Search engine for discovering works of Art, research articles, and books related to Art and Culture
ShareThis
Javascript must be enabled to continue!

Entropy-based depth-averaged velocity assessment from surface flow velocity

View through CrossRef
Discharge estimation at a river site depends on local hydraulic conditions identified by recording water levels. In fact, stage monitoring is straightforward and relatively inexpensive compared with the cost necessary to carry out flow velocity measurements which are, however, limited to low flows and constrained by the accessibility of the site. In this context, the mean flow velocity is hard to estimate for high flow, affecting de-facto the reliability of discharge assessment for extreme events. On the other hand, the surface flow velocity can be easily monitored by using radar sensors allowing to achieve a good estimate of discharge by exploiting the entropy theory applied to rivers hydraulic (Chiu,1987). The growing interest towards the use of no-contact methods to estimate discharge (Tauro et al., 2018) in field applications has shown that the cross-track velocity distribution can be inferred with sufficient accuracy using the surface velocities, usurf, sampled using Surface Velocity Radars (SVR) (Fulton and Ostrowski, 2008; Moramarco et al., 2017, Alimenti et al. 2020), the quantitative imaging techniques as LSPIV (Fujita et al., 1998) or PTV (Tauro et al., 2019). In this context, overall the velocity-area method is applied to estimate the mean flow velocity starting from the depth-averaged velocity, uvert, which is inferred through the velocity index, k=uvert/usurf.. For many river gage sites configurations, k has been set to 0.85. However, considering k refers to a monotonous velocity profile, not taking account of dip phenomena, the application may fail in estimating the depth-averaged velocity (Moramarco et al., 2017; Koussis et al., 2022, Pumo et al., 2025). Based on that, this work proposes a new entropy-based approach to estimate the depth-averaged velocity starting from the measured surface velocity retrieved by conventional and/or no-contact measurements. The approach exploits the dependence of the entropy parameter M with the hydraulic and geometric characteristics of channel (Moramarco and Dingmann, 2017), allowing to derive formulations on Manning’s roughness, shear velocity and water surface slope. Based on these features, the entropy-based method by using the measured surface velocity and the geometry of the river site is able to turn usurf  into uvert considering for each  usurf  an index which depends on the local water surface slope. The application to river sites along the Tiber River, Po River and Amazon River has shown the effectiveness of the approach in estimating the depth-averaged velocities with a fair accuracy along all verticals. Therefore, the method well lends itself to be integrated in the field of no-contact streamflow measurements.  
Copernicus GmbH
Title: Entropy-based depth-averaged velocity assessment from surface flow velocity
Description:
Discharge estimation at a river site depends on local hydraulic conditions identified by recording water levels.
In fact, stage monitoring is straightforward and relatively inexpensive compared with the cost necessary to carry out flow velocity measurements which are, however, limited to low flows and constrained by the accessibility of the site.
In this context, the mean flow velocity is hard to estimate for high flow, affecting de-facto the reliability of discharge assessment for extreme events.
On the other hand, the surface flow velocity can be easily monitored by using radar sensors allowing to achieve a good estimate of discharge by exploiting the entropy theory applied to rivers hydraulic (Chiu,1987).
The growing interest towards the use of no-contact methods to estimate discharge (Tauro et al.
, 2018) in field applications has shown that the cross-track velocity distribution can be inferred with sufficient accuracy using the surface velocities, usurf, sampled using Surface Velocity Radars (SVR) (Fulton and Ostrowski, 2008; Moramarco et al.
, 2017, Alimenti et al.
2020), the quantitative imaging techniques as LSPIV (Fujita et al.
, 1998) or PTV (Tauro et al.
, 2019).
In this context, overall the velocity-area method is applied to estimate the mean flow velocity starting from the depth-averaged velocity, uvert, which is inferred through the velocity index, k=uvert/usurf.
For many river gage sites configurations, k has been set to 0.
85.
However, considering k refers to a monotonous velocity profile, not taking account of dip phenomena, the application may fail in estimating the depth-averaged velocity (Moramarco et al.
, 2017; Koussis et al.
, 2022, Pumo et al.
, 2025).
Based on that, this work proposes a new entropy-based approach to estimate the depth-averaged velocity starting from the measured surface velocity retrieved by conventional and/or no-contact measurements.
The approach exploits the dependence of the entropy parameter M with the hydraulic and geometric characteristics of channel (Moramarco and Dingmann, 2017), allowing to derive formulations on Manning’s roughness, shear velocity and water surface slope.
Based on these features, the entropy-based method by using the measured surface velocity and the geometry of the river site is able to turn usurf  into uvert considering for each  usurf  an index which depends on the local water surface slope.
The application to river sites along the Tiber River, Po River and Amazon River has shown the effectiveness of the approach in estimating the depth-averaged velocities with a fair accuracy along all verticals.
Therefore, the method well lends itself to be integrated in the field of no-contact streamflow measurements.
  .

Related Results

Explaining the entropy concept and entropy components
Explaining the entropy concept and entropy components
Total entropy of a thermodynamic system consists of two components: thermal entropy due to energy, and residual entropy due to molecular orientation. In this article, a three-ste...
Numerical Study on Entropy Generation of the Multi-Stage Centrifugal Pump
Numerical Study on Entropy Generation of the Multi-Stage Centrifugal Pump
The energy loss of the multi-stage centrifugal pump was investigated by numerical analysis using the entropy generation method with the RNG k-ε turbulence model. Entropy generation...
A Generalized Measure of Cumulative Residual Entropy
A Generalized Measure of Cumulative Residual Entropy
In this work, we introduce a generalized measure of cumulative residual entropy and study its properties. We show that several existing measures of entropy, such as cumulative resi...
Hydrological Analysis of Stream Flow, Velocity, and Cross-Sectional Dynamics of River Benue at Jimata Bridge
Hydrological Analysis of Stream Flow, Velocity, and Cross-Sectional Dynamics of River Benue at Jimata Bridge
This study examines the stream turbulence, stream power, and cross-sectional dynamics of the River Benue at Jimeta Bridge over three dates: May 12, 2024, July 27, 2024, and August ...
Effects of Flow Depth and Sediment Size on Near Bed Hydraulics and Sediment Mobility in Open Channel Flow
Effects of Flow Depth and Sediment Size on Near Bed Hydraulics and Sediment Mobility in Open Channel Flow
Accurate prediction of sediment mobility in open channel flows is essential for effective river engineering and sediment management. This study examines the combined influence of f...
Entropy and Wealth
Entropy and Wealth
While entropy was introduced in the second half of the 19th century in the international vocabulary as a scientific term, in the 20th century it became common in colloquial use. Po...

Back to Top