The Seasonal Rainfall Forecast in Nanning City in 2019 with the Method of Trend Comparison Ratio (TCR)
- 1 Institute of Mountain Hazards and Environment, CAS, Chengdu, China
- 2 Department of the Hydrology and Water Resources Engineering, College of Environmental Science and Engineering, Guilin University of Technology, Guilin, China
- 3 Institute of Mountain Hazards and Environment, CAS, Chengdu, China
Abstract
In this paper, the monthly rainfall statistical data of Nanning City, Capital of Guangxi Zhuang Autonomous Region, China, from 2006 to 2018, were col lected. On the basis of qualitative analysis of the rainfall seasonal changing law, the non-linear seasonal rainfall forecast model on Nanning City with the method of Trend Comparison Ratio (TCR) was established by the statistical analysis software Office Excel 2013. The model was used to predict the rainfall in spring, summer, autumn and winter in Nanning in 2019. The results were: 286.41 mm, 695.79 mm, 292.20 mm and 118.11 mm, respectively. It was also found that the predicted results were consistent with the seasonal distribution cha racteristics, annual distribution characteristics and the trend of historica l rainfall time series fluctuation, through the qualitative analysis of figures. Compared with the actual measured rainfall data of Nanning City in 2019 in the China Statistical Yearbook (2020), the predicted values are basically consistent with the measured values.
- Xue, Y. and Chen, L.P. (2020) Time Series Analysis and R Software. Tsinghua University Press, Beijing. https://doi.org/10.14293/S2199-1006.1.SOR-UNCAT.CL8ASMM.v1
- Kocenda, E. and Cerny, A. (2018) Time Series Analysis: Method and Application. Chemical Industry Press, Beijing.
- Sang, Y.F., Wang, Z.G. and Liu, C.M. (2013) Research Progress on the Time Series Analysis Methods in Hydrology. Progress in Geography, 32, 20-30.
- Zhang, S.W. and Li, Z.F. (1996) Application of Time Series Analysis in Forecasting Annual Precipitation. Journal of Water Resources Research, 17, 7-11.
- Bai, Y.J. (2011) Application of Rainfall Forecasting Based on Improved Time Series Model. Computer Simulation, 28, 141-145.
- Sang, Y.F., Wang, Z.G. and Liu, C.M. (2013) Applications of Wavelet Analysis to Hydrology: Status and Prospects. Progress in Geography, 32, 1413-1422.
- Bender, M. and Simonivic, S. (1992) Time Series Modeling for Long-Range Streamflow Forecasting. Journal of Water Resources Planning and Management, 118, 857-869. https://doi.org/10.1061/(ASCE)0733-9496(1994)120:6(857)
- Jiang, X.R. (2019) Seasonal Prediction Modeling and Its Application in Short-Term Macroeconomic Prediction. Statistics and Decision, 16, 75-78.
- Zhang, Y. (2013) Theoretical Analysis and Application of Structural Time Series Model in Seasonal Adjustment. Nankai University, Tianjing.
- Lin, Z.D., Chen, X.W., Lin, M.S., et al. (2017) Spatial and Temporal Variations of Storm-Floods in Xixi Watershed of Southeast Coastal Region. Mountain Research, 35, 488-495.
- Pattanaik, D.R., Mukhopadhyay, B. and Kumar, A. (2012) Monthly Forecast of Indian Southwest Monsoon Rainfall Based on NCEP’s Coupled Forecast System. Atmospheric and Climate Sciences, 2, 479-491. https://doi.org/10.4236/acs.2012.24042
- Zhao, G.C., Zhao, P.F. and Wang, G.Y. (2019) Spatial and Temporal Distribution Characteristics and Prediction Model of Rainfall in Tianjin. Haihe Water Resources, 4, 41-43.
- Shi, N. and Chen, L.W. (2002) Long Term Variation of Global Land Annual Precipitation (from 1948-2000). Chinese Science Bulletin, 47, 1671-1674.
- Shen, X.Z., Guan, X. and Niu, Y.J. (2020) Time Series Analysis and Forecast of Rainfall Based on IDRISI. Shanxi Forestry Science and Technology, 49, 24-26.