Recession curve power-law exponent estimation: is there a perfect approach?

Sharma, Durga and Biswal, Basudev (2022) Recession curve power-law exponent estimation: is there a perfect approach? Hydrological Sciences Journal, 67 (8). pp. 1228-1237. ISSN 0262-6667

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Abstract

The coefficient (k) of the (Formula presented.) (discharge at time t) power-law relationship is typically computed after fixing the value of the exponent (α). However, recession analysis poses the challenge of finding a suitable α for a basin. Although many recent studies have suggested considering the median of the α distribution as the representative value, a purely logical reasoning for this has not been yet provided. In this study, we argue that there is no perfect approach to estimate α and that it should depend on the final objective. Recession flow prediction is considered the objective in this study. We employ a model to predict recession discharge for 408 USGS (United States Geological Survey), basins for a wide range of α values. Considering Nash-Sutcliffe efficiency as the indicator of model performance, we observed that the optimum value of α is substantially lower than the α median for most of the basins. Overall, our study establishes that there is no single value of (Formula presented.) preferable for all practical purposes. © 2022 IAHS.

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IITH Creators:

IITH Creators	ORCiD

Item Type:

Article

Additional Information:

The authors thank the Ministry of Human Resource Development (MHRD), India, for funding Durga Sharma’s doctoral research. We thank all the anonymous reviewers for providing very valuable comments and suggestions, leading to a significant improvement of the paper. We also thank Clément Roques for providing the source code for the exponential time step method.

Uncontrolled Keywords:

Brutsaert-Nieber equation; discharge prediction; individual recession curve analysis; power-law relationships; recession curve parameter estimation

Subjects:

Civil Engineering

Divisions:

Department of Civil Engineering

Depositing User:

. LibTrainee 2021

Date Deposited:

16 Jul 2022 05:17

Last Modified:

16 Jul 2022 05:17