Nonstationarities and At-site Probabilistic Forecasts of Seasonal Precipitation in the East River Basin, China

Peng Sun; Qiang Zhang; Xihui Gu; Peijun Shi; Vijay P. Singh; Changqing Song; Xiuyu Zhang

doi:10.1007/s13753-018-0165-x

Article Contents

Article Navigation > International Journal of Disaster Risk Science > 2018 > 9(1): 100-115

Peng Sun, Qiang Zhang, Xihui Gu, Peijun Shi, Vijay P. Singh, Changqing Song, Xiuyu Zhang. Nonstationarities and At-site Probabilistic Forecasts of Seasonal Precipitation in the East River Basin, China[J]. International Journal of Disaster Risk Science, 2018, 9(1): 100-115. doi: 10.1007/s13753-018-0165-x

Citation:

Peng Sun, Qiang Zhang, Xihui Gu, Peijun Shi, Vijay P. Singh, Changqing Song, Xiuyu Zhang. Nonstationarities and At-site Probabilistic Forecasts of Seasonal Precipitation in the East River Basin, China[J]. International Journal of Disaster Risk Science, 2018, 9(1): 100-115. doi: 10.1007/s13753-018-0165-x

Citation:

Peng Sun, Qiang Zhang, Xihui Gu, Peijun Shi, Vijay P. Singh, Changqing Song, Xiuyu Zhang. Nonstationarities and At-site Probabilistic Forecasts of Seasonal Precipitation in the East River Basin, China[J]. International Journal of Disaster Risk Science, 2018, 9(1): 100-115. doi: 10.1007/s13753-018-0165-x

PDF

Nonstationarities and At-site Probabilistic Forecasts of Seasonal Precipitation in the East River Basin, China

doi: 10.1007/s13753-018-0165-x

Peng Sun^1,2,3,
Qiang Zhang^{2,3,4
,
,},
Xihui Gu⁵,
Peijun Shi^2,3,4,
Vijay P. Singh⁶,
Changqing Song^2,3,4,
Xiuyu Zhang^{7
,
,}

1 College of Territorial Resources and Tourism, Anhui Normal University, Wuhu 241000, Anhui, China;
2 Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education, Beijing Normal University, Beijing 100875, China;
3 State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China;
4 Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing 100875, China;
5 School of Environmental Studies, China University of Geosciences, Wuhan 430074, Hubei, China;
6 Department of Biological and Agricultural Engineering and Zachry Department of Civil Engineering, Texas A&M University, College Station, TX 77843, USA;
7 South China Institute of Environmental Sciences, Guangzhou 510655, Guangdong, China

Funds:

41401052). Our cordial gratitude should be extended to the editor, Dr. Ying Li, and anonymous reviewers for their professional and pertinent comments and suggestions, which were greatly helpful for further quality improvement of this manuscript.

This work is financially supported by the Fund for Creative Research Groups of the National Natural Science Foundation of China (Grant No. 41621061), the National Science Foundation for Distinguished Young Scholars of China (Grant No. 51425903), and the National Science Foundation of China (Grant Nos. 41601023

Available Online: 2021-04-26

Abstract

Abstract

Seasonal precipitation changes under the influence of large-scale climate oscillations in the East River basin were studied using daily precipitation data at 29 rain stations during 1959–2010. Seasonal and global models were developed and evaluated for probabilistic precipitation forecasting. Generalized additive model for location, scale, and shape was used for at-site precipitation forecasting. The results indicate that: (1) winter and spring precipitation processes at most stations are nonstationary, while summer and autumn precipitation processes at few of the stations are stationary. In this sense, nonstationary precipitation processes are dominant across the study region; (2) the magnitude of precipitation is influenced mainly by the Arctic Oscillation, the North Pacific Oscillation, and the Pacific Decadal Oscillation (PDO). The El Niño / Southern Oscillation (ENSO) also has a considerable effect on the variability of precipitation regimes across the East River basin; (3) taking the seasonal precipitation changes of the entire study period as a whole, the climate oscillations influence precipitation magnitude, and this is particularly clear for the PDO and the ENSO. The latter also impacts the dispersion of precipitation changes; and (4) the seasonal model is appropriate for modeling spring precipitation, but the global model performs better for summer, autumn, and winter precipitation.
- China,
- ENSO regimes,
- GAMLSS model,
- Nonstationarity,
- Probabilistic precipitation forecasting

FullText(HTML)

References(31)

References

Box, G.E.P., G.M. Jenkins, G.C. Reinsel, and G.M. Ljung. 2015. Time series analysis: Forecasting and control. Hoboken, NJ: John Wiley & Sons.

Chang, N.B., Y.J. Yang, I. Sanaz, and L. Mullona. 2017. Multi-scale quantitative precipitation forecasting using nonlinear and nonstationary teleconnection signals and artificial neural network models. Journal of Hydrology 548: 305–321.

Chen, Y.D., T. Yang, C.Y. Xu, Q. Zhang, X. Chen, and Z.C. Hao. 2010. Hydrologic alteration along the Middle and Upper East River (Dongjiang) basin, South China: A visually enhanced mining on the results of RVA method. Stochastic Environmental Research and Risk Assessment 24(1): 9–18.

Chen, Y.D., Q. Zhang, M.Z. Xiao, and V.P. Singh. 2013. Evaluation of risk of hydrological droughts by the trivariate Plackett copula in the East River basin (China). Natural Hazards 68(2): 529–547.

Ding, Y., Y. Liu, S. Liang, X. Ma, Y. Zhang, D. Si, P. Liang, Y. Song, and J. Zhang. 2014. Interdecadal variability of the East Asian winter monsoon and its possible links to global climate change. Journal of Meteorological Research 28(5): 693–713.

Feng, J., and J. Li. 2011. Influence of El Niño Modoki on spring rainfall over south China. Journal of Geophysical Research 116(D13): 1–10.

Francesco, S., and C.G. Kilsby. 2014. Simulating daily rainfall fields over large areas for collective risk estimation. Journal of Hydrology 512: 285–302.

Francesco, S., and N. Rebora. 2015. Impact of precipitation forecast uncertainties and initial soil moisture conditions on a probabilistic flood forecasting chain. Journal of Hydrology 519: 1052–1067.

Gabriele, V., and S. Francesco. 2012. Development of statistical models for at-site probabilistic seasonal rainfall forecast. International Journal of Climatology 32(4): 2197–2212.

Huang, C., Q. Zhang, V.P. Singh, X.H. Gu, and P.J. Shi. 2017. Spatio-temporal variation of dryness/wetness across the Pearl River basin, China, and relation to climate indices. International Journal of Climatology 37(1): 318–332.

Ingsrisawang, L., S. Ingsriswang, S. Somchit, P. Aungsuratana, and W. Khantiyanan. 2008. Machine learning techniques for short-term rain forecasting system in the northeastern part of Thailand. World Academy of Science, Engineering and Technology 41: 248–253.

Jones, M.R., H.J. Fowler, C.G. Kilsby, and S. Blenkinsop. 2013. An assessment of changes in seasonal and annual extreme rainfall in the UK between 1961 and 2009. International Journal of Climatology 33(5): 1178–1194.

Kisi, O., and M. Cimen. 2012. Precipitation forecasting by using wavelet-support vector machine conjunction model. Engineering Applications of Artificial Intelligence 25(4): 783–792.

Li, J.Z., Y.X. Wang, S.F. Li, and R. Hu. 2016. A nonstationary standardized precipitation index incorporating climate indices as covariates. Journal of Geophysical Research Atmospheres 120(23): 12082–12095.

Lu, K., and L. Wang. 2011. A novel nonlinear combination model based on support vector machine for rainfall prediction. Proceedings of the IEEE 4th International Joint Conference on Computational Sciences and Optimization: 1343–1347.

Manzato, A. 2007. Sounding-derived indices for neural network based short-term thunderstorm and rainfall forecasts. Atmospheric Research 83(2–4): 349–365.

Mason, S.J., and O. Baddour. 2008. Statistical modelling. In Seasonal Climate: Forecasting and Managing Risk, ed. A. Troccoli, M. Harrison, D.L.T. Andersen, S.J. Mason, 189–192. Dordrecht, The Netherlands: Springer.

Mishra, A.K., and V.R. Desai. 2005. Spatial and temporal drought analysis in the Kansabati River Basin, India. International Journal of River Basin Management 3(1): 31–41.

Nastos, P.T., K.P. Moustris, I.K. Larissi, and A.G. Paliatsos. 2013. Rain intensity forecast using artificial neural networks in Athens, Greece. Atmospheric Research 119: 153–160.

Ortiz-García, E.G., S. Salcedo-Sanz, and C. Casanova-Mateo. 2014. Accurate precipitation prediction with support vector classifiers: A study including novel predictive variables and observational data. Atmospheric Research 139: 128–136.

Rigby, R.A., and D.M. Stasinopoulos. 2005. Generalized additive models for location, scale and shape. Journal of the Royal Statistical Society 54(3): 507–554.

Shukla, R.P., K.C. Tripathi, A.C. Pandey, and M.I. Das. 2011. Prediction of Indian summer monsoon rainfall using Niño indices: a neural network approach. Atmospheric Research 102: 99–109.

Villarini, G., and F. Serinaldi. 2012. Development of statistical models for at-site probabilistic seasonal rainfall forecast. International Journal of Climatology 32(14): 2197–2212.

Wang, S.W., J. Feng, and G. Liu. 2013. Application of seasonal time series model in the precipitation forecast. Mathematical and Computer Modelling 58(3–4): 677–683.

Wei, H., J.L. Li, and T.G. Liang 2005. Study on the estimation of precipitation resources for rainwater harvesting agriculture in semi-arid land of China. Agricultural Water Management 71(1): 33–45.

Wong, J.S., Q. Zhang, and Y.D. Chen. 2010. Statistical modeling of daily urban water consumption in Hong Kong: Trend, changing patterns, and forecast. Water Resources Research 46(3): Article W03506.

Xia, J., K.M. O’Connor, R.K. Kachroo, and G.C. Liang. 1997. A non-linear perturbation model considering catchment wetness and its application in river flow forecasting. Journal of Hydrology 200: 164–178.

Zhang, Q., J.F. Li, V.P. Singh, C.Y. Xu, and J. Deng. 2013. Influence of ENSO on precipitation in the East River basin, South China. Journal of Geophysical Research 118(5): 2207–2219.

Zhang, Q., V.P. Singh, J.T. Peng, and Y.D. Chen. 2012a. Spatial-temporal changes of precipitation structure across the Pearl River basin, China. Journal of Hydrology 440–441: 113–122.

Zhang, Q., P. Sun, V.P. Singh, and X. Chen. 2012b. Spatial-temporal precipitation changes (1956–2000) and their implications for agriculture in China. Global and Planetary Change 82–83: 86–95.

Zhang, Q., M.Z. Xiao, V.P. Singh, and Y.D. Chen. 2014. Max-stable based evaluation of impacts of climate indices on extreme precipitation processes across the Poyang Lake basin, China. Global and Planetary Change 122: 271–281.

Relative Articles

Supplements(0)

Cited By

Proportional views