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Discussion papers | Copyright
https://doi.org/10.5194/hess-2018-267
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 28 May 2018

Research article | 28 May 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

A Hybrid Stochastic Rainfall Model That Reproduces Rainfall Characteristics at Hourly through Yearly Time Scale

Jeongha Park1, Christian Onof2, and Dongkyun Kim1 Jeongha Park et al.
  • 1Department of Civil Engineering, Hongik University, Seoul, 04066, Republic of Korea
  • 2Department of Civil and Environmental Engineering, Imperial College, London, SW7 2AZ, UK

Abstract. A novel approach of stochastic rainfall generation that can reproduce various statistical characteristics of observed rainfall at hourly through yearly time scale is presented. The model uses the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model to generate monthly rainfall. Then, it downscales the generated monthly rainfall to the hourly aggregation level using the Modified Bartlett-Lewis Rectangular Pulse (MBLRP) model, a type of Poisson cluster rainfall model. Here, the MBLRP model is fine-tuned such that it can reproduce the fine-scale properties of observed rainfall. This was achieved by first generating a set of fine scale rainfall statistics reflecting the complex correlation structure between rainfall mean, variance, auto-covariance, and proportion of dry periods, and then coupling it to the generated monthly rainfall, which were used as the basis of the MBLRP parameters to downscale monthly rainfall. The approach was tested at the 29 gauges located in the Midwest to the East Coast of the Continental United States with a variety of rainfall characteristics. The results of the test suggest that our hybrid model accurately reproduces the first through the third order statistics as well as the intermittency properties from the hourly to the annual time scale; and the statistical behaviour of monthly maxima and extreme values of the observed rainfall was well reproduced as well.

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Latest update: 20 Aug 2018
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Short summary
Rainfall data is often not available for the analysis of the water related problems such as floods and droughts. In such cases, researchers and engineers use rainfall generators which can produce synthetic rainfall data. However, most rainfall generators can serve only one specific purpose. In other words, one rainfall generator cannot be applied to analyze both flood and drought. We invented a multi-purpose rainfall generator that can be applied to analyze most water related problems.
Rainfall data is often not available for the analysis of the water related problems such as...
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