Novel Data-Driven Approach for Development of Synthetic Hyetograph
Publication: Journal of Hydrologic Engineering
Volume 24, Issue 10
Abstract
Simulation based flood risk or rainfall-runoff studies require multiple extreme rainfall sequences as input, for which synthetic hyetographs are generally employed. Temporal distribution for synthetic hyetograph can be generated by deterministic approaches (geometric shapes, standard temporal distribution) or stochastic approaches/weather generators. The pertinent limitations are the missing inherent randomness of rainfall, limited numbers (former), and/or complexity in implementation (latter). Addressing these limitations, a novel data-driven nonparametric approach is proposed in this article for extraction of a huge number of temporal patterns of rainfall from observations. When compared to stochastic approaches, generation of synthetic hyetographs is easier in the proposed approach. Synthetic hyetographs generated in this approach represented the observed storms better than other deterministic approaches from literature.
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Data Availability Statement
All data used in the study are confidential in nature and were available to the author only for research purposes with restrictions. The author is not authorized to share the data.
Acknowledgments
The author gratefully acknowledges the agencies for providing the data for the study. The author sincerely appreciates the significant contribution from the unknown learned reviewers and editors for providing guidance and critical insightful observations/suggestions, which immensely helped in improving the technical content and clarity of this article.
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Published In
Journal of Hydrologic Engineering
Volume 24 • Issue 10 • October 2019
Copyright
©2019 American Society of Civil Engineers.
History
Received: Sep 3, 2018
Accepted: May 25, 2019
Published online: Aug 5, 2019
Published in print: Oct 1, 2019
Discussion open until: Jan 5, 2020
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