Rainfall disaggregation methods: Theory and applications (invited)

D. Koutsoyiannis, Rainfall disaggregation methods: Theory and applications (invited), Proceedings, Workshop on Statistical and Mathematical Methods for Hydrological Analysis, edited by D. Piccolo and L. Ubertini, Rome, 1–23, doi:10.13140/RG.2.1.2840.8564, Università di Roma "La Sapienza", 2003.



A large variety of disaggregation methods that have appeared in hydrological literature and used in hydrological applications are reviewed with emphasis in rainfall modelling. The general-purpose stochastic disaggregation models, which have been used at several applications including rainfall modelling but at time scales not finer than monthly, are summarised. The specialised models for rainfall disaggregation, in particular at fine time scales, are examined in more detail. A special disaggregation technique, which, instead of using simultaneously both coarser and finer time scales in one mathematical expression, couples two independent stochastic models, one at each time scale, is further analysed. Two examples of implementing this technique to fine scale rainfall disaggregation are given. In the first case the implementation results in a single variate rainfall disaggregation model (Hyetos) based on the Bartlett-Lewis process. In the second case it results in a multivariate rainfall disaggregation model (MuDRain). These two implementations are demonstrated with results from real world applications.

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See also: http://dx.doi.org/10.13140/RG.2.1.2840.8564

Our works that reference this work:

1. D. Koutsoyiannis, and A. Langousis, Precipitation, Treatise on Water Science, edited by P. Wilderer and S. Uhlenbrook, 2, 27–78, Academic Press, Oxford, 2011.
2. F. Lombardo, E. Volpi, D. Koutsoyiannis, and F. Serinaldi, A theoretically consistent stochastic cascade for temporal disaggregation of intermittent rainfall, Water Resources Research, 53 (6), 4586–4605, doi:10.1002/2017WR020529, 2017.
3. 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, 556, 980–992, doi:10.1016/j.jhydrol.2016.07.015, 2018.

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

1. Segond, M.-L., C. Onof and H.S. Wheater, Spatial-temporal disaggregation of daily rainfall from a generalized linear model, Journal of Hydrology, 331(3-4), 674-689, 2006.
2. 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.
3. Zhang, J., R.R. Murch, M.A. Ross, A.R. Ganguly and M. Nachabe, Evaluation of statistical rainfall disaggregation methods using rain-gauge information for West-Central Florida, Journal of Hydrologic Engineering, 13 (12), 1158-1169, 2008.
4. Knoesen, D., and J. Smithers, The development and assessment of a daily rainfall disaggregation model for South Africa, Hydrological Sciences Journal, 54(2), 217-233, 2009.
5. 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.
6. Duy, B. K., P. Archambeau, S. Erpicum, B. J. Dewals and M. Pirotton, Large scale hydrological modelling of drained impervious areas, Houille Blanche-Revue Internationale de l Eau, (5), 167-173, 2009.
7. Chen, J., F. P. Brissette and R. Leconte, A daily stochastic weather generator for preserving low-frequency of climate variability, Journal of Hydrology, 388 (3-4), 480-490, 2010.
8. Wang, L., C. Onof and C. Maksimovic, Reconstruction of sub-daily rainfall sequences using multinomial multiplicative cascades, Hydrol. Earth Syst. Sci. Discuss., 7, 5267-5297, doi:10.5194/hessd-7-5267-2010, 2010.
9. Licznar, P., T. G. Schmitt and D. E. Rupp, Distributions of microcanonical cascade weights of rainfall at small timescales, Acta Geophysica, 59 (5), 1013-1043, 2011.
10. 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.
11. #de la Llata, R., A system dynamics model to evaluate sustainability of water supply in a watershed, ANSS Proceedings of the 44th Annual Simulation Symposium, ISBN:1-930638-56-6, 167-174, 2011.
12. 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.
13. Hidayah, E., Implementing of temporal rainfall disaggregation model using Bayesian PAR1 model combined with adjusting and filtering procedure in Sampean catchments area, Journal of Applied Sciences Research, 8 (1), 314-320, 2012.
14. #Lu,Y., X. Qin and T. Xu, Statistical downscaling and disaggregation of rainfall using master-station-based approach, 6th International Perspective on Water Resources & The Environment, Izmir, Turkey, 2013.
15. Vormoor, K., and T. Skaugen, Temporal disaggregation of daily temperature and precipitation grid data for Norway, Journal of Hydrometeorology, 14, 989–999, 2013.
16. Bürger, G., M. Heistermann and A. Bronstert, Towards sub-daily rainfall disaggregation via Clausius-Clapeyron, Journal of Hydrometeorology, 15 (3), 1303-1311, 2014.
17. Licznar, P., C. De Michele and W. Adamowski, Precipitation variability within an urban monitoring network via microcanonical cascade generators, Hydrol. Earth Syst. Sci., 19 (1), 485-506, 2015.
18. #Shrestha, A., M. S. Babel and S. Weesakul, Integrated modelling of climate change and urban drainage, Managing Water Resources under Climate Uncertainty, Springer International Publishing, 89-103, 10.1007/978-3-319-10467-6_5, 2015.
19. 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.
20. Förster, K., F. Hanzer, B. Winter, T. Marke, and U. Strasser, An open-source MEteoroLOgical observation time series DISaggregation Tool (MELODIST v0.1.1), Geoscientific Model Development, 9, 2315-2333, doi:10.5194/gmd-9-2315-2016, 2016.
21. Shrestha, A., M. S. Babel, S. Weesakul, and Z. Vojinovic, Developing intensity–duration–frequency (IDF) curves under climate change uncertainty: The case of Bangkok, Thailand, Water, 9(2), 145, doi:10.3390/w9020145, 2017.
22. Li, X., A. Meshgi, X. Wang, J. Zhang, S. H. X. Tay, G. Pijcke, N. Manocha, M. Ong, M. T. Nguyen, and V. Babovic, Three resampling approaches based on method of fragments for daily-to-subdaily precipitation disaggregation, International Journal of Climatology, doi:10.1002/joc.5438, 2018.

Tagged under: Stochastic disaggregation, Rainfall models, Stochastics