气候变化领域集成服务门户(CCPortal): Evaluation of data-driven models to downscale rainfall parameters from global climate models outputs: The case study of latyan watershed

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DOI	10.2166/wcc.2018.191
	Evaluation of data-driven models to downscale rainfall parameters from global climate models outputs: The case study of latyan watershed
	Hosseini R.H.; Golian S.; Yazdi J.
发表日期	2020
ISSN	20402244
起始页码	200
结束页码	216
卷号	11 期号:1
英文摘要	Assessment of climate change in future periods is considered necessary, especially with regard to probable changes to water resources. One of the methods for estimating climate change is the use of the simulation outputs of general circulation models (GCMs). However, due to the low resolution of these models, they are not applicable to regional and local studies and downscaling methods should be applied. The purpose of the present study was to use GCM models’ outputs for downscaling precipitation measurements at Amameh station in Latyan dam basin. For this purpose, the observation data from the Amameh station during the 1980–2005 period, 26 output variables from two GCM models, namely, HadCM3 and CanESM2 were used. Downscaling was performed by three data-driven methods, namely, artificial neural network (ANN), nonparametric K-nearest neighborhood (KNN) method, and adaptive network-based fuzzy inference system method (ANFIS). Comparison of the monthly results showed the superiority of KNN compared to the other two methods in simulating precipitation. However, all three, ANN, KNN, and ANFIS methods, showed satisfactory results for both HadDCM3 and CanESM2 GCM models in downscaling precipitation in the study area. © IWA Publishing 2020.
英文关键词	Artificial intelligence; CanESM2; Climate change; Downscaling; GCM; HadCM3
语种	英语
scopus关键词	Climate change; Fuzzy inference; Fuzzy neural networks; Nearest neighbor search; Water resources; Adaptive network based fuzzy inference system; Data-driven methods; Downscaling methods; General circulation model; Global climate model; K-nearest neighborhoods; Precipitation measurement; Simulation outputs; Climate models; artificial neural network; climate modeling; downscaling; fuzzy mathematics; general circulation model; global climate; nearest neighbor analysis; numerical model; precipitation assessment; rainfall; watershed; Iran; Latian Dam; Tehran [Iran]
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/147953
作者单位	Department of Civil Engineering, Shahrood University of Technology, Shahrood, Iran; Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran
推荐引用方式 GB/T 7714	Hosseini R.H.,Golian S.,Yazdi J.. Evaluation of data-driven models to downscale rainfall parameters from global climate models outputs: The case study of latyan watershed[J],2020,11(1).
APA	Hosseini R.H.,Golian S.,&Yazdi J..(2020).Evaluation of data-driven models to downscale rainfall parameters from global climate models outputs: The case study of latyan watershed.Journal of Water and Climate Change,11(1).
MLA	Hosseini R.H.,et al."Evaluation of data-driven models to downscale rainfall parameters from global climate models outputs: The case study of latyan watershed".Journal of Water and Climate Change 11.1(2020).