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River discharge measurements

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


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.This study underscores the importance of local alpha coefficients over a constant global value in improving discharge estimation accuracy. Using simplified approaches, such the ANN here proposed, this is possible also without using expensive and not always usable instrumentations, such the ADCP. Although the analysis covered a wide range of cases, only small rivers with predominantly torrential and, in some cases, ephemeral flow regimes were considered. The roughness conditions examined are those that predominantly characterize Sicilian rivers, and the ADCP measurements were conducted prevalently under ordinary flow conditions.

Thus, while the findings align with other studies on different river types, they should be contextualized exclusively to rivers and conditions similar to those explored here. The same considerations should be extended to the suitability of the proposed ANN. Given the growing interest in contactless river monitoring methods, conducting similar analyses on other rives types would be highly beneficial to validate and expand the applicability of these results and the proposed ANN. This represents a promising direction for future research.

Velocity, acceleration, speed, momentum, displacement, vector, scalar, uniform motion, non-uniform motion, relative velocity, instantaneous velocity, average velocity, terminal velocity, angular velocity, tangential velocity, linear velocity, escape velocity, velocity-time graph, projectile motion, kinematics

#velocity, #speed, #momentum, #acceleration, #displacement, #vector, #scalar, #motion, #relativevelocity, #instantaneousvelocity, #averagevelocity, #terminalvelocity, #angularvelocity, #tangentialvelocity, #linearvelocity, #escapevelocity, #velocitytimegraph, #projectilemotion, #kinematics

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