Hydrologic Regionalization under Data Scarcity: Implications for Streamflow Prediction
Publication: Journal of Hydrologic Engineering
Volume 26, Issue 9
Abstract
Continuous streamflow prediction is crucial in many applications of water resources planning and management. However, streamflow prediction is challenging, particularly in data-scarce regions. This paper demonstrates an approach to regionalize the flow duration curve for predicting daily streamflow in the data-scare region of the central Himalayas. We developed a regression-based model to estimate streamflow at various segments of a flow duration curve by incorporating basin characteristics and climate variables. This study analyzes the sensitivities of proximity and characteristics between the donor (gauged) and receptor (ungauged) basins for time-series streamflow prediction. Our results show that regionalization techniques perform better in low to medium flows over high flows. Our findings are significant in the central Himalayan regional context to inform operational and management decisions in water sector projects like hydropower plants, which generally rely on low-to-medium streamflow information. Although the quantitative results are region-specific, the approach and insights are generalizable to the Himalayan region.
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Data Availability Statement
All or part of the data and code will be made available upon request to the corresponding author. We used freely available codes such as Matplotlib in Python and ggplot in R for data analysis and graph production.
Acknowledgments
We would like to acknowledge the Department of Hydrology and Meteorology (DHM) for providing the streamflow data.
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Journal of Hydrologic Engineering
Volume 26 • Issue 9 • September 2021
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© 2021 American Society of Civil Engineers.
History
Received: Feb 18, 2021
Accepted: May 21, 2021
Published online: Jul 7, 2021
Published in print: Sep 1, 2021
Discussion open until: Dec 7, 2021
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