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Mechanism and forecasting methods for severe droughts and floods in Songhua River Basin in China

  • Hongyan LiEmail author
  • Yuxin Wang
  • Xiubin Li
Article

Abstract

The influence of various factors, mechanisms, and principles affecting runoff are summarized as periodic law, random law, and basin-wide law. Periodic law is restricted by astronomical factors, random law is restricted by atmospheric circulation, and basin-wide law is restricted by underlying surface. The commensurability method was used to identify the almost period law, the wave method was applied to deducing the random law, and the precursor method was applied in order to forecast runoff magnitude for the current year. These three methods can be used to assess each other and to forecast runoff. The system can also be applied to forecasting wet years, normal years and dry years for a particular year as well as forecasting years when floods with similar characteristics of previous floods, can be expected. Based on hydrological climate data of Baishan (1933–2009) and Nierji (1886–2009) in the Songhua River Basin, the forecasting results for 2010 show that it was a wet year in the Baishan Reservoir, similar to the year of 1995; it was a secondary dry year in the Nierji Reservoir, similar to the year of 1980. The actual water inflow into the Baishan Reservoir was 1.178 × 1010 m3 in 2010, which was markedly higher than average inflows, ranking as the second highest in history since records began. The actual water inflow at the Nierji station in 2010 was 9.96 × 109 m3, which was lower than the average over a period of many years. These results indicate a preliminary conclusion that the methods proposed in this paper have been proved to be reasonable and reliable, which will encourage the application of the chief reporter release system for each basin. This system was also used to forecast inflows for 2011, indicating a secondary wet year for the Baishan Reservoir in 2011, similar to that experienced in 1991. A secondary wet year was also forecast for the Nierji station in 2011, similar to that experienced during 1983. According to the nature of influencing factors, mechanisms and forecasting methods and the service objects, mid- to long-term hydrological forecasting can be divided into two classes: mid- to long-term runoff forecasting, and severe floods and droughts forecasting. The former can be applied to quantitative forecasting of runoff, which has important applications for water release schedules. The latter, i.e., qualitative disaster forecasting, is important for flood control and drought relief. Practical methods for forecasting severe droughts and floods are discussed in this paper.

Keywords

Songhua River Basin runoff drought and flood forecasting 

References

  1. Amilcare P, Luca R, 1997. Nonlinear analysis of river flow time sequences. Water Resources Research, 33(6): 1353–1367.CrossRefGoogle Scholar
  2. Bai Renhai, Li Shuai, 2001. Analysis of precipitation and rainstorm over Nenjiang River and Songhuajiang River Valley. Heilongjiang Meteorology, (3): 1–4. (in Chinese)Google Scholar
  3. Box G E P, Jenkins G M, Reinsel G C, 1976. Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day.Google Scholar
  4. Breaford P W, Seyfried M S, Matison T H, 1991. Searching for chaotic dynamic in snowmelt runoff. Water Resources Research, 27(6): 1005–1010.CrossRefGoogle Scholar
  5. Cao Hongxing, He Jifei, Lu Yuehua et al., 1982. The Physical Basis of Climate and Climate Modeling. Beijing: Science Press, 26–27. (in Chinese)Google Scholar
  6. Cao Yongqiang, You Hailin, Xing Xiaosen et al., 2009. Multiple regression runoff forecasting model based on logistic equation and its application. Water Power, 35(6): 12–14. (in Chinese)Google Scholar
  7. Chen Yiping, Li Xiaoniu, 1996. Application of grey system theory in water conservancy and its prospect. Pearl River, (1): 25–27. (in Chinese)Google Scholar
  8. Fan Chuiren, Xia Jun, Zhang Liping et al., 2008. Long-term Forecast of Flood and Drought Disasters in China: Theory. Methods. Practice. Beijing: China Water Power Press. (in Chinese)Google Scholar
  9. Fan Zhongxiu, 1999. Mid and Long Term Hydrological Forecast. Nanjing: Hohai University Press. (in Chinese)Google Scholar
  10. Friis-Christensen E, Lassen K, 1991. Length of the solar cycle: An indicator of solar activity closely associated with climate. Science, 254: 698–700.CrossRefGoogle Scholar
  11. Geophysical statistical forecasting group of Peking University, 1973. The 11-year period of sunspot and climate forecast of myriametric wave. Meteorological Science and Technology Information, 3: 31–36. (in Chinese)Google Scholar
  12. Han Min, 2007. The Theory and Methods of Chaotic Time Series Prediction. Beijing: China Water Power Press. (in Chinese)Google Scholar
  13. Hsu K, Gupta H V, Sorroshian S, 1995. Artificial neural network modeling of the rainfall-runoff process. Water Resource Research, 31(10): 2517–2530.CrossRefGoogle Scholar
  14. Hu Tiesong, Yuan Peng, Ding Jing, 1995. Applications of artificial neural network to hydrology and water resources. Advances in Water Science, 6(1): 76–82. (in Chinese)Google Scholar
  15. Huang W, Xu B, Chan-Hilton A, 2004. Forecasting flows in Apalachicola River using neural networks. Hydrological Processes, 18(13): 2545–2564. doi: 10.1002/hyp.149CrossRefGoogle Scholar
  16. Huang Zhongshu, Wang Qinliang, Kuang Qi, 1985. Primary study on the relationship between thermal conditions of the Qinghai-Tibet Plateau and the North Pacific and drought and flood of the Yangtze River in flood season. In: Yangtze Valley Planning Office (ed.). Selected Papers of Hydrologic Forecast (Nationwide Symposium on Hydrologic Forecast 1981). Beijing: China Power Press, 180–187. (in Chinese)Google Scholar
  17. Huang Zhongshu, 1979. Preliminary analysis of relationship between thermal conditions of the Qinghai-Tibet Plateau and drought and flood of the Yangtze River. Yangtze River, (2): 38–46. (in Chinese)Google Scholar
  18. Huang Zhongshu, 1986. Relation between thermal conditions of the Qinghai-Tibet Plateau and atmospheric myriametric wave. Geographical Research, 5(1): 32–41. (in Chinese)Google Scholar
  19. Huang Zhongshu, Jin Xingping, 2005. The Basic Theory and Applied Technology of Hydroclimate Prediction, the Books of Large and Medium-sized Hydro Project Technology of the Yangtze River Water Conservancy Commission. Beijing: China Water Power Press. (in Chinese)Google Scholar
  20. Jayawardena A W, Feizhou L, 1993. Chaos in hydrological time series. International Association of Hydrological Science Publish, (213): 59–66.Google Scholar
  21. Jayawardena A W, Feizhou L, 1994. Analysis and prediction of chaos in rainfall and stream flow time series. Journal of Hydrology, (753): 23–52.Google Scholar
  22. Li Shuai, Bai Renhai, Chen Li, 2002. Analysis of summer precipitation and water level variation over Nenjiang River and Songhuajiang River Valley. Heilongjiang Meteorology, (3): 7–11. (in Chinese)Google Scholar
  23. Li Xianbin, Ding Jing, Li Houqiang, 1999. The combination forecasting using artificial neural network based on wavelet transformed sequences. Journal of Hydraulic Engineering, (2): 1–4. (in Chinese)Google Scholar
  24. Li Yawei, Chen Shouyu, Han Xiaojun, 2006. Yellow river ice flood prediction based on SVR. Journal of Dalian University of Technology, 46(2): 272–275. (in Chinese)Google Scholar
  25. Lin Jianyi, Cheng Chuntian, 2006. Application of support vector machine method to long-term runoff forecast. Journal of Hydraulic Engineering, 37(6): 681–686. (in Chinese)Google Scholar
  26. Lu Jiong, 1950. Sea temperature and flood and drought problem. Acta Meteorologica Sinica, 21(1–4): 1–19. (in Chinese)Google Scholar
  27. Lu Jiong, 1951. Northwest pacific and its problem of East Asia climate. Acta Geographica Sinica, 18(1–2): 69–88. (in Chinese)Google Scholar
  28. Marlyn L S, 2008. Hydroclimatology Perspectives and Applications. England: Cambridge University Press.Google Scholar
  29. Peng Gongbing, Si Youyuan, Lu Wei, 1982. Application of geophysical factors in long term weather forecast. Chinese Science Bulletin, 12: 752–755. (in Chinese)Google Scholar
  30. Reid G C, 1987. Influence of solar variability on global sea surface temperatures. Nature, 329: 142–143.CrossRefGoogle Scholar
  31. Shozo T, Hiroshi M, Akio M et al., 1997. Forecasting of time series with fractal geometry by using scale transformations and parameters estimation obtained by the wavelet transform. Elec tronics and Communications in Japan, 80(8): 20–30.CrossRefGoogle Scholar
  32. Si Gongwang, Zhou Qinghua, Yao Lirong, 1974. Relationship between climatic fluctuation of rainfall in middle and lower reaches of the Yangtze River at plum rain season and atmospheric circulation. Meteorological Science and Technology, (5): 10–15. (in Chinese)Google Scholar
  33. Sivakumar B, Phoon K K, Liong S Y et al., 1999. Comment on ‘Nonlinear analysis of river flow time sequences’ by Amilcare Porporato and Luca Ridolfi. Water Resources Research, 35(3): 895–897.CrossRefGoogle Scholar
  34. Tang Chengyou, Guo Lijuan, Wang Rui, 2007. Application of prediction model for stochastic combination of stepwise regression of hydrologic time series. Water Resources and Hydropower Engineering, 38(6):1–4. (in Chinese)Google Scholar
  35. Tang Maocang, 1998. Introduction of A New Method of Natural Disasters Forecasts. Beijing: University of Science and Technology of China Press, 429–431. (in Chinese)Google Scholar
  36. Tang Maocang, Gao Xiaoqing, 1995a. Some statistic characteristics of ‘underground hot vortex’ in China during 1980–1993 (I)—Spatial temporal distribution of underground hot vortex. Science in China (Series B), 25(11): 1186–1192. (in Chinese)Google Scholar
  37. Tang Maocang, Gao Xiaoqing, 1995b. Some statistic characteristics of ‘underground hot vortex’ in China during 1980–1993 (II)—Statistic correlation between ‘underground hot vortex’ and earthquake. Science in China (Series B), 25(12): 1313–1319. (in Chinese)Google Scholar
  38. Tang Maocang, Li Dongliang, Zhang Yongjun, 2004. How to succeed in the short-term climate prediction. Plateau Meteorology, 23(5): 714–717. (in Chinese)Google Scholar
  39. Tang M C, Li T S, Zhang J et al., 1989. The operational forecasting total precipitation in flood season (Apr.–Sept.) of 5 years (1983–1987). Advances in Atmospheric Sciences, 6(3): 289–300.CrossRefGoogle Scholar
  40. Tang Maocang, Zhao Zhenguo, Ma Zhuguo, 1997. A summary of precipitation prediction in flood-season by using the method of underground information during the recent ten years (1985–1994). Climatic and Environmental Research, 2(1): 55–60. (in Chinese)Google Scholar
  41. Tang Qicheng, Xiong Yi, 1998. Chinese River Hydrology. Beijing: Science Press. (in Chinese)Google Scholar
  42. Tao Shiyan, Zhao Yujia, Chen Xiaomin, 1958. The relationship between mei-yu period of East Asia and seasonal variations of atmospheric circulation over Asia. Acta Meteorologica Sinica, 29(2): 119–134. (in Chinese)Google Scholar
  43. Wang Bende, 1992. The Fuzzy Mathematical Method in Mid-long Term Hydrologic Forecasting. Dalian: Dalian University of Technology Press. (in Chinese)Google Scholar
  44. Wang Junde, 1993. Hydrological Statistics. Beijing: Hydraulic and Electric Power Press, 236–295. (in Chinese)Google Scholar
  45. Wang Lei, 1978. Sunspot and climatological forecast. Meteorological Monthly, (7): 26–27. (in Chinese)Google Scholar
  46. Wang Liang, Zhang Hongwei, Niu Zhiguang, 2005. Application of support vector machines in short-term prediction of urban water consumption. Journal of Tianjin University, 38(11): 1021–1025. (in Chinese)Google Scholar
  47. Wang Shaowu, 1973. Research on relationship between atmospheric circulation and climatic anomaly and its prospect. Meteorological Science and Technology, 3: 24–30. (in Chinese)Google Scholar
  48. Weng Duming, 1963. The action of solar radiation on the air temperature annual march in China. Acta Geographica Sinica, 29(2): 145–155. (in Chinese)Google Scholar
  49. Weng Wenbo, 1984. Fundamentals of Forecasting. Beijing: Petroleum Industry Press. (in Chinese)Google Scholar
  50. Weng Wenbo, Lu Niudun, Zhang Qing, 1996. Theory of Forecasting. Beijing: Petroleum Industry Press. (in Chinese)Google Scholar
  51. Xia Jun, 1991. Study on a method of commensurable information forecasting of hydrologic disastrous events. Journal of Wuhan University of Hydraulic and Electrical Engineering, 24(3): 288–295. (in Chinese)Google Scholar
  52. Xia Jun, 1993. A grey correlative analysis and pattern recognition applied to mid-long term runoff forecasting. Advances in Water Science, 4(3): 190–197. (in Chinese)Google Scholar
  53. Xin Jianshi, 1985. A summary of precipitation forecast in flood season over ten years (1975–1984). Plateau Meteorology, 4(4): 372–381. (in Chinese)Google Scholar
  54. Xiong Dishu, 1991. Weather Lore of China. Beijing: China Meteorological Press, 571. (in Chinese)Google Scholar
  55. Xu Daoyi, 2010. Commensurability prediction methods of Weng Wenbo and its meaning. In: Gao Jianguo et al. (eds.). The Integration of Disaster Forecasting Methods. Beijing: China Meteorological, 135–139. (in Chinese)Google Scholar
  56. Ye Duzheng, 1952. Seasonal variation due to influence of Tibet Plateau on atmospheric circulation. Acta Meteorologica Sinica, 23(1–2): 33–47. (in Chinese)Google Scholar
  57. Ye Duzheng, Gu Zhenchao, 1955. Influence of Tibet Plateau on atmospheric circulation in East Asia and weather in China. Chinese Science Bulletin, (6): 29–33. (in Chinese)Google Scholar
  58. Ye Duzheng, Luo Siwei, Zhu Baozhen, 1957. The wind structure and heat balance in the lower troposphere over Tibetan Plateau and its surrounding. Chinese Science Bulletin, (3): 116–117. (in Chinese)Google Scholar
  59. Zhang Zhiming, Fan Zhongxiu, 1996. Meteorology and Climatology. Beijing: China Water Power Press. (in Chinese)Google Scholar
  60. Zhao Yonglong, Ding Jing, Deng Yuren, 1998. Wavelet network model of phase space and its application in hydrologic prediction. Advances in Water Science, 9(3): 252–287. (in Chinese)Google Scholar

Copyright information

© Science Press, Northeast Institute of Geography and Agricultural Ecology, CAS and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Key Laboratory of Groundwater Resources and Environment, Ministry of EducationJilin UniversityChangchunChina
  2. 2.Baihan Power Plant of State Grid Xin Yuan Company LimitedHuadianChina

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