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Applications to River Flow Data

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Chaos in Hydrology

Abstract

Chaos theory has found widespread applications in studies on river flow data. Indeed, river flow is the most studied data in the context of chaos studies in hydrology. Early applications mainly focused on identification and prediction of chaotic behavior in river flow dynamics. Later years witnessed studies on a wide range of problems associated with river flow, including scaling and disaggregation, missing data estimation, reconstruction of system equations, multivariable analysis, and spatial variability and classification. Many studies have also addressed the important issues in the applications of chaos theory to river flow data, including data size, data noise, and selection of parameters involved in chaos identification and prediction methods. This chapter presents a review of applications of chaos theory to river flow data. The studies are roughly grouped into three categories to represent the following aspects: identification and prediction, scaling and disaggregation, and spatial variability and classification. These applications are also illustrated through examples.

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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 River Flow Data. In: Chaos in Hydrology. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-2552-4_10

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