期	2019
ISSN	0930-7575
起始页码	5775
结束页码	5799
卷号	52 期号:2020-09-10
英文摘要	A distributed modeling of hydrological impact under climate change relies on climate scenarios for multiple climate variables at multiple locations across the catchment. The site-specific or variable-specific downscaling methods only produce climate change scenarios for a specific site or specific variable, which is inadequate to drive distributed hydrological models to investigate the spatio-temporal variability of climate change impacts at the catchment scale. This study proposes an integrated framework combining quantile mapping (QM), stochastic weather generator (WG) and Empirical Copula (EC) approaches for multi-site multivariate downscaling of global climate model outputs from monthly, grid-scale to daily, station-specific scale. In this hybrid scheme, the QM method is used to spatially downscale the monthly large-scale climate model outputs; then a stochastic WG is used to temporally downscale the monthly data to daily data by adjusting the WG parameters according to the predicted changes from large-scale climate models; at last, the observed inter-site and inter-variable dependencies, the temporal persistence, as well as the inter-annual variability are restored using the EC approach. An application of the proposed methodology is presented for statistical downscaling of the monthly precipitation, maximum and minimum temperatures from historical simulations of two Earth System Models (ESMs) to eleven weather stations over Daqing river basin in north China. The proposed methodologies are calibrated during the period 1957–1986 and evaluated in the period 1987–2016. The results show that the proposed downscaling approach is able to reconstruct the marginally distributional statistics, inter-site and inter-variable dependencies, and temporal persistence in the downscaled data for the validation period. A Limitation is also noted, such as a possible misrepresentation of the dependence structure and inter-annual variability under a non-stationary climate condition. The proposed methodologies are useful for downscaling ensembles of large-scale climate model simulations and projections for distributed hydrological impact studies. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.
英文关键词	Empirical Copula; Global climate models; Multi-site multivariate downscaling; Quantile mapping; Stochastic weather generator
语种	英语
scopus关键词	climate change; climate effect; climate modeling; downscaling; global climate; mapping; multivariate analysis; China; Daqing River; Yunnan
来源期刊	Climate Dynamics
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/146330
作者单位	Department of Civil and Environmental Engineering, National University of Singapore, Singapore, Singapore
推荐引用方式 GB/T 7714	Li X.,Babovic V.. Multi-site multivariate downscaling of global climate model outputs: an integrated framework combining quantile mapping; stochastic weather generator and Empirical Copula approaches[J],2019,52(2020-09-10).
APA	Li X.,&Babovic V..(2019).Multi-site multivariate downscaling of global climate model outputs: an integrated framework combining quantile mapping; stochastic weather generator and Empirical Copula approaches.Climate Dynamics,52(2020-09-10).
MLA	Li X.,et al."Multi-site multivariate downscaling of global climate model outputs: an integrated framework combining quantile mapping; stochastic weather generator and Empirical Copula approaches".Climate Dynamics 52.2020-09-10(2019).