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.
Similar content being viewed by others
References
Arnell N W, Reynard N S, 1996. The effects of climate change due to global warming on river flows in Great Britain. Journal of Hydrology, 183(3–4): 397–424. DOI: 10.1016/0022-1694(95)02950-8
Cao Yongqiang, You Hailin, Xing Xiaosen et al., 2009. Multiple regression runoff forecasting model based on logistic equation and its application. Water Power, (6): 12–14. (in Chinese)
Chen Yaning, Xu Changchun, Hao Xingming et al., 2009. Fifty-year climate change and its effect on annual runoff in the Tarim River Basin, China. Quaternary International, 208(1–2):53–61. DOI: 10.1016/j.quaint.2008.11.011
David L, Yves G, Jean L P et al., 2004. Evidence for global runoff increase related to climate warming. Advances in Water Resources, 27(6): 631–642. DOI: 10.1016/j.advwatres.2004.02.0-20
Fujihara Y, Tanaka K, Watanabe T et al., 2008. Assessing the impacts of climate change on the water resources of the Seyhan River Basin in Turkey: Use of dynamically downscaled data for hydrologic simulations. Journal of Hydrology, 353(1–2): 33–48. DOI: 10.1016/j.jhydrol.2008.01.024
Gao S, Wang J, Xiong L et al., 2002. A macro-scale and semi-distributed monthly water balance model to predict climate change impacts in China. Journal of Hydrology, 268(1): 1–15. DOI: 10.1016/S0022-1694(02)00075-6
He Xilin, Dong Xianjun, Zhou Jianwei, 1998. A grey forecasting method of mid-long term spring runoff forecasting in Manas River. Journal of Shihezi University (Natural Science), 2(3):227–230. (in Chinese)
Hong Shizhong, 1999. Nonlinear time series analysis and its application to geoscience. Advance in Earth Sciences, 14(6):559–565. (in Chinese)
Jiang Xiaohui, Liu Changming, Liu Yu et al., 2004. Mix regression model application for forecast annual flow in Yellow River under duality model. Science in China (Ser. E), 34(supp.):95–102. (in Chinese)
Jonathan I M, Graciana P, Kenneth M M, 2004. Evaluation of the impact of climate change on hydrology and water resources in Swaziland: Part II. Physics and Chemistry of the Earth, 29(15–18): 1193–1202. DOI: 10.1016/j.pce.2004.09.035
Li Kerang, Chen Yufeng, 1999. The progress in methodologies to study impacts of global climate change in China. Geographical Research, 18(2): 214–219. (in Chinese)
Liu Changming, 2004. Study of some problems in water cycle changes of the Yellow River basin. Advances in Water Science, 15(5): 608–614. (in Chinese)
Liu Chunzhen, 2004. The issue in the study of climate change on the terrestrial hydrological cycle. Advances in Earth Science, 19(1): 115–119. (in Chinese)
Maria H, Martin F, Michael S et al., 2006. An application of artificial neural networks to carbon, nitrogen and phosphorus concentrations in three boreal streams and impacts of climate change. Ecological Modelling, 195(1–2): 51–60. DOI: 10.101-6/j.ecolmodel.2005.11.009
Roger N J, Francis H S, Walter C et al., 2006. Estimating the sensitivity of mean annual runoff to climate change using selected hydrological models. Advances in Water Resources, 29(10): 1419–1429. DOI: 10.1016/j.advwatres.2005.11.001
Science U N A O, 1977. Climate Change and Water Supply. Washington D C: National Academy Press.
Susan S D, Peter L, Ray M et al., 2008. The impacts of climate change on hydrology in Ireland. Journal of Hydrology, 356(1–2): 28–45. DOI: 10.1016/j.jhydrol.2008.03.025
Vivek K A, 2002. The use of the aridity index to assess climate change effect on annual runoff. Journal of Hydrology, 265(1–4): 164–177. DOI: 10.1016/S0022-1694(02)00101-4
Wang Ling, Huang Guoru, 2003. Application of modified BP model to basin daily-runoff forecasting. Water Resources and Power, 21(1): 32–34. (in Chinese)
Wang Shunjiu, 2006. Impacts of global climate change on hydrology and water resources. Advances in Climate Change, 2(5): 223–227. (in Chinese)
Wang Xiujie, Lian Jijian, Fei Shouming et al., 2007. Chaotic multivariate autoregressive model of daily runoff prediction based on wavelet de-noising. Journal of System Simulation, 19(15): 3605–3608. (in Chinese)
Wang Yimin, Yu Xingjie, Chang Jianxia, 2008. Prediction of runoff based on BP neural network and Markov model. Engineering Journal of Wuhan University, 41(5): 14–18. (in Chinese)
Wang Zhenglong, Hu Yonghong, 2007. Applied Time Series Analysis. Beijing: Science Press. (in Chinese)
Yu Guorong, Ye Hui, Xia Ziqiang et al., 2009. Application of projection pursuit auto-regressionmodel in predicting runof of Yangtze River. Journal of Hohai University (Natural Sciences), 37(3): 263–266. (in Chinese)
Author information
Authors and Affiliations
Corresponding author
Additional information
Foundation item: Under the auspices of National Natural Science Foundation of China (No. 50809004)
Rights and permissions
About this article
Cite this article
Jiang, Y., Liu, C., Zheng, H. et al. Responses of river runoff to climate change based on nonlinear mixed regression model in Chaohe River Basin of Hebei Province, China. Chin. Geogr. Sci. 20, 152–158 (2010). https://doi.org/10.1007/s11769-010-0152-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11769-010-0152-7