Abstract:Dissolved oxygen mass balance has been computed for different reaches of River Kali in western Uttar Pradesh (India) to obtain the reaeration coefficient K 2 . A total of 270 field data sets have been collected during the period from March 1999 to February 2000. Eleven most popular predictive equations, used for reaeration prediction and utilizing mean stream velocity, bed slope, flow depth, friction velocity and Froude number, have been tested for their applicability in the River Kali using data generated during field survey. The K 2 values computed from these predictive equations have been compared with the K 2 values observed from dissolved oxygen balance measurements in the field. The performance of predictive equations have been evaluated using error estimation, namely standard error (SE), normal mean error (NME), mean multiplicative error (MME) and correlation statistics. The equations developed by Smoot and by Cadwallader and McDonnell showed comparatively better results. Moreover, a refined predictive equation has been developed using a least-squares algorithm for the River Kali that minimizes error estimates and improves correlation between observed and computed reaeration coefficients.
Abstract:Different commonly used predictive equations for the reaeration rate coefficient (K 2 ) have been evaluated using 231 data sets obtained from the literature and 576 data sets measured at different reaches of the River Kali in western Uttar Pradesh, India. The data sets include stream/channel velocity, bed slope, flow depth, cross-sectional area and reaeration rate coefficient (K 2 ), obtained from the literature and generated during the field survey of River Kali, and were used to test the applicability of the predictive equations. The K 2 values computed from the predictive equations have been compared with the corresponding K 2 values measured in streams/channels. The performance of the predictive equations has been evaluated using different error estimation, namely standard error (SE), normal mean error (NME), mean multiplicative error (MME) and coefficient of determination (r 2 ). The results show that the reaeration rate equation developed by Parkhurst and Pomeroy yielded the best agreement, with the values of SE, NME, MME and r 2 as 33Ð387, 4Ð62, 3Ð58 and 0Ð95, respectively, for literature data sets (case 1) and 37Ð567, 3Ð57, 2Ð6 and 0Ð95, respectively, for all the data sets (literature data sets and River Kali data sets) (case 2). Further, to minimize error estimates and improve correlation between measured and computed reaeration rate coefficients, supplementary predictive equations have been developed based on Froude number criteria and a least-squares algorithm. The supplementary predictive equations have been verified using different error estimates and by comparing measured and computed reaeration rate coefficients for data sets not used in the development of the equations.
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