Jiang Yan1; Liu Changming1; Zheng Hongxing2; Li Xuyong3; Wu Xianing4
Source Publicationchinesegeographicalscience
AbstractTaking 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.
Document Type期刊论文
Affiliation1.College of Water Sciences,Beijing Normal University
2.Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences
4.Sinohydro Corporation Limited
Recommended Citation
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.
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