Disaggregation the Daily Rainfall Dataset into Sub-Daily Resolution in the Temperate Oceanic Climate Region
High resolution rain data are very important to fulfill the input of hydrological models. Among models of high-resolution rainfall data generation, the temporal disaggregation was chosen for this study. The paper attempts to generate three different rainfall resolutions (4-hourly, hourly and 10-minutes) from daily for around 20-year record period. The process was done by DiMoN tool which is based on random cascade model and method of fragment. Differences between observed and simulated rain dataset are evaluated with variety of statistical and empirical methods: Kolmogorov-Smirnov test (K-S), usual statistics, and Exceedance probability. The tool worked well at preserving the daily rainfall values in wet days, however, the generated data are cumulated in a shorter time period and made stronger storms. It is demonstrated that the difference between generated and observed cumulative distribution function curve of 4-hourly datasets is passed the K-S test criteria while in hourly and 10-minutes datasets the P-value should be employed to prove that their differences were reasonable. The results are encouraging considering the overestimation of generated high-resolution rainfall data.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.3607811Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 585
 Ahamed, S., Piantadosi, J., Agrawal, M. and Boland, J., 2013. Generating synthetic rainfall using a disaggregation model (Doctoral dissertation, Modelling and Simulation Society of Australia and New Zealand).
 Gaume, E., Mouhous, N. and Andrieu, H., 2007. Rainfall stochastic disaggregation models: Calibration and validation of a multiplicative cascade model. Advances in Water Resources, 30(5), pp.1301-1319.
 Güntner, A., Olsson, J., Calver, A. and Gannon, B., 2001. Cascade-based disaggregation of continuous rainfall time series: the influence of climate. Hydrology and Earth System Sciences Discussions, 5(2), pp.145-164.
 Gyasi-Agyei, Y. and Mahbub, S. P. B., 2007. A stochastic model for daily rainfall disaggregation into fine time scale for a large region. Journal of Hydrology, 347(3-4), pp.358-370.
 Hanaish, I. S., Ibrahim, K. and Jemain, A. A., 2011. Daily rainfall disaggregation using HYETOS model for Peninsular Malaysia. matrix, 2, p.1.
 Kilsby, C. G., Jones, P. D., Burton, A., Ford, A. C., Fowler, H. J., Harpham, C., James, P., Smith, A. and Wilby, R.L., 2007. A daily weather generator for use in climate change studies. Environmental Modelling & Software, 22(12), pp.1705-1719.
 Koutsoyiannis, D., 2003, May. Rainfall disaggregation methods: Theory and applications. In Workshop on Statistical and Mathematical Methods for Hydrological Analysis, Rome (Vol. 5270, pp. 1-23).
 Licznar, P., Łomotowski, J. and Rupp, D. E., 2011. Random cascade driven rainfall disaggregation for urban hydrology: An evaluation of six models and a new generator. Atmospheric Research, 99(3-4), pp.563-578.
 Lisniak, D., Franke, J. and Bernhofer, C., 2013. Circulation pattern based parameterization of a multiplicative random cascade for disaggregation of observed and projected daily rainfall time series. Hydrology and Earth System Sciences, 17(7), pp.2487-2500.
 Mansell, M. G., 2003. Rural and urban hydrology. Thomas Telford.
 Molnar, P. and Burlando, P., 2005. Preservation of rainfall properties in stochastic disaggregation by a simple random cascade model. Atmospheric Research, 77(1-4), pp.137-151.
 Nathan, R., Jordan, P., Scorah, M., Lang, S., Kuczera, G., Schaefer, M. and Weinmann, E., 2016. Estimating the exceedance probability of extreme rainfalls up to the probable maximum precipitation. Journal of hydrology, 543, pp.706-720.
 Olsson, J., 1998. Evaluation of a scaling cascade model for temporal rain-fall disaggregation. Hydrology and Earth System Sciences Discussions, 2(1), pp.19-30.
 Sharma, A. and Srikanthan, S., 2006. Continuous rainfall simulation: A nonparametric alternative. In 30th Hydrology & Water Resources Symposium: Past, Present & Future (p. 86). Conference Design.
 Valencia R. D., Schaake Jr. J. C., 1973. Disaggregation Processes in Stochastic Hydrology. Water Resources Research, 9(3):580-585.