气候变化领域集成服务门户(CCPortal): Drought frequency projection using regional climate scenarios reconstructed by seasonal artificial neural network model

CCPortal

DOI	10.2166/wcc.2014.130
	Drought frequency projection using regional climate scenarios reconstructed by seasonal artificial neural network model
	Lee J.H.; Moon S.J.; Kang B.S.
发表日期	2014
ISSN	20402244
起始页码	578
结束页码	592
卷号	5 期号:4
英文摘要	The climate change impacts on drought in the Korean peninsula were projected using Global Climate Model (GCM) output reconstructed regionally by an artificial neural network (ANN) model. The reconstructed model outputs were subsequently used as an input to project drought severity evaluated by Standard Precipitation Index (SPI). The original GCM output corresponds to the CGCM3.1/T63 under the 20C3M reference scenario and the IPCC A1B, A2 and B1 projection scenarios. Because in general GCM shows limitation in capturing typhoon generation occurred at sub-grid scale, the training and validation of the ANN model utilized a precipitation data set with typhoon-generated rainfall eliminated for enhancing the ANN’s computational performance. The non-stationarity characteristics of SPI was examined using the Mann–Kendall test. The projection was implemented for the near future period (2011–2040), mid-term (2041–2070) and long-term (2071–2100) future periods. The results indicated mitigated drought severity under all scenarios in terms of frequency, magnitude and drought spells even for the mildest B1 scenario. The SDF (severity-duration-frequency) curves illustrate the common patterns of alleviated drought severity for most future scenarios and elongated drought duration. The reconstructed GCM projection recovers the underestimated precipitation and provided more realistic drought projection even though there would be still uncertainties of spatial and temporal variability. © 2014, IWA Publishing. All rights reserved.
英文关键词	Artificial neural network; Climate change; Drought projection; SPI; Statistical downscaling
语种	英语
scopus关键词	Climate models; Drought; Hurricanes; Neural networks; Artificial neural network modeling; Artificial neural network models; Climate change impact; Computational performance; Global climate model; Spatial and temporal variability; Standard precipitation indices; Statistical downscaling; Climate change
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/148248
作者单位	Department of Civil Engineering, Joongbu University, Kumsan-gun, Chungnam, South Korea
推荐引用方式 GB/T 7714	Lee J.H.,Moon S.J.,Kang B.S.. Drought frequency projection using regional climate scenarios reconstructed by seasonal artificial neural network model[J],2014,5(4).
APA	Lee J.H.,Moon S.J.,&Kang B.S..(2014).Drought frequency projection using regional climate scenarios reconstructed by seasonal artificial neural network model.Journal of Water and Climate Change,5(4).
MLA	Lee J.H.,et al."Drought frequency projection using regional climate scenarios reconstructed by seasonal artificial neural network model".Journal of Water and Climate Change 5.4(2014).