Stochastic structure of annual discharges of large European rivers

Open access

Abstract

Water resource has become a guarantee for sustainable development on both local and global scales. Exploiting water resources involves development of hydrological models for water management planning. In this paper we present a new stochastic model for generation of mean annul flows. The model is based on historical characteristics of time series of annual flows and consists of the trend component, long-term periodic component and stochastic component. The rest of specified components are model errors which are represented as a random time series. The random time series is generated by the single bootstrap model (SBM). Stochastic ensemble of error terms at the single hydrological station is formed using the SBM method. The ultimate stochastic model gives solutions of annual flows and presents a useful tool for integrated river basin planning and water management studies. The model is applied for ten large European rivers with long observed period. Validation of model results suggests that the stochastic flows simulated by the model can be used for hydrological simulations in river basins.

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  • Birsan M.V. Molnar P. Burlando P. Pfaundler M. 2005. Streamflow trends in Switzerland. Journal of Hydrology 314 1-4 312-329.

  • Blasco N. Santamaria R. 1996. Testing memory patterns in the Spanish stock market. Applied Financial Economics 6 5 401-411.

  • Box G.E.P. Jenkins G. 1970. Time Series Analysis Forecasting and Control. First Ed. Holden day Inc. San Francisco.

  • Douglas E.M. Vogel R.M. Kroll C.N. 2000. Trends in floods and low flows in the United States: impact of spatial correlation. Journal of Hydrology 240 1-2 90-105.

  • Fendekova M. Pekarova P. Fendek M. Pekár J. Škoda P. 2014. Global drivers effect in multi-annual variability of runoff. J. Hydrol. Hydromech. 62 3 169-176.

  • Hanggi P. Weingartner. R. 2011. Inter-annual variability of runoff and climate within the Upper Rhine River Basin 1808-2007. Hydrological Sciences Journal 56 1 34-50.

  • Hannaford J. Buys G. Stahl K. Tallaksen L.M. 2013. The influence of decadal-scale variability on trends in long European streamflow records. Hydrol. Earth Syst. Sci. 17 2717-2733.

  • Hurst H. 1951. Long term storage capacity of reservoirs Transactions of the American Society of Civil Engineers. 6 770-799.

  • Kendall M.G. Stuart A. 1966. Anvanced of Statistics. vol 3. Design and Analysis and Time Series. Hafner Publ. Co. New York.

  • Khaliq M.N. Ouarda T. Gachon P. Sushama L. St- Hilaire A. 2009. Identification of hydrological trends in the presence of serial and crosscorrelations: A review of selected methods and their application to annual flow regimes of Canadian rivers. Journal of Hydrology 368 1-4 117-130

  • Kundzewicz Z.W. Bates B. Wu S. Palutikof J. 2008. Climate change and water Intergovernmental Panel on Climate Change. IPCC Technical Paper VI. IPCC Secretariat Geneva 210 p.

  • Labat D. 2006. Oscillations in land surface hydrological cycle. Earth and Planetary Science Letters 242 1-2 143-154.

  • Labat D. Godderis Y. Probst J.L. Guyot J.L. 2004. Evidence for global runoff increase related to climate warming. Advances in Water Resources 27 6 631-642.

  • Machiwal D. Jha M.K. 2006. Time series analysis of hydrologic data for water resources planning and management: a review. J. Hydrol. Hydromech. 54 3 237-257.

  • Milly P.C.D. Dunne K.A. Vecchia A.V. 2005. Global pattern of trends in streamflow and water availability in a changing climate. Nature 438 7066 347-350.

  • Mudelsee M. 2010. Climate Time Series Analysis: Classical Statistical and Bootstrap Methods. Springer Dordrecht 474 p.

  • Pekarova P. Pekar J. 2006. Long-term discharge prediction for the Turnu Severin station (the Danube) using a linear autoregressive model. Hydrological Processes 20 5 1217-1228.

  • Pekarova P. Miklanek P. Pekar J. 2003. Spatial and temporal runoff oscillation analysis of the main rivers of the world during the 19th-20th centuries. Journal of Hydrology 274 1-4 62-79.

  • Pekarova P. Miklanek P. Pekar J. 2006. Long-term trends and runoff fluctuations of European rivers. Climate variability and change - hydrological impacts. In: Proc. 5th FRIEND World Conference Havana Cuba.

  • Probst J. Tardy Y. 1987. Long range streamflow and world continental runoff fluctuation since the beginning of this century. Journal of Hydrology 94 3-4 289-311.

  • 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 p. (2nd Printing 1985 3rd Printing 1988)

  • Shuster A. 1887. On lunar and solar periodicities of earthquakes. Proc. Roy. Soc. pp. 455-465.

  • Srinivas V.V. Srinivasan K. 2005. Hybrid moving block bootstrap for stochastic simulation of multi-site multiseason streamflows. Journal of Hydrology 302 1-4 307-330.

  • Stahl K. Hisdal H. Hannaford J. Tallaksen L.M. Van Lanen H.A.J. Sauquet E. Demuth S. Fendekova M. Jodar J. 2010. Streamflow trends in Europe: evidence from a dataset of near-natural catchments. Hydrology and Earth System Sciences. 14 12 2367-2382.

  • Stojković M. Ilić A. Prohaska S. Plavšić J. 2014. Multitemporal analysis of mean annual and seasonal stream flow trends including periodicity and multiple non-linear regression. Water Resources Management 28 12 4319-4335.

  • Stojković M. Prohaska S. A. Koprivica. 2012. Analysis of Trends and Cycles in Longest Hydrometeorological Time Series in the World. BALWOIS Ohrid Macedonia.

  • Yevjevich V. 1972. Stochastic processes in hydrology. Water Resources Publications Fort Collins Colorado USA.

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