**References**

- J.X. Chang, H.X. Zhang, Y.M. Wang, Y.L. Zhu, Assessing the impact of climate variability and human activities on streamflow variation, Hydrol. Earth Syst. Sci., 20 (2016) 1547–1560.
- J.T. Barge, H.O. Sharif, An ensemble empirical mode decomposition, self-organizing map, and linear genetic programming approach for forecasting river streamflow, Water, 8 (2016) 247.
- F.H.S. Chiew, N.J. Potter, J. Vaze, C. Petheram, L. Zhang, J. Teng, D.A. Post, Observed hydrologic non-stationarity in far southeastern Australia: implications for modelling and prediction, Stochastic. Environ. Res. Risk Assess., 28 (2014) 3–15.
- S.H.W. Wang, B.J. Fu, S.H.L. Piao, Y.H. Lü, P. Ciais, X.M. Feng, Y.F. Wang, Reduced sediment transport in the Yellow River due to anthropogenic changes, Nat. Geosci., 9 (2015) 1–5.
- L. Chen, Y. Zhang, J. Zhou, V.P. Singh, S. Guo, J. Zhange, Realtime error correction method combined with combination flood forecasting technique for improving the accuracy of flood forecasting, J. Hydrol., 521 (2015) 157–169.
- E. Stonevicius, G. Valiuskevicius, E. Rimkus, J. Kazys, Climate induced changes of Lithuanian rivers runoff in 1960–2009, Water Resour., 14 (2014) 592–603.
- S.H.L. Lu, D.L. Li, J. Wen, Analysis on periodic variations and abrupt change of air temperature over Qinghai-Xizang plateau under global warming, Plateau Meteorol., 29 (2010) 1378–1385.
- H.C. Lloyd, S.W. Tommy, Runoff forecasting for an asphalt plane by artificial neural networks and comparisons with kinematic wave and autoregressive moving average models, J. Hydrol., 397 (2011) 191–201.
- D.P. Solomatine, K.N. Dulal, Model trees as an alternative to neural networks in rainfall-runoff modeling, Hydrol. Sci. J., 48 (2003) 399–411.
- R.B. Mohammad, Z. Zahra, K. Sungwon, Soft computing techniques for rainfall-runoff simulation: local non–parametric paradigm vs. model classification methods, Water Resour. Manage., 31 (2017) 3843–3865.
- O. Kisi, C. Ozkan, A new approach for modeling sedimentdischarge relationship: local weighted linear regression, Water Resour. Manage., 31 (2017) 1–23.
- J.-H. Jeon, C.-G. Park, A. Bernard, Comparison of performance between genetic algorithm and SCE-UA for calibration of SCS-CN surface runoff simulation, Water, 6 (2014) 3433–3456.
- N. Vahid, H.B. Aida, A. Jan, G. Mekonnen, Using self-organizing maps and wavelet transforms for space–time pre-processing of satellite precipitation and runoff data in neural network based rainfall–runoff modeling, J. Hydrol., 407 (2013) 28–40.
- X. Zhao, X. Chen, Y. Xu, An EMD-based chaotic least squares support vector machine hybrid model for annual runoff forecasting, Water, 9 (2017) 153.
- R.H. Compagnucci, S.A. Blanco, M.A. Figliola, P.M. Jacovkis, Variability in subtropical Andean Argentinean Atuel river: a wavelet approach, Environmetrics, 11 (2015) 251–269.
- C. Gaucherel, Use of wavelet transform for temporal characterization of remote watersheds, J. Hydrol., 269 (2002) 101–121.
- C.H.M. Liu, L. Cheng, Analysis on runoff series with special reference to drying up courses of Lower Huanghe River, J. Geogr. Sci., 55 (2000) 57–265.
- L. Chen, V.P. Singh, Entropy-based derivation of generalized distributions for hydrometeorological frequency analysis, J. Hydrol., 577 (2018) 699–712.
- A.H. Tewfiki, D. Sinaha, P. Jorgensen, On the optimal choice of a wavelet for signal representation, IEEE Trans. Inf. Theory, 38 (1992) 747–765.
- D. Labat, J. Ronchail, J. Callede, J.L. Guyot, Wavelet analysis of Amazon hydrological regime variability, Geophys. Res. Lett., 31 (2004) 33–45.
- J. Wang, J.J. Meng, Research on runoff variations based on wavelet analysis and wavelet neural network model: a case study of the Heihe River drainage basin, J. Geogr. Sci., 17 (2007) 327–338.
- N.E. Huang, Z. Shen, S.R. Long, M.C. Wu, H.H. Shih, Q. Zheng, N.C. Yen, C.C. Tung, H.H. Liu, The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis, Proc. R. Soc. London, Ser. A, 8 (1998) 903–995.
- N.E. Huang, Z. Shen, S.R. Long, A new view of nonlinear water waves: the Hilbert spectrum, Annu. Rev. Fluid Mech., 31 (1999) 417–457.
- M. Li, X. Wu, X. Liu, An improved EMD method for timefrequency feature extraction of telemetry vibration signal based on multi-scale median filtering, Circuits Syst. Signal Process., 34 (2015) 815–830.
- K.Q. Zhao, A.L. Xuan, Set pair theory-a new theory method of non-define and its applications, Syst. Eng., 14 (1989) 18–23.
- K.Q. Zhao, Set Pair Analysis and Its Elementary Application, Science and Technology Publishing House of Zhejiang, Hangzhou, 2000.
- Q. Zou, J.ZH. Zhou, CH. Zhou, L.X. Song, J. Guo, Comprehensive flood risk assessment based on set pair analysis-variable fuzzy sets model and fuzzy AHP, Stochastic Environ. Res. Risk Assess., 27 (2013) 525–546.
- B. Zhu, H.F. Wang, W.S.H. Wang, Y.Q. Li, Analysis of relation between flood peak and volume based on set pair analysis, J. Sichuan Univ., 39 (2007) 29–33.
- P. Feng, R.G. Han, Z.H.H. Ding, Multiple time-scale SPA analysis on uncertainty relationship between rivers’ runoff time series, J. Sci. Eng., 17 (2009) 716–726.
- D.R. Zhang, C.H. Xue, Relationship between the El Nino and precipitation patterns in China since 1500 AD, Q. J. Appl. Meteorol., 5 (1994) 168–175.
- D. Liu, Q. Fu, T. Li, W. Li, Wavelet analysis of the complex precipitation series in the Northern Jiansanjiang Administration of the Heilongjiang land reclamation, China, J. Water Clim. Change, 7 (2016) 796–809.
- Y. Mei, H. Deng, F. Wang, On midrange periodicities in solar radio flux and sunspot areas, Astrophys. Space Sci., 363 (2018) 84.