A stochastic disaggregation method for design storm and flood synthesis

D. Koutsoyiannis, A stochastic disaggregation method for design storm and flood synthesis, Journal of Hydrology, 156, 193–225, doi:10.1016/0022-1694(94)90078-7, 1994.



A simple technique for short scale rainfall disaggregation is developed and studied both theoretically and empirically. This technique can be combined with a variety of rainfall models. The simplest implementation of the technique for a Markovian structure at a discrete time with only three parameters is studied in detail as an easy and convenient engineering tool for design storm and flood studies. Combining the disaggregation technique with a succession of simple hydrologic models, i.e., a production function, a unit hydrograph and a flood routing model we form a stochastic approach for design storm and flood synthesis. Similar to common engineering methods the proposed method starts with the selection of certain characteristics of the design storm (i.e., its duration and total depth that corresponds to a given return period). Subsequently, the method generates a series of probable time distributions by disaggregating the given total depth into incremental depths. Then the series of hyetographs is routed through the hydrological models and the result is the conditional probability distribution function of the outflow peak of the hydraulic construction studied, given the duration and total storm depth. From this distribution we can adopt the design discharge either as the conditional expected value of the outflow peak or a value corresponding to a selected probability level. The method is illustrated with a real-world example and compared to common engineering methods.

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See also: http://dx.doi.org/10.1016/0022-1694(94)90078-7

Our works referenced by this work:

1. D. Koutsoyiannis, and P. van der Riet, Hydrology, Ch. 5 in Engineering Studies, Arachthos River, Middle Course hydroelectric projects, Master Plan, Commissioner: Public Power Corporation, Contractor: Arachthos Swiss-Anglo-German Consulting Group (ASAG), Report number 2, 38 pages, Athens, October 1983.
2. D. Koutsoyiannis, and P. van der Riet, Hydrology, Ch. 5, Arachthos River, Aghios Nicolaos hydroelectric project, Engineering Report, Commissioner: Public Power Corporation, Contractor: Arachthos Swiss-Anglo-German Consulting Group (ASAG), Report number 2, 38 pages, Athens, August 1984.
3. R. Ruoss, and D. Koutsoyiannis, Hydrology, Ch. 4 in Engineering Studies I, Arachthos River, Steno - Kalaritikos hydroelectric project, Engineering Report, Commissioner: Public Power Corporation, Contractor: Arachthos Swiss-Anglo-German Consulting Group (ASAG), Report number 2, 17 pages, Athens, August 1984.
4. D. Koutsoyiannis, A disaggregation model of point rainfall, PhD thesis, 310 pages, doi:10.12681/eadd/0910, National Technical University of Athens, Athens, 1988.
5. 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.
6. 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.
7. D. Koutsoyiannis, and E. Foufoula-Georgiou, A scaling model of storm hyetograph, Water Resources Research, 29 (7), 2345–2361, doi:10.1029/93WR00395, 1993.

Our works that reference this work:

1. D. Koutsoyiannis, and D. Pachakis, Deterministic chaos versus stochasticity in analysis and modeling of point rainfall series, Journal of Geophysical Research-Atmospheres, 101 (D21), 26441–26451, doi:10.1029/96JD01389, 1996.
2. D. Koutsoyiannis, and A. Manetas, Simple disaggregation by accurate adjusting procedures, Water Resources Research, 32 (7), 2105–2117, doi:10.1029/96WR00488, 1996.
3. D. Koutsoyiannis, D. Kozonis, and A. Manetas, A mathematical framework for studying rainfall intensity-duration-frequency relationships, Journal of Hydrology, 206 (1-2), 118–135, 1998.
4. D. Koutsoyiannis, and C. Onof, Rainfall disaggregation using adjusting procedures on a Poisson cluster model, Journal of Hydrology, 246, 109–122, 2001.
5. D. Koutsoyiannis, and N. Mamassis, On the representation of hyetograph characteristics by stochastic rainfall models, Journal of Hydrology, 251, 65–87, 2001.
6. D. Koutsoyiannis, C. Onof, and H. S. Wheater, Multivariate rainfall disaggregation at a fine timescale, Water Resources Research, 39 (7), 1173, doi:10.1029/2002WR001600, 2003.
7. 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.
8. P. Kossieris, C. Makropoulos, C. Onof, and D. Koutsoyiannis, A rainfall disaggregation scheme for sub-hourly time scales: Coupling a Bartlett-Lewis based model with adjusting procedures, Journal of Hydrology, doi:10.1016/j.jhydrol.2016.07.015, 2016.
9. E. Dodangeh, K. Shahedi, K. Solaimani, and P. Kossieris, Usability of the BLRP model for hydrological applications in arid and semi-arid regions with limited precipitation data, Modeling Earth Systems and Environment, 2017.

Works that cite this document: View on Google Scholar or ResearchGate

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1. Arnaud, P., and J. Lavabre, Using a stochastic model for generating hourly hyetographs to study extreme rainfalls, Hydrological Sciences Journal, 44(3), 433-446, 1999.
2. Arnaud, P., J. Lavabre and J.-M. Masson, Results improvement of stochastic model for generating hourly hyetographs, applied to the French seaboard, Revue des Sciences de l'Eau, 12(2), 251-271, 1999.
3. Arnaud, P., and J. Lavabre, A stochastic model of hourly rainfall with rainfall-runoff transformation for predicting flood frequency, Revue des Sciences de l'Eau, 13(4), 441-462, 2000.
4. Heneker, T.H., M.F. Lambert and G. Kuczera, A point rainfall model for risk-based design, Journal of Hydrology, 247, 54-71, 2001.
5. Arnaud, P., and J. Lavabre, Coupled rainfall model and discharge model for flood frequency estimation, Water Resources Research, 38 (6), art. no. 1075, 2002.
6. #Wending, I., and W. James, Two neural networks for generation of high-resolution long-term storm rainfall compared to Ormsbee's method - Case study for Toronto, Global Solutions for Urban Drainage, 1-15, 2002.
7. Wendling, I., and W. James, Comparison of neural networks to Ormsbee's method for rain generation - applied to Toronto, Ontario, Journal of Water Management Modeling, 10.14796/JWMM.R215-20, 2003.
8. Ramos, M.H., Rainfall disaggregation, Houille Blanche-Revue Internationale de L'Eau, (6), 123-128 2003.
9. #EPA and Hard Rock Mining, A Source Book for Industry in the Northwest and Alaska, Appendix A: Hydrology, US Environmental Protection Agency, 2003.
10. Kandel, D.D., A.W. Western, R.B. Grayson and H.N. Turra, Process parameterization and temporal scaling in surface runoff and erosion modelling, Hydrological Processs, 18 (8), 1423-1446, 2004.
11. Salvadori, G., and C. De Michele, Frequency analysis via copulas: Theoretical aspects and applications to hydrological events, Water Resources Research, 40(12), W12511, 2004.
12. Cowpertwait, P.S.P., A spatial-temporal point process model of rainfall for the Thames catchment, UK, Journal of Hydrology, 330(3-4), 586-595, 2006.
13. Cowpertwait, P.S.P., T. Lockie and M.D. Davies, A stochastic spatial-temporal disaggregation model for rainfall, Journal of Hydrology New Zealand, 45(1), 1-12, 2006.
14. Wu, S.-J., Y.-K. Tung and J.-C. Yang, Stochastic generation of hourly rainstorm events, Stochastic Environmental Research and Risk Assessment, 21(2), 195-212, 2006.
15. Kwon, H.H, U. Lall and A.F. Khalil, Stochastic simulation model for nonstationary time series using an autoregressive wavelet decomposition: Applications to rainfall and temperature, Water Resources Research, 43(5), W05407, 2007.
16. Arnaud, P., J.A. Fine and J. Lavabre, An hourly rainfall generation model applicable to all types of climate, Atmospheric Research, 85(2), 230-242, 2007.
17. Wang, Q.J., and R.J. Nathan, A method for coupling daily and monthly time scales in stochastic generation of rainfall series, Journal of Hydrology, 346(3-4), 122-130, 2007.
18. Arnaud, P., J. Lavabre, B. Sol, and C. Desouches, Regionalization of an hourly rainfall generating model over metropolitan France for flood hazard estimation, Hydrological Sciences Journal, 53(1), 34-47, 2008.
19. Kim, B.-S., B.-K. Kim, M.-S. Kyung and H.-S. Kim, Impact Assessment of Climate Change on Extreme Rainfall and I-D-F Analysis, Journal of Korea Water Resources Association, 41 (4), 379-394, 2008.
20. Morrissey, M. L., Superposition of the Neyman–Scott Rectangular Pulses Model and the Poisson White Noise Model for the Representation of Tropical Rain Rates, Journal of Hydrometeorology, 10(2), 395-412, 2009.
21. Kwon, H., U. Lall, and J. Obeysekera, Simulation of daily rainfall scenarios with interannual and multidecadal climate cycles for South Florida, Stochastic Environmental Research and Risk Assessment, 23 (7), 879-896, 2009.
22. Calvo, B.. and F. Savi, A real-world application of Monte Carlo procedure for debris flow risk assessment, Computers & Geosciences, 35(5), 967–977, 2009.
23. #Varga, C., J. E. Ball and M. Babister, A hydroinformatic approach to development of design temporal patterns of rainfall, IAHS Publication 331, 20-29, 2009.
24. #Ortiz, E., and E. Todini, Acople modelos numéricos de tiempo (NWP) a modelos hidrológicos distribuidos. Sistema de predicciones hidrometeorológicas en tiempo real en las cuencas de Galicia Costa. El sistema ARTEMIS, “Meteorología y Energías Renovables ”. XXXI Jornadas Científicas de la Asociación Meteorológica Española, 2010.
25. Vandenberghe, S., N. E. C. Verhoest, E. Buyse and B. De Baets, A stochastic design rainfall generator based on copulas and mass curves, Hydrol. Earth Syst. Sci., 14, 2429–2442, 2010.
26. Lee, T., J. D. Salas, and J. Prairie, An enhanced nonparametric streamflow disaggregation model with genetic algorithm, Water Resour. Res., 46, W08545, doi:10.1029/2009WR007761, 2010.
27. He, L., G. Q. Wang and X. D. Fu, Disaggregation model of daily rainfall and its application in the Xiaolihe watershed, Yellow River, Journal of Environmental Informatics, 16(1), 11-18, 2010.
28. #Sharma, A., and R. Mehrotra, Rainfall Generation, in Rainfall: State of the Science (eds F. Y. Testik and M. Gebremichael), American Geophysical Union, Washington, DC, 10.1029/2010GM000973, 2010.
29. Ndiritu, J., A variable length block bootstrap for multi-site synthetic streamflow generation, Hydrol. Sci. J., 56 (3), 362-379, 2011.
30. Derakhshana, H., and N. Talebbeydokhti, Rainfall disaggregation in non-recording gauge stations using space-time information system, Scientia Iranica, 18 (5), 995-1001, 2011.
31. Dewals, B. J., P. Archambeau, B. K. Duy, S. Erpicum and M. Pirotton, Semi-explicit modelling of watersheds with urban drainage systems, Engineering Applications of Computational Fluid Mechanics, 6 (1), 46–57, 2012.
32. Lu, B. H., H. H. Gu, Z. Y. Xie, J. F. Liu, L. J. Ma and W. X. Lu, Stochastic simulation for determining the design flood of cascade reservoir systems, Hydrology Research, 43 (1-2), 54-63, 2012.
33. Rogger, M., B. Kohl, H. Pirkl, A. Viglione, J. Komma, R. Kirnbauer, R. Merz and G. Blöschl, Runoff models and flood frequency statistics for design flood estimation in Austria - do they tell a consistent story?, Journal of Hydrology, 456-457, 30-43, 2012.
34. French, R., and M. Jones, Design rainfall temporal patterns in Australian Rainfall and Runoff: Durations exceeding one hour, Australian Journal of Water Resources, 16 (1), 21-28, 2012.
35. Bisantino, T., R. Bingner, W. Chouaib, F. Gentile and L. G. Trisorio, Estimation of runoff, peak discharge and sediment load at the event scale in a medium-size Mediterranean watershed using the AnnAGNPS model, Land Degradation & Development, 10.1002/ldr.2213, 2013.
36. Wright, D. B., J. A. Smith, G. Villarini and M. L. Baeck, Estimating the frequency of extreme rainfall using weather radar and stochastic storm transposition, Journal of Hydrology, 10.1016/j.jhydrol.2013.03.003, 2013.
37. Yusop, Z., H. Nasir and F. Yusof, Disaggregation of daily rainfall data using Bartlett Lewis Rectangular Pulse model: a case study in central Peninsular Malaysia, Environmental Earth Sciences, 71 (8), 3627-3640, 2014.
38. Jeong, C., and T. Lee, Copula-based modeling and stochastic simulation of seasonal intermittent streamflows for arid regions, Journal of Hydro-environment Research, 10.1016/j.jher.2014.06.001, 2014.
39. AghaKouchak, A., Entropy-copula in hydrology and climatology, Journal of Hydrometeorology, 10.1175/JHM-D-13-0207.1, 2014.
40. Lobo, G.P., J.R. Frankenberger, D.C. Flanagan and C.A. Bonilla, Evaluation and improvement of the CLIGEN model for storm and rainfall erosivity generation in central Chile, Catena, 127, 206-213, 2015.
41. #Lobo, G., and C. Bonilla, Modeling the erosivity of frontal storms in the semi-arid climate of Central Chile using CLIGEN, E-proceedings of the 36th IAHR World Congress, The Hague, the Netherlands, 2015.
42. Villani, V., D. Di Serafino, G., Rianna, and P. Mercogliano, Stochastic models for the disaggregation of precipitation time series on sub-daily scale: identification of parameters by global optimization, CMCC Research Paper, RP0256, 2015.

Tagged under: Stochastic disaggregation, Extremes, Stochastics