Theoretical and Applied Climatology

, Volume 132, Issue 1–2, pp 479–489 | Cite as

A statistical forecast model using the time-scale decomposition technique to predict rainfall during flood period over the middle and lower reaches of the Yangtze River Valley

  • Yijia Hu
  • Zhong Zhong
  • Yimin Zhu
  • Yao Ha
Original Paper


In this paper, a statistical forecast model using the time-scale decomposition method is established to do the seasonal prediction of the rainfall during flood period (FPR) over the middle and lower reaches of the Yangtze River Valley (MLYRV). This method decomposites the rainfall over the MLYRV into three time-scale components, namely, the interannual component with the period less than 8 years, the interdecadal component with the period from 8 to 30 years, and the interdecadal component with the period larger than 30 years. Then, the predictors are selected for the three time-scale components of FPR through the correlation analysis. At last, a statistical forecast model is established using the multiple linear regression technique to predict the three time-scale components of the FPR, respectively. The results show that this forecast model can capture the interannual and interdecadal variation of FPR. The hindcast of FPR during 14 years from 2001 to 2014 shows that the FPR can be predicted successfully in 11 out of the 14 years. This forecast model performs better than the model using traditional scheme without time-scale decomposition. Therefore, the statistical forecast model using the time-scale decomposition technique has good skills and application value in the operational prediction of FPR over the MLYRV.



This work is sponsored by the National Natural Science Foundation of China (grants 41675077, 41205075, 41430426, and 41330420), the R&D Special Fund for Public Welfare Industry (Meterology) (GYHY201306025), and the National Public Welfare Research Found of China (GYHY201106017).


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Copyright information

© Springer-Verlag Wien 2017

Authors and Affiliations

  1. 1.College of Meteorology and OceanographyPLA University of Science and TechnologyNanjingChina
  2. 2.School of Atmospheric SciencesNanjing UniversityNanjingChina

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