Annual and seasonal discharge prediction in the middle Danube River basin based on a modified TIPS (Tendency, Intermittency, Periodicity, Stochasticity) methodology

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-LSearch 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.ch5Search 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.2014.08.017Search 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-zSearch 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-2014-0027Search 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.0006518Search 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.2016.07.014Search 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: 10.1029/2000WR900044.10.1029/2000WR900044Search in Google Scholar

Koutsoyiannis, D., 2003. Climate change, the Hurst phenomenon and hydrological statistics. Hydrol. Sci. J., 48, 3–24.10.1623/hysj.48.1.3.43481Search in Google Scholar

Labat, D., 2006. Oscillations in land surface hydrological cycle. Earth and Planetary Science Letters, 242, 143–154.10.1016/j.epsl.2005.11.057Search 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, 885–900.10.13031/2013.23153Search 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.5939Search 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, 1379–1389.10.1080/01621459.1968.10480934Search 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-2015-0009Search 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.2014.09.081Search in Google Scholar