中国地理科学(英文版) ›› 2010, Vol. 20 ›› Issue (2): 152-158.doi: 10.1007/s11769-010-0152-7

• 论文 • 上一篇    下一篇

Responses of River Runoff to Climate Change Based on Nonlinear Mixed Regression Model in Chaohe River Basin of Hebei Province, China

JIANG Yan1,2, LIU Changming2,3, ZHENG Hongxing3, LI Xuyong1, WU Xianing4   

  1. 1. State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China;
    2. College of Water Sciences, Beijing Normal University, Beijing 100875, China;
    3. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
    4. Sinohydro Corporation Limited, Beijing 100044, China
  • 收稿日期:2009-03-09 修回日期:2009-10-09 出版日期:2010-01-22 发布日期:2010-04-23
  • 通讯作者: JIANG Yan.E-mail:lirenjy@sohu.com;yanjiang@rcees.ac.cn E-mail:lirenjy@sohu.com;yanjiang@rcees.ac.cn
  • 基金资助:

    Under the auspices of National Natural Science Foundation of China (No. 50809004)

Responses of River Runoff to Climate Change Based on Nonlinear Mixed Regression Model in Chaohe River Basin of Hebei Province, China

JIANG Yan1,2, LIU Changming2,3, ZHENG Hongxing3, LI Xuyong1, WU Xianing4   

  1. 1. State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China;
    2. College of Water Sciences, Beijing Normal University, Beijing 100875, China;
    3. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
    4. Sinohydro Corporation Limited, Beijing 100044, China
  • Received:2009-03-09 Revised:2009-10-09 Online:2010-01-22 Published:2010-04-23
  • Supported by:

    Under the auspices of National Natural Science Foundation of China (No. 50809004)

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.

关键词: river runoff, runoff forecast, nonlinear mixed regression model, linear multi-regression model, linear mixed regression model, BP neural network

Abstract:

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.

Key words: river runoff, runoff forecast, nonlinear mixed regression model, linear multi-regression model, linear mixed regression model, BP neural network