Responses of river runoff to climate change based on nonlinear mixed regression model in Chaohe River Basin of Hebei Province,China |
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| Authors: | Yan Jiang Changming Liu Hongxing Zheng Xuyong Li Xianing Wu |
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| Institution: | [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 |
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| 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. |
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| Keywords: | river runoff runoff forecast nonlinear mixed regression model linear multi-regression model linear mixed regression model BP neural network |
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