A dynamic model for short-scale rainfall disaggregation

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.



The single-site dynamic disaggregation model developed and presented in this paper is a generalized step-by-step approach to stochastic disaggregation problems. The forms studied concern low-level variables with Markovian structure and normal or gamma marginal distributions. Combined with a rainfall model, the disaggregation scheme gives a rainfall generator, transforming monthly rainfall into events and hourly amounts. A particular application of the generator, based on historical data, is used to illustrate and test the model.

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See also: http://dx.doi.org/10.1080/02626669009492431

Our works referenced by this work:

1. D. Koutsoyiannis, A disaggregation model of point rainfall, PhD thesis, 310 pages, doi:10.12681/eadd/0910, National Technical University of Athens, Athens, 1988.

Our works that reference this work:

1. 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.
2. I. Nalbantis, D. Koutsoyiannis, and Th. Xanthopoulos, Modelling the Athens water supply system, Water Resources Management, 6, 57–67, doi:10.1007/BF00872188, 1992.
3. 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.
4. D. Koutsoyiannis, and A. Manetas, Simple disaggregation by accurate adjusting procedures, Water Resources Research, 32 (7), 2105–2117, doi:10.1029/96WR00488, 1996.
5. D. Koutsoyiannis, and C. Onof, Rainfall disaggregation using adjusting procedures on a Poisson cluster model, Journal of Hydrology, 246, 109–122, 2001.
6. D. Koutsoyiannis, and N. Mamassis, On the representation of hyetograph characteristics by stochastic rainfall models, Journal of Hydrology, 251, 65–87, 2001.
7. 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.
8. 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.

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

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

1. Glasbey, C.A., G. Cooper, and M.B. McGechan, Disaggregation of daily rainfall by conditional simulation from a point-process model, Journal of Hydrology, 165(1-4), 1-9, 1995
2. McGechan, M.B., and G. Cooper, A simulation-model operating with daily weather data to explore silage and haymaking opportunities in climatically different areas of Scotland, Agricultural Systems, 48(3), 315-343, 1995.
3. Khaliq, M.N., and C. Cunnane, Modelling point rainfall occurrences with the Modified Bartlett- Lewis Rectangular Pulses Model, Journal of Hydrology, 180(1-4), 109-138, 1996.
4. Connoly, R.D., J. Schirmer and P. K. Dunn, A daily rainfall disaggregation model, Agricultural and Forest Meteorology, 92(2), 105-117, 1998.
5. Heneker, T.H., M.F. Lambert and G. Kuczera, A point rainfall model for risk-based design, Journal of Hydrology, 247, 54-71, 2001.
6. Back, Á. J., R. Dorfman and R. Clarke, Modelagem da precipitação horária por meio do modelo de pulsos retangulares de Bartlett-Lewis modificado (Modelling hourly rainfall with modified Bartlett-Lewis model), Revista Brasileira de Recursos Hídricos, 4 (1), 5-17, 1999.
7. Burian, S.J., S.R. Durrans, S.J. Nix and R.E. Pitt, Training artificial neural networks to perform rainfall disaggregation, Journal of Hydrologic Engineering-ASCE, 6(1), 43-51, 2001.
8. Stehlik, J., and A. Bardossy, Multivariate stochastic downscaling model for generating daily precipitation series based on atmospheric circulation, Journal of Hydrology, 256(1-2), 120-141, 2002.
9. #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.
10. 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.
11. Elshamy, M.E., H.S. Wheater, N. Gedney and C. Huntingford, Evaluation of the rainfall component of a weather generator for climate impact studies, Journal of Hydrology, 326(1-4), 1-24, 2006.
12. 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.
13. Debele, B., R. Srinivasan and J. Yves Parlange, Accuracy evaluation of weather data generation and disaggregation methods at finer timescales, Advances in Water Resources, 30(5), 1286-1300, 2007.
14. Damé, R.D.C.F., C.F.A. Teixeira, and V.S.S.Terra, Comparison of different methodologies to estimate intensity-duration- frequency curves for Pelotas - RS, Brazil, Engenharia Agricola, 28 (2), 245-255, 2008.
15. Rupp, D. E., R. F. Keim, M. Ossiander, M. Brugnach and J. S. Selker, Time scale and intensity dependency in multiplicative cascades for temporal rainfall disaggregation, Water Resources Research, 45, W07409, doi:10.1029/2008WR007321, 2009.
16. Andrés-Doménech, I., A. Montanari and J. B. Marco, Stochastic rainfall analysis for storm tank performance evaluation, Hydrol. Earth Syst. Sci., 14, 1221-1232, doi:10.5194/hess-14-1221-2010, 2010.
17. Jennings, S. A., M. F. Lambert and G. Kuczera, Generating synthetic high resolution rainfall time series at sites with only daily rainfall using a master-target scaling approach, Journal of Hydrology, 393 (3-4), 163-173, 2010.
18. Serinaldi, F., Multifractality, imperfect scaling and hydrological properties of rainfall time series simulated by continuous universal multifractal and discrete random cascade models, Nonlin. Processes Geophys., 17, 697-714, doi: 10.5194/npg-17-697-2010, 2010.
19. #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.
20. Kalra, A., and S. Ahmad, Evaluating changes and estimating seasonal precipitation for the Colorado River Basin using a stochastic nonparametric disaggregation technique, Water Resources Research, 47, W05555, doi: 10.1029/2010WR009118, 2011.
21. Pui, A., A. Sharma, R. Mehrotra, B. Sivakumar and E. Jeremiah, A comparison of alternatives for daily to sub-daily rainfall disaggregation, Journal of Hydrology, 470–471, 138–157, 2012.
22. Abdellatif, M., W. Atherton and R. Alkhaddar, Application of the stochastic model for temporal rainfall disaggregation for hydrological studies in north western England, Journal of Hydroinformatics, 15 (2), 555-567, 2013.
23. #Kim, S., and Y. Seo, Spatial disaggregation of areal rainfall using multilayer perceptron, International Hydrological Program, Korean National Committee, 2014.
24. Dunkerley, D., Intra-event intermittency of rainfall: an analysis of the metrics of rain and no-rain periods, Hydrological Processes, 29 (15), 3294-3305, 10.1002/hyp.10454, 2015.
25. Kim, S. and V.P. Singh, Spatial disaggregation of areal rainfall using two different artificial neural networks models, Water, 7(6), 2707-2727, 10.3390/w7062707, 2015.
26. Schiavo Bernardi, E., D. Allasia, R. Basso, P. Freitas Ferreira and R. Tassi, TRMM rainfall estimative coupled with Bell (1969) methodology for extreme rainfall characterization, Proc. IAHS, 369, 163-168, 10.5194/piahs-369-163-2015, 2015.

Tagged under: Stochastic disaggregation, Rainfall models, Stochastics