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A cascade approach to continuous rainfall data generation at point locations

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Abstract

High-resolution temporal rainfall data sequences serve as inputs for a range of applications in planning, design and management of small (especially urban) water resources systems, including continuous flow simulation and evaluation of alternate policies for environmental impact assessment. However, such data are often not available, since their measurements are costly and time-consuming. One alternative to obtain high-resolution data is to try to derive them from available low-resolution information through a disaggregation procedure. This study evaluates a random cascade approach for generation of high-resolution rainfall data at a point location. The approach is based on the concept of scaling in rainfall, or, relating the properties associated with the rainfall process at one temporal scale to a finer-resolution scale. The procedure involves two steps: (1) identification of the presence of scaling behavior in the rainfall process; and (2) generation of synthetic data possessing same/similar scaling properties of the observed rainfall data. The scaling identification is made using a statistical moment scaling function, and the log–Poisson distribution is assumed to generate the synthetic rainfall data. The effectiveness of the approach is tested on the rainfall data observed at the Sydney Observatory Hill, Sydney, Australia. Rainfall data corresponding to four different successively doubled resolutions (daily, 12, 6, and 3 h) are studied, and disaggregation of data is attempted only between these successively doubled resolutions. The results indicate the presence of multi-scaling behavior in the rainfall data. The synthetic data generated using the log–Poisson distribution are found to exhibit scaling behaviors that match very well with that for the observed data. However, the results also indicate that fitting the scaling function alone does not necessarily mean reproducing the broader attributes that characterize the data. This observation clearly points out the extreme caution needed in the application of the existing methods for identification of scaling in rainfall, especially since such methods are also prevalent in studies of the emerging satellite observations and thus in the broader spectrum of hydrologic modeling.

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Acknowledgments

This research was funded by the Australian Research Council. Their support is gratefully acknowledged. The authors would like to thank Faisal Hossain and an anonymous reviewer for their positive comments and useful suggestions on an earlier version of this manuscript.

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Authors and Affiliations

  1. Department of Land, Air and Water Resources, University of California, Davis, CA, 95616, USA

    Bellie Sivakumar

  2. School of Civil and Environmental Engineering, The University of New South Wales, Sydney, NSW, 2052, Australia

    Ashish Sharma

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  1. Bellie Sivakumar
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  2. Ashish Sharma
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Correspondence to Bellie Sivakumar.

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Sivakumar, B., Sharma, A. A cascade approach to continuous rainfall data generation at point locations. Stoch Environ Res Risk Assess 22, 451–459 (2008). https://doi.org/10.1007/s00477-007-0145-y

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