responsesofriverrunofftoclimatechangebasedonnonlinearmixedregressionmodelinchaoheriverbasinofhebeiprovincechina

	responsesofriverrunofftoclimatechangebasedonnonlinearmixedregressionmodelinchaoheriverbasinofhebeiprovincechina
	Jiang Yan 2; Liu Changming 2; Zheng Hongxing 3; Li Xuyong 4; Wu Xianing 1
刊名	chinesegeographicalscience
	2010
卷号	20 期号:2 页码:152
ISSN号	1002-0063
英文摘要	Taking the nonlinear nature of runoff system into account, and combining auto-regression method and multi-regression method, a Nonlinear Mixed Regression Model (NMR) was established to analyze the impact of temperature and precipitation changes on annual river runoff process. The model was calibrated and verified by using BP neural network with observed meteorological and runoff data from Daiying Hydrological Station in the Chaohe River of Hebei Province in 1956-2000. Compared with auto-regression model, linear multi-regression model and linear mixed regression model, NMR can improve forecasting precision remarkably. Therefore, the simulation of climate change scenarios was carried out by NMR. The results show that the nonlinear mixed regression model can simulate annual river runoff well.
语种	英语
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/127341]
专题	中国科学院地理科学与资源研究所
作者单位	1.Sinohydro Corporation Limited 2.College of Water Sciences,Beijing Normal University 3.Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences 4.中国科学院生态环境研究中心
推荐引用方式 GB/T 7714	Jiang Yan,Liu Changming,Zheng Hongxing,et al. responsesofriverrunofftoclimatechangebasedonnonlinearmixedregressionmodelinchaoheriverbasinofhebeiprovincechina[J]. chinesegeographicalscience,2010,20(2):152.
APA	Jiang Yan,Liu Changming,Zheng Hongxing,Li Xuyong,&Wu Xianing.(2010).responsesofriverrunofftoclimatechangebasedonnonlinearmixedregressionmodelinchaoheriverbasinofhebeiprovincechina.chinesegeographicalscience,20(2),152.
MLA	Jiang Yan,et al."responsesofriverrunofftoclimatechangebasedonnonlinearmixedregressionmodelinchaoheriverbasinofhebeiprovincechina".chinesegeographicalscience 20.2(2010):152.