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Applications to Rainfall Data

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Chaos in Hydrology

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Abstract

Initial applications of the ideas of chaos theory in hydrology were on rainfall data. Early studies essentially addressed the identification and prediction of chaotic behavior of rainfall data. Encouraging outcomes from these studies subsequently led to investigations on the chaotic nature of scaling relationships in rainfall and disaggregation of data, including development of a new chaotic approach for rainfall disaggregation. More recently, some studies have examined the spatial variability and classification of rainfall. In addition to these, a number of studies have also addressed the important methodological and data issues in the applications of chaos methods to rainfall data. This chapter presents a review of chaos studies on rainfall data. The presentation is organized into three parts to address three important problems associated with rainfall: identification and prediction of chaos, scaling and disaggregation, and spatial variability and classification. An example is presented for each of these to demonstrate the utility and effectiveness of chaos concepts and methods to study these different problems.

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Authors and Affiliations

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

    Bellie Sivakumar

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

    Bellie Sivakumar

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  1. Bellie Sivakumar
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Sivakumar, B. (2017). Applications to Rainfall Data. In: Chaos in Hydrology. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-2552-4_9

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