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Sediment transport can be observed within the flow of water in rivers, canals, and coastal regions, encompassing both suspended-load transport and bed-load transport. Bed-load transport specifically occurs near the riverbed, playing a crucial role in the formation of the riverbed itself. The current study aimed to explore the process of sediment transport by employing the entropy concept as a theoretical approach. To this end, field data collected using a current meter in the Alaknanda River at Srinagar in India were utilized. A comparison was made between the calculated mean velocity and discharge values and the observed data obtained from the Central Water Commission (CWC), demonstrating a maximum error percentage of 9%. Subsequently, shear velocity was determined for various cross-sections under different flow scenarios. The Shields parameter was then derived from the shear-velocity distribution to evaluate the transport potential of the sediment particles. The model results showed varying bed-load transport rates that increased as the particle size decreased and the discharge rate increased. In conclusion, the study findings highlight the efficacy of utilizing the entropy theory for estimating flow discharge and sediment transport in developing countries. [ABSTRACT FROM AUTHOR]

This study implements an entropy theory-based approach to infer bathymetry for 29 selected cross-sections along a 1740 km reach of the Congo River. A genetic algorithm optimization approach is used based on an analysis of near-surface velocity measurements to generate a random sample of 1000 bathymetry profiles from which the analysis is carried out. The resulting simulated bathymetry shows good agreement compared to the measurements obtained via Accoustic Doppler Current Profiler (ADCP), with a correlation that varies from 0.49 to 0.88. The bathymetry results are subsequently used to estimate the two-dimensional cross-sectional flow velocity distribution and, consequently, to calculate the river discharge. The mean errors observed for flow area, discharge, and mean velocity are found to be 2.7%, 1.3%, and 1%, respectively. This study demonstrates, for the first time, the successful application of an entropy-based approach to estimate bathymetry and discharge in large rivers and has significant implications for remote sensing applications. [ABSTRACT FROM AUTHOR]

Estimating the flow velocity and discharge in rivers is of particular interest for monitoring, modeling, and research purposes. Instruments for measuring water level and surface velocity are generally mounted on bridge decks, and this poses a challenge because the bridge structure, with piers and abutments, can perturb the flow field. The current research aims to investigate the applicability of entropy theory to estimate the velocity distribution and the discharge in the vicinity of river bridges. For this purpose, a computational fluid dynamics (CFD) model is used to obtain three-dimensional flow fields along a stretch of the Paglia River (central Italy), where a historical multi-arch bridge strongly affects flood flows. The input data for the entropy model include the cross-sectional bathymetry and the surface velocity provided by the numerical simulations. A total of 12 samples, including three different flow conditions at four cross-sections, one upstream and three downstream of the bridge, are considered. It is found that the entropy model can be reliably applied upstream of the bridge, also when forced with a single (i.e., the maximum) value of the surface velocity, with errors on total discharge below 13 % in the considered case. By contrast, downstream of the bridge, the wakes generated by the bridge piers strongly affect the velocity distribution, both in the spanwise and in the vertical directions and for very long distances. Here, notwithstanding the complex and multimodal spanwise distribution of flow velocity, the entropy model estimates the discharge with error lower than 8 % if forced with the river-wide distribution of the surface velocity. The present study has important implications for the optimal positioning of sensors and suggests the potential of using CFD modeling and entropy theory jointly to foster greater knowledge of river systems. [ABSTRACT FROM AUTHOR]

The present work concerns the interaction between hydraulic processes and biological communities in rivers. In particular, the aim of this study is to investigate the interactions between flow dynamics and the freshwater mussels (FMs) to verify if the mussels' behavioural response to the hydrodynamic stress could be used to monitor natural extreme events in rivers. Although the influence of mussels on the kinematic characteristics of flow at the water–sediment interface was investigated by a certain number of studies, their behavioural response to flow, both in static and dynamic conditions, remains understudied. Laboratory experiments were performed in an artificial flume exposing Unio elongatulus to different values of flow discharge, both in steady and in unsteady conditions either with or without sediment transport. Mussels' behavioural responses were detected by using Hall sensor technology to measure gaping frequency, amplitude and duration, both in static conditions and under the effect of hydrodynamic stresses. Five categories of behavioural response were identified: Normal Activity (NA), Resting (Re), Transition (Tr), Adaptation (Ad) and Avoidance (Av). During NA (standard feeding and moving), FMs presented valve gaping, while during Re valves were kept constantly opened for water filtration. After a variation of flow discharge (ΔQ), FMs promptly reacted showing a transition from their normal behaviour, with constant gaping frequency (below 0.01 Hz), to higher valve gaping frequencies. The mean valves' gaping frequency increased as a function of ΔQ, and the highest values were reached in the presence of sediment transport. The mean valve opening amplitude was less sensitive to ΔQ. Its range of variation was very narrow with the highest values corresponding to the protrusion/retraction of the animals' foot to move or anchor to the substrate. The percentage of mussels responding to the discharge variation (Transition behaviour) increases with ΔQ confirming that mussels' behavioural response represents a promising tool for monitoring the occurrence of hydrodynamic stressors in fluvial systems. [ABSTRACT FROM AUTHOR]

Quantifying the effects of external climatic and anthropogenic stressors on aquatic ecosystems is an important task for scientific purposes and management progress in the field of water resources. In this study, we propose an innovative use of biotic communities as real-time indicators, which offers a promising solution to directly quantify the impact of these external stressors on the aquatic ecosystem health. Specifically, we investigated the influence of natural river floods on riverine biotic communities using freshwater mussels (FMs) as reliable biosensors. Using the valvometry technique, we monitored the valve gaping of FMs and analysed both the amplitude and frequency. The valve movement of the FMs was tracked by installing a magnet on one valve and a Hall effect sensor on the other valve. The magnetic field between the magnet and the sensor was recorded using an Arduino board, and its changes over time were normalised to give the opening percentage of the FMs (how open the mussels were). The recorded data were then analysed using continuous wavelet transform (CWT) analysis to study the time-dependent frequency of the signals. The experiments were carried out both in a laboratory flume and in the Paglia River (Italy). The laboratory experiments were conducted with FMs in two configurations: freely moving on the bed and immobilised on vertical rods. Testing of the immobilised configuration was necessary because the same configuration was used in the field in order to prevent FMs from packing against the downstream wall of the protection cage during floods or from breaking their connection wires. These experiments allowed us to verify that immobilised mussels show similar responses to abrupt changes in flow conditions as free mussels. Moreover, immobilised mussels produced more neat and interpretable signals than free-moving mussels due to the reduced number of features resulting from movement constraints. We then analysed the response of 13 immobilised mussels under real river conditions during a flood on 31 March 2022. The FMs in the field showed a rapid and significant change in valve gap frequency as the flood escalated, confirming the general behaviour observed in the laboratory in the presence of an abrupt increase in the flow. These results highlight the effectiveness of using FMs as biosensors for the timely detection of environmental stressors related to natural floods and emphasise the utility of CWT as a powerful signal-processing tool for the analysis of valvometry data. The study proposes the integration of FM valvometry and CWT for the development of operational real-time biological early-warning systems (BEWSs) with the aim of monitoring and protecting aquatic ecosystems. Future research should focus on extending the investigation of the responsiveness of FMs to specific stressors (e.g. turbidity, temperature, and chemicals) and on testing the applications of the proposed BEWSs to quantify the impact of both natural stressors (e.g. heat waves and droughts) and anthropogenic stressors (e.g. hydropeaking, reservoir flushing, and chemical contamination). [ABSTRACT FROM AUTHOR]

Estimating the flow velocity and discharge in rivers is of particular interest for monitoring, modelling, and research purposes. Instruments for measuring water level and surface velocity are generally mounted on bridge decks, and this poses a challenge because the bridge structure (e.g., piers and abutments) can lead to perturbated flow fields. The current research aims to investigate the applicability of the entropy theory to estimate the velocity distribution and the discharge in the vicinity of river bridges. To this purpose, a Computational Fluid Dynamics (CFD) model is used to obtain threedimensional flow fields along a stretch of the Paglia River (central Italy), where a historical multi-arch bridge strongly affects flood flows. The input data for the entropy model include the cross-sectional bathymetry and the surface velocity provided by the numerical simulations. Different flow conditions and cross-sections, either upstream and downstream of the bridge, are considered. It is found that the entropy model can be applied safely upstream of the bridge, also when forced with a single (i.e., the maximum) value of the surface velocity, with errors on total discharge below 13% in the considered case. On the contrary, downstream the bridge, the wakes generated by the bridge piers strongly affect the velocity distribution, both in the spanwise and in the vertical directions, and for very long distances. Here, notwithstanding the complex and multimodal spanwise distribution of flow velocity, the entropy model estimates the discharge with error lower than 8% if forced with the river-wide distribution of the surface velocity. The present study has important implications for the optimal positioning of sensors and suggest the potential of using CFD modelling and entropy theory jointly to foster the knowledge of river systems. [ABSTRACT FROM AUTHOR]

The aim of this study is to investigate the impact of natural river floods on biotic communities. To this purpose, we used freshwater mussels (FMs), recognized as one of the most reliable bioindicators in aquatic environments. A well-established valvometry technique was applied to measure the FMs valve gaping behaviour, considering both gaping amplitude and frequency. The mussels have been employed in two distinct configurations, either free to move or stuck on vertical bars. We performed experiments in a laboratory flume and in the Paglia River (Italy). The FMs valve gaping movement was first recorded, then the continuous wavelet transform (CWT) analysis was applied to the signals to get the time-dependent frequency of the signals. Laboratory experiments allowed to assess to what extent stuck mussels react differently than free mussels to abrupt increases in flow conditions. Subsequently, we examined the response of thirteen stuck mussels installed in real riverine conditions during a moderate flood occurred on March 31, 2022, with a rapid increase of the water level. The experimental results demonstrate that stuck mussels produce signals that are more consistent and easier to interpret compared to free mussels, primarily due to the reduced number of features resulting from movement constraints. The stuck mussels in the field showed a sharp and timely change of valve gaping frequency as the flood ramped up, thus confirming the findings in the laboratory. The results highlight the effectiveness of using FMs as bioindicators for assessing the impact of floods on the aquatic ecosystem, and the utility of CWT as a suitable signal processing tool for analyzing valvometric time series. These findings provide a pathway towards the integration of FMs valvometry and CWT for the development of operational real-time Biological Early Warning Systems (BEWS) aimed at the monitoring and safeguarding of aquatic ecosystems. [ABSTRACT FROM AUTHOR]

Rain gauges, weather radars, satellite sensors and modelled data from weather centres are used operationally for estimating the spatial-temporal variability of rainfall. However, the associated uncertainties can be very high, especially in poorly equipped regions of the world. Very recently, an innovative method, named SM2RAIN, that uses soil moisture observations to infer rainfall, has been proposed by Brocca et al. (2013) with very promising results when applied with in situ and satellite-derived data. However, a thorough analysis of the physical consistency of the SM2RAIN algorithm has not been carried out yet. In this study, synthetic soil moisture data generated from a physically-based soil water balance model are employed to check the reliability of the assumptions made in the SM2RAIN algorithm. Next, high quality and multiyear in situ soil moisture observations, at different depths (5-30 cm), and rainfall for ten sites across Europe are used for testing the performance of the algorithm, its limitations and applicability range.