A simplified method for estimating the alpha coefficient in surface velocity based river discharge measurements

• Coeff. α converts surface velocities into depth-averaged values in river monitoring. • ADCP data in Sicily showed significant deviations of empirical α from standard α* • An ANN was developed to predict α from easily measurable geometric variables. • The ANN was particularly performing where vertical vel. profiles follow power laws. • Tested with LS-PIV, the ANN outperformed methods based on α* and site-specific α. Remote sensing techniques for river monitoring facilitate faster measurement campaigns compared to traditional methods, reduce risks to personnel and instruments, and allow measurements under critical flow conditions. An alpha coefficient (α) is commonly employed to convert surface velocities, obtained by contactless techniques, into depth-averaged velocities, which are used for the application of the velocity-area method for assessing discharge. Some optical-based software programs use a constant α value, based on a theoretical “standard”. However, analyses of empirical vertical velocity profiles in real cases reveal that α can significantly deviate from this standard due to various factors (roughness, turbulence, etc.). This study analyzes several ADCP (Acoustic Doppler Current Profiler)-based measurements in Sicily, Italy, to explore factors influencing flow velocity distribution and potential errors from using the standard α for discharge estimation via surface velocity-based methods. The results confirmed substantial variability in α, which is functionally related to some geometric factors characterizing the cross-section shape and the specific vertical where the velocity profile is computed. The generated dataset of empirical α values is also used to implement an Artificial Neural Network (ANN), offering a straightforward tool suitable for non-contact techniques. The ANN predicts α at any vertical of a measurement transect as a function of variables however necessary for discharge assessment by non-intrusive methods, leading to depth-averaged velocity estimates from surface velocities that are more accurate than those derived from conventional approaches, as demonstrated by four test cases.