Assessment of Reaeration Equations for River Tungabhadra, Karnataka, India and Generation of the Refined Equation

Abstract

The ability of a water body to self-purify itself is dependent on its re-aeration rate (Ka). This rate is necessary to calculate the dissolved oxygen content in the waterbody. This rate also depends on some variables that include the stream velocity, stream bed slope, cross section area, water depth, frictional velocity, discharge rate, Froude’s number and a number of other things. For the purpose of this study, thirteen empirical equations are considered when evaluating the performance of the re-aeration rates. This is done with respect to the size of the Tungabhadra river. Observation of the re-aeration rate for this study was done using mass balance approach. The data needed for this was gotten from field investigation data obtained from 288 separate samples (6 different sites) between the period March, 2017 to December, 2018. The performance evaluation of the re-aeration equation was done via the implementation of least square techniques. The following statistical error methods were applied in due course; standard error, normal mean method and mean multiplicative method. The results of the methods are 0.16, -0.0006 and 2.75. The coefficient of correlation for this was 0.91 and by interpretation, it shows an efficient outcome.

Numerical Modeling of Reaeration Coefficient for Lakes: A Case Study of Sawa Lake, Iraq

“…In addition, wind speed data at a specific height is required in order to determine the reaeration coefficient in lakes using such models. Ranjith et al (2019) used many empirical equations to calculate the reaeration rate coefficient of the Tungabhadra River, India, in order to evaluate the river dissolved oxygen. These equations are all related to stream variables, such as river slop, depth, and velocity.…”