New Index for Runoff Variability Analysis in Rainfall Driven Rivers in Southeastern United States
Publication: Journal of Hydrologic Engineering
Volume 24, Issue 12
Abstract
The process of river runoff confluence is highly complex and combines both tendency and chaos. Research on the temporal and spatial tendencies of river runoff variability can yield significant information to understand river systems. This paper aims to identify rainfall–runoff variability tendencies using a novel variability metric: the Mei-Wang Fluctuation (MWF) index, which estimates the variability as a compound measurement of quantitative variation (Qv) and contour variation (Cv) and has been successfully applied to quantify the variability of power output. Six rainfall rivers located in North Carolina and Texas with few reservoirs are chosen as the study case. The results show several novel tendencies of the runoff variability, including the following: (1) when the temporal resolution length decreases from 1 day to 1 month, the runoff variability indices, Cv and MWF, conspicuously decrease, while Qv declines slightly; (2) from a spatial perspective, when the drainage area rises, these indices increase, and notably, Qv increases in a linear pattern (sharply in a narrow river basin, slowly in a broad river basin); and (3) these spatial tendencies are further demonstrated by replacing the outflow of a reservoir with its inflow. Additionally, the linear tendency exhibited between Qv and drainage area calls for further exploration that could contribute to hydrological analysis.
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Acknowledgments
Xianxun Wang, Yadong Mei and Hao Wang work are supported by Joint Funds of the National Natural Science Foundation of China (U1865201), the China Postdoctoral Science Foundation (2016M600610), the Natural Science Youth Foundation of Hubei Province (ZRMS2017000495) and the National Natural Science Foundation of China (51479140). Edgar Virguez work is supported by the Alfred P. Sloan Foundation through the Duke University Energy Data Analytics Fellowship, the CEIBA Foundation through the Rodolfo Llinas Scholarship and the Center for Climate and Energy Decision Making (SES-0949710) funded by the National Science Foundation. Lihua Chen work is supported by the National Key Research and Development Program of China (2017YFC0405900), the National Natural Science Foundation of China (51669003) and the Guangxi Provincial Natural Science Foundation (AB16380284). The conclusions reached or positions taken by the researchers or other grantees represent the views of the grantees themselves and not those of the foundation or its trustees, officers, or staff.
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Journal of Hydrologic Engineering
Volume 24 • Issue 12 • December 2019
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©2019 American Society of Civil Engineers.
History
Received: Nov 13, 2018
Accepted: Apr 17, 2019
Published online: Oct 3, 2019
Published in print: Dec 1, 2019
Discussion open until: Mar 3, 2020
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