[Aksoy, H., Bayazit, M., 2000. A model for daily flows of intermittent streams. Hydrological Processes, 14, 1725–1744.10.1002/1099-1085(200007)14:10<1725::AID-HYP108>3.0.CO;2-L]Search in Google Scholar
[Box., G.E.P., Jenkins, G.M., Reinsel, G.C., 2008. Time Series Analysis, Forecasting and Control. Fourth Edition. John Wiley & Sons, INC., Publication. USA.10.1002/9781118619193.ch5]Search in Google Scholar
[Efstratiadis, A., Dialynas, Y.G., Kozanis, S., Koutsoyiannis, D., 2014. A multivariate stochastic model for the generation of synthetic time series at multiple time scales reproducing long-term persistence. Environmental Modeling and Software, 62, 139–152.10.1016/j.envsoft.2014.08.017]Search in Google Scholar
[Cengiz, T.M., 2011. Periodic structures of great lakes levels using wavelet analysis. J. Hydrol. Hydromech., 59, 24–35.10.2478/v10098-011-0002-z]Search in Google Scholar
[Fendeková, M., Pekárová, P., Fendek, M., Pekár, J., Škoda, P., 2014. Global drivers effect in multi-annual variability of runoff. J. Hydrol. Hydromech., 62, 169–176.10.2478/johh-2014-0027]Search in Google Scholar
[Hipel, K.W., McLeod, A.I., 1994. Time Series Modelling of Water Resources and Environmental Systems. Elsevier, Amsterdam, The Netherlands.]Search in Google Scholar
[Hurst, H., 1951. Long term storage capacity of reservoirs. Transactions of the American Society of Civil Engineers, 6, 770–799.10.1061/TACEAT.0006518]Search in Google Scholar
[Kostić, S., Stojković, M., Prohaska, S., 2016. Hydrological flow rate estimation using artificial neural networks: model development and potential applications. Applied Mathematics and Computation. DOI: 10.1016/j.amc.2016.07.014.10.1016/j.amc.2016.07.014]Search in Google Scholar
[Koutsoyiannis, D., 2000. A generalized mathematical framework for stochastic simulation and forecast of hydrologic time series. Water Resources Research, 36, 1519–1533. DOI: 10.1029/2000WR900044.10.1029/2000WR900044]Search in Google Scholar
[Koutsoyiannis, D., 2003. Climate change, the Hurst phenomenon and hydrological statistics. Hydrol. Sci. J., 48, 3–24.10.1623/hysj.48.1.3.43481]Search in Google Scholar
[Labat, D., 2006. Oscillations in land surface hydrological cycle. Earth and Planetary Science Letters, 242, 143–154.10.1016/j.epsl.2005.11.057]Search in Google Scholar
[Mandelbrot, B.B., 1965. Une class de processus stochastiques nomothetiquesa mothetiques a soi: Application a la loi climatologiqu de H. E. Hurst. [A class of stochastic homogeneous processes: Application to the climatological law of H. E. Hurst]. C. R. Hebd. Seances Acad. Sci., 260, 3284–3277. (In French.)]Search in Google Scholar
[Moriasi, D.N., Arnold, J.G., van Liew, M.W., Bingner, R.L., Harmel, R.D., Veith, T.L., 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50, 885–900.10.13031/2013.23153]Search in Google Scholar
[Pekarova, P., Pekar, J., 2006. Long-term discharge prediction for the Turnu Severin station (the Danube) using a linear autoregressive model. Hydrological Processes, 20, 1217–1228. DOI: 10.1002/hyp.5939.10.1002/hyp.5939]Search in Google Scholar
[Pekarova, P., Miklanek, P., Pekar, J., 2006. Long-term trends and runoff fluctuations of European rivers. IAHS Publ. 308. IAHS Press, Wallingford, pp. 520–525.]Search in Google Scholar
[Salas, J.D., Delleur, J.W., Yevjevich, V., Lane, W.L., 1980. Applied Modeling of Hydrologic Time Series, Water Resources Publications. Littleton. Colorado. USA. 484.]Search in Google Scholar
[Sen, P.K., 1968. Estimates of the regression coefficient based on Kendall’s tau. Journal of the American Statistical Association, 63, 1379–1389.10.1080/01621459.1968.10480934]Search in Google Scholar
[Stojković., M., Plavšić, J., Prohaska, S., 2012. Stohastička analiza serija srednje godišnjih proticaja na stanicama na Dunavu. [Stochastic analysis of mean annual flow time series for the sites of the Danube River]. 16. Savetovanje SDHI i SDH. Donji Milanovac. Serbia. (In Serbian.)]Search in Google Scholar
[Stojković, M., Prohaska, S., Plavšić, J., 2014. Internal stochastic structure of annual discharge time series of Serbia’s large Rivers. Journal of Serbian Water Pollution Control Society “Water Research and Management”, 4, 3–13.]Search in Google Scholar
[Stojković., M., Prohaska, S., Plavšić, J., 2015. Stochastic structure of annual discharges of large European rivers. J. Hydrol. Hydromech., 63, 63–70.10.1515/johh-2015-0009]Search in Google Scholar
[Thomas., H.A., Fiering, M.B., 1962. Mathematical Synthesis of Streamflow Sequences for the Snalysis of River Basin by Simulation. In Design of Water Resources Systems. Harvard University Press, Cambridge, Massachusetts.]Search in Google Scholar
[Yevjevich, V., 1963. Fluctuation of wet and dry years - Part 1. Research data assembly and mathematical models. Hydrology paper 1. Colorado State University, Fort Collins, Colorado, USA.]Search in Google Scholar
[Yevjevich, V., 1972. Stochastic Processes in Hydrology. Water Resources Publications, Fort Collins, Colorado, USA.]Search in Google Scholar
[Yevjevich, V., 1984. Structure of Daily Hydrologic Series. Water Resources Publications. Water Resources Publications, Fort Collins, Colorado, USA.]Search in Google Scholar
[Wanga, H., Sankarasubramanianb, A., Ranjithanb, R.S., 2014. Understanding the low-frequency variability in hydroclimatic attributes over the southeastern US. Journal of Hydrology, 521, 170–181.10.1016/j.jhydrol.2014.09.081]Search in Google Scholar