A stochastic methodology for generation of seasonal time series reproducing overyear scaling behaviour

A. Langousis, and D. Koutsoyiannis, A stochastic methodology for generation of seasonal time series reproducing overyear scaling behaviour, Journal of Hydrology, 322, 138–154, 2006.

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[English]

In generating synthetic time series of hydrological processes at sub-annual scales it is important to preserve seasonal characteristics and short-term persistence. At the same time, it is equally important to preserve annual characteristics and overyear scaling behaviour. This scaling behaviour, which is equivalent to the Hurst phenomenon, has been detected in a large number of hydroclimatic series and affects seriously planning and design of hydrosystems. However, when seasonal models are used the preservation of annual characteristics and overyear scaling is a difficult task and is often ignored unless disaggregation techniques are applied, which, however, involve several difficulties (e.g. in parameter estimation) and inaccuracies. As an alternative, a new methodology is proposed that directly operates on seasonal time scale, avoiding disaggregation, and simultaneously preserves annual statistics and the scaling properties on overyear time scales. Two specific stochastic models are proposed, a simple widely used seasonal model with short memory to which long-term persistence is imposed using a linear filter, and a combination of two sub-models, a stationary one with long memory and a cyclostationary one with short memory. Both models are tested in a real world case and found to be accurate in reproducing all the desired statistical properties and virtually equivalent from an operational point of view.

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See also: http://dx.doi.org/10.1016/j.jhydrol.2005.02.037

Our works referenced by this work:

1. D. Koutsoyiannis, and Th. Xanthopoulos, A dynamic model for short-scale rainfall disaggregation, Hydrological Sciences Journal, 35 (3), 303–322, doi:10.1080/02626669009492431, 1990.
2. D. Koutsoyiannis, A nonlinear disaggregation method with a reduced parameter set for simulation of hydrologic series, Water Resources Research, 28 (12), 3175–3191, doi:10.1029/92WR01299, 1992.
3. D. Koutsoyiannis, and A. Manetas, Simple disaggregation by accurate adjusting procedures, Water Resources Research, 32 (7), 2105–2117, doi:10.1029/96WR00488, 1996.
4. D. Koutsoyiannis, Optimal decomposition of covariance matrices for multivariate stochastic models in hydrology, Water Resources Research, 35 (4), 1219–1229, doi:10.1029/1998WR900093, 1999.
5. D. Koutsoyiannis, A generalized mathematical framework for stochastic simulation and forecast of hydrologic time series, Water Resources Research, 36 (6), 1519–1533, 2000.
6. D. Koutsoyiannis, Coupling stochastic models of different time scales, Water Resources Research, 37 (2), 379–391, doi:10.1029/2000WR900200, 2001.
7. D. Koutsoyiannis, The Hurst phenomenon and fractional Gaussian noise made easy, Hydrological Sciences Journal, 47 (4), 573–595, doi:10.1080/02626660209492961, 2002.
8. D. Koutsoyiannis, A. Efstratiadis, and G. Karavokiros, A decision support tool for the management of multi-reservoir systems, Journal of the American Water Resources Association, 38 (4), 945–958, doi:10.1111/j.1752-1688.2002.tb05536.x, 2002.
9. D. Koutsoyiannis, A. Efstratiadis, G. Karavokiros, A. Koukouvinos, N. Mamassis, I. Nalbantis, E. Rozos, Ch. Karopoulos, A. Nassikas, E. Nestoridou, and D. Nikolopoulos, Master plan of the Athens water resource system — Year 2002–2003, Modernisation of the supervision and management of the water resource system of Athens, Report 14, 215 pages, Department of Water Resources, Hydraulic and Maritime Engineering – National Technical University of Athens, Athens, December 2002.
10. D. Koutsoyiannis, Climate change, the Hurst phenomenon, and hydrological statistics, Hydrological Sciences Journal, 48 (1), 3–24, doi:10.1623/hysj.48.1.3.43481, 2003.
11. D. Koutsoyiannis, G. Karavokiros, A. Efstratiadis, N. Mamassis, A. Koukouvinos, and A. Christofides, A decision support system for the management of the water resource system of Athens, Physics and Chemistry of the Earth, 28 (14-15), 599–609, doi:10.1016/S1474-7065(03)00106-2, 2003.
12. A. Langousis, Developement of cyclostationary stochastic hydrological models preserving short-term memory and long-term persistence, Diploma thesis, 327 pages, Department of Water Resources, Hydraulic and Maritime Engineering – National Technical University of Athens, July 2003.
13. D. Koutsoyiannis, Reliability concepts in reservoir design, Water Encyclopedia, Vol. 4, Surface and Agricultural Water, edited by J. H. Lehr and J. Keeley, 259–265, doi:10.1002/047147844X.sw776, Wiley, New York, 2005.
14. D. Koutsoyiannis, Nonstationarity versus scaling in hydrology, Journal of Hydrology, 324, 239–254, 2006.

Our works that reference this work:

1. D. Koutsoyiannis, H. Yao, and A. Georgakakos, Medium-range flow prediction for the Nile: a comparison of stochastic and deterministic methods, Hydrological Sciences Journal, 53 (1), 142–164, doi:10.1623/hysj.53.1.142, 2008.
2. D. Koutsoyiannis, Older and modern considerations in the design and management of reservoirs, dams and hydropower plants (Solicited), 1st Hellenic Conference on Large Dams, Larisa, doi:10.13140/RG.2.1.3213.5922, Hellenic Commission on Large Dams, Technical Chamber of Greece, 2008.
3. D. Koutsoyiannis, and A. Langousis, Precipitation, Treatise on Water Science, edited by P. Wilderer and S. Uhlenbrook, 2, 27–78, Academic Press, Oxford, 2011.
4. F. Lombardo, E. Volpi, and D. Koutsoyiannis, Rainfall downscaling in time: Theoretical and empirical comparison between multifractal and Hurst-Kolmogorov discrete random cascades, Hydrological Sciences Journal, 57 (6), 1052–1066, 2012.
5. A. Efstratiadis, Y. Dialynas, S. Kozanis, and D. Koutsoyiannis, A multivariate stochastic model for the generation of synthetic time series at multiple time scales reproducing long-term persistence, Environmental Modelling and Software, 62, 139–152, doi:10.1016/j.envsoft.2014.08.017, 2014.
6. D. Koutsoyiannis, Generic and parsimonious stochastic modelling for hydrology and beyond, Hydrological Sciences Journal, 61 (2), 225–244, doi:10.1080/02626667.2015.1016950, 2016.

Other works that reference this work (this list might be obsolete):

1. Arganis-Juarez, M.L., D. Dominguez Mora Ramon, H.L. Cisneros-Iturbe and G.E. Fuentes-Mariles, Synthetic sample generation of monthly inflows into two dams using the modified Svanidze method, Hydrological Sciences Journal, 53(1), 130-141, 2008.
2. Khaliq, M.N., T.B.M.J. Ouarda, P. Gachon and L. Sushama, Temporal evolution of low-flow regimes in Canadian rivers, Water Resources Research, 44 (8), W08436, 2008.
3. #Coser, M. C., and A. S. F. Mendonça, Modelagem de séries de vazões sazonais apresentando dependência de longo termo, Simpósio Brasileiro de Recursos Hídricos, 19, Anais, Campo Grande: ABRH, 2009.
4. Salas, J. D., and T. Lee, Nonparametric simulation of single-site seasonal streamflows, Journal of Hydrologic Engineering, 15 (4), 284-296, 2010.
5. Srivastav, R. K., K. Srinivasan and K. P. Sudheer, Simulation-optimization framework for multi-season hybrid stochastic models, Journal of Hydrology, 404 (3-4), 209-225, 2011.
6. Langousis, A., and V. Kaleris, Theoretical framework to estimate spatial rainfall averages conditional on river discharges and point rainfall measurements from a single location: an application to western Greece, Hydrol. Earth Syst. Sci., 17, 1241-1263, 10.5194/hess-17-1241-2013, 2013.
7. Yusof, F., I. L. Kane and Z. Yusop, Structural break or long memory: an empirical survey on daily rainfall data sets across Malaysia, Hydrol. Earth Syst. Sci., 17, 1311-1318, 2013.
8. #Müller, R., and N. Schütze, Improving the future performance and reliability of multi-reservoir systems by multi-objective optimization, IAHS-AISH Proceedings and Reports, 362, 24-32, 2013.
9. Ilich, N., An effective three-step algorithm for multi-site generation of stochastic weekly hydrological time series, Hydrological Sciences Journal, 59 (1), 85-98, 2014.
10. Panagoulia, D., and E. I. Vlahogianni, Non-linear dynamics and recurrence analysis of extreme precipitation for observed and general circulation model generated climates, Hydrological Processes, 28(4), 2281–2292, 2014.
11. Boudaghpour S., , M. Bagheri and Z. Bagheri, Using stochastic modeling techniques to predict the changes of total suspended solids and sediments in Lighvan Chai catchment area in Iran, Journal of River Engineering, 2 (1), 2014.
12. Srivastav, R., K. Srinivasan, and S. P. Sudheer, Simulation-optimization framework for multi-site multi-season hybrid stochastic streamflow modeling, Journal of Hydrology, doi:10.1016/j.jhydrol.2016.09.025, 2016.

Tagged under: Hurst-Kolmogorov dynamics, Scaling, Stochastics