A supplementary approach for estimating reaeration rate coefficients

Abstract

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.

Dynamics of dissolved oxygen isotopic ratios: a transient model to quantify primary production, community respiration, and air–water exchange in aquatic ecosystems

“…In streams and rivers, k is often parameterised in terms of channel depth and water velocity (Moog and Jirka 1998). Despite dozens of equations that can be used to estimate k for different settings, modelcalculated k values can vary by orders of magnitude for the same system (Jha et al 2001(Jha et al , 2004McBride 2002). Finally, because dissolved O 2 saturation concentrations are strongly temperature dependent (Weiss 1970), changes in water temperature will also affect the direction and rate of G. Previous O 2 dual-and triple-isotope approaches have relied on empirical models to estimate k, and this variable remains the greatest source of uncertainty (Juranek and Quay 2005).…”

Critical appraisal of BOD and DO models applied to a highly polluted river in India

“…The slope of the line provides the values of the deoxygenation coefficient (K 1 ). The re-aeration coefficient (K 2 ) used for DO modelling was computed (Jha et al, 2004) …”

“…Another main process in the oxygen condition of streams is that of re-aeration (Moog & Jirka, 1998;Jha et al, 2001Jha et al, , 2004Jain & Jha, 2005), which is the uptake of oxygen across the water surface due to the turbulent motion of water and to molecular diffusion. This process reduces the "oxygen deficit" (D) of water, which is defined as the difference between saturation oxygen content and the actual dissolved oxygen level.…”

Comparing the stream re-aeration coefficient estimated from ANN and empirical models / Comparaison d'estimations par un RNA et par des modèles empiriques du coefficient de réaération en cours d'eau

“…For monitoring all the data, extensive field investigations were made during March 1999-February 2000 (Jha et al, 2001(Jha et al, , 2004. The water quality variables measured/analysed by standard methods (APHA, 1985) on different reaches of the River Kali are: dissolved oxygen (DO) of upstream, downstream and incoming tributaries; biochemical oxygen demand (BOD) of upstream, downstream, and incoming tributaries; photosynthesis by plants; respiration by plants; and water temperature.…”

“…Re-aeration is the dominant natural means by which a water body may recover dissolved oxygen (DO). Since the work of Streeter & Phelps (1925), a number of biochemical oxygen demand and dissolved oxygen (BOD-DO) models have been proposed (Bhargava, 1983;Thomann & Müller, 1987;Jolankai, 1997;Jha et al, 2001Jha et al, , 2004, in which the re-aeration coefficient is an essential input parameter. Appendix I explains the importance of the re-aeration coefficient in DO modelling.…”