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.

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[English]

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:

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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.
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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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Tagged under: Stochastic disaggregation, Extremes, Stochastics