气候变化领域集成服务门户(CCPortal): Evaluation of global climate models for precipitation projection in sub-Himalaya region of Pakistan

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DOI	10.1016/j.atmosres.2020.105061
	Evaluation of global climate models for precipitation projection in sub-Himalaya region of Pakistan
	Iqbal Z.; Shahid S.; Ahmed K.; Ismail T.; Khan N.; Virk Z.T.; Johar W.
发表日期	2020
ISSN	0169-8095
卷号	245
英文摘要	The selection of global climate models (GCMs) for a region remained a difficult step in climate change studies. A state-of-the-art Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithm is proposed in this paper for GCM selection. The ranking of GCMs obtained using SVM-RFE was compared to that obtained using entropy-based similarity assessment index known as Symmetrical Uncertainty (SU). The study was conducted in the sub-Himalayan region of Pakistan where a reliable projection of climate is highly significant for water resources management in the entire western part of South Asia. The RF-based regression model was employed to generate a multi-model ensemble (MME) mean of the top-ranked GCMs. The MME mean projection was utilized to estimate the spatiotemporal changes in annual precipitation in comparison with precipitation of 1961‐–2000 for various representative concentration pathway (RCP) scenarios. The SVM-RF selected five GCMs (MIROC5, EC-EARTH, CNRM-CM5, BCC-CSM1.1(m) and BCC-CSM1.1) as most suitable for climate change projections in the study area. Obtained results were found to collaborate well with the results of multiple conventional statistical metrics. The MME mean projections revealed precipitation alteration between −1% and 18% during 2020‐–2059, and 0 and 24% during 2060–2099 for different RCPs. Precipitation was projected to increase up to 20% in the north whereas a decrease up-to −16% in the south. © 2020 Elsevier B.V.
英文关键词	Climate change modelling; Recursive feature elimination; Sub-Himalayan region; Support vector machine; Symmetrical Uncertainty
语种	英语
scopus关键词	Climate change; Earth (planet); Regression analysis; Support vector machines; Water resources; Annual precipitation; Climate change projections; Global climate model; Multi-model ensemble; Similarity assessment; Spatio-temporal changes; Support vector machine recursive feature eliminations; Water resources management; Climate models; algorithm; annual variation; climate modeling; ensemble forecasting; precipitation assessment; precipitation intensity; spatiotemporal analysis; support vector machine; Pakistan
来源期刊	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/141840
作者单位	School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru, 81310, Malaysia; Water Program Leadership for Environment and Development (LEAD), Pakistan; Faculty of Water Resource Management, Lasbela University of Agriculture Water and Marine Sciences (LUAWMS), Uthal, Balochistan 90150, Pakistan; NUST Institute of Civil Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan
推荐引用方式 GB/T 7714	Iqbal Z.,Shahid S.,Ahmed K.,et al. Evaluation of global climate models for precipitation projection in sub-Himalaya region of Pakistan[J],2020,245.
APA	Iqbal Z..,Shahid S..,Ahmed K..,Ismail T..,Khan N..,...&Johar W..(2020).Evaluation of global climate models for precipitation projection in sub-Himalaya region of Pakistan.Atmospheric Research,245.
MLA	Iqbal Z.,et al."Evaluation of global climate models for precipitation projection in sub-Himalaya region of Pakistan".Atmospheric Research 245(2020).