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Surging debris flows are among the most destructive natural hazards, and elucidating the interaction between coarse‐grained fronts and the trailing liquefied slurry is key to understanding these flows. Here, we describe the application of high‐resolution and high‐frequency 3D LiDAR data to explore the dynamics of a debris flow at Illgraben, Switzerland. The LiDAR measurements facilitate automated detection of features on the flow surface, and construction of the 3D flow depth and velocity fields through time. Measured surface velocities (2–3 m s−1) are faster than front velocities (0.8–2 m s−1), illustrating the mechanism whereby the flow front is maintained along the channel. Further, we interpret the relative velocity of different particles to infer that the vertical velocity profile varies between plug flow and one that features internal shear. Our measurements provide unique insights into debris‐flow motion, and provide the foundation for a more detailed understanding of these hazardous events. Plain Language Summary Debris flows are surging flows of soil, wood, and water that can impact people and infrastructure far downstream of their initiation zone. Work by others has identified that debris flows tend to develop a distinct segregation between large particles concentrated at the front of the flow and small particles at the tail; however, the formation process and implications of this for debris‐flow motion have remained vague. In this work, we present measurements from laser scanners, originally developed for autonomous vehicles, that provide insight into this process. The scanners provide 10 scans per second, which can be used to measure the velocity of objects (rocks and woody debris) on the surface of the flow. We show that different objects in the flow move at different speeds, which results in many destructive features of debris flows. These measurements, and the resulting process understanding, are important for predicting debris‐flow hazard and reducing the associated risk. Key Points High‐resolution 3D LiDAR scans at subsecond intervals demonstrate a novel method for exploring debris‐flow dynamics LiDAR‐derived front and surface velocities allow identification of processes forming and maintaining the debris‐flow front Observations of individual particle motion place constraints on the vertical velocity profile and temporal variation in flow regimes

The vertical distribution of flow velocity plays a critical role in sediment transport and nutrient circulation within mangrove forests, characterized by their intricate root structures. Existing velocity profile models typically rely on a single mixing length scale, limiting their ability to capture the energy distribution of turbulent eddies across multiple scales. To address this limitation, we propose a turbulence model based on the eddy energetics that incorporates multiple length scales, resolving both local and non‐local effects. The model closes the shear stress term through a co‐spectral density function, employing a modified spectral linear Rotta scheme to account for the pressure‐redistribution effect. It integrates all relevant characteristic scales by leveraging a well‐established vertical velocity spectrum that encompasses eddies from the energy‐containing and inertial ranges. Validation of the model through numerical solutions shows excellent agreement with data from both flume experiments and field observations. Additionally, an approximate analytical solution is derived under simplified conditions, which reduces to the mixing length model under restricted scenarios. These findings offer a fresh model for characterizing the hydrodynamic impact of mangrove prop roots on turbulence, with implications for understanding material mixing and transport processes in mangroves.

This study aimed (i) to explore the relationship between vertical (jumping) and horizontal (sprinting) force–velocity–power (FVP) mechanical profiles in a large range of sports and levels of practice, and (ii) to provide a large database to serve as a reference of the FVP profile for all sports and levels tested. A total of 553 participants (333 men, 220 women) from 14 sport disciplines and all levels of practice participated in this study. Participants performed squat jumps (SJ) against multiple external loads (vertical) and linear 30–40 m sprints (horizontal). The vertical and horizontal FVP profile (i.e., theoretical maximal values of force ( F 0 ), velocity ( v 0 ), and power ( P max )) as well as main performance variables (unloaded SJ height in jumping and 20-m sprint time) were measured. Correlations coefficient between the same mechanical variables obtained from the vertical and horizontal modalities ranged from −0.12 to 0.58 for F 0 , −0.31 to 0.71 for v 0 , −0.10 to 0.67 for P max , and −0.92 to −0.23 for the performance variables (i.e, SJ height and sprint time). Overall, results showed a decrease in the magnitude of the correlations for higher-level athletes. The low correlations generally observed between jumping and sprinting mechanical outputs suggest that both tasks provide distinctive information regarding the FVP profile of lower-body muscles. Therefore, we recommend the assessment of the FVP profile both in jumping and sprinting to gain a deeper insight into the maximal mechanical capacities of lower-body muscles, especially at high and elite levels.

To solve the problem of SVP (Sound velocity profiles) representative error caused by the difficulty of obtaining continuous time series SVP in deep-sea operations, an SVP timing prediction method combining EMD (Empirical mode decomposition) and NARX (Nonlinear autoregressive neural network with external input) is proposed. To begin with, the time-series SVP are stratified according to different depths, and the time-series variation curves of sound velocity at different depths are obtained; Furthermore, the EMD is used to decompose the time series variation curve of sound velocity into multiple IMF (Intrinsic mode function) components, each component contains local characteristic signals of different time scales of the original signal. The NARX is used to establish a prediction model for each IMF components, and the prediction values of the sound velocity at different depths are obtained. The EMD-NARX, NARX and polynomial fitting model are analyzed and verified by Argo buoys data in the South China Sea, and the results of the experiment show that EMD-NARX improves the time series prediction accuracy of sound velocity by 32.24% and 65.15% compared with NARX and polynomial fitting, respectively, so EMD-NARX has a good prediction effect on the time series SVP of the deep sea.

Resistance training (RT) load and volume are considered crucial variables to appropriately prescribe and manage for eliciting the targeted acute responses (i.e., minimizing neuromuscular fatigue) and chronic adaptations (i.e., maximizing neuromuscular adaptations). In traditional RT contexts, load and volume are generally pre-prescribed; thereby, potentially yielding sub-optimal outcomes. A RT concept that individualizes programming is autoregulation: a systematic two-step feedback process involving, (1) monitoring performance and its constituents (fitness, fatigue, and readiness) across multiple time frames (short-, moderate-, and long-term); and (2) adjusting programming (i.e., load and volume) to elicit the targeted goals (i.e., responses and adaptations). A growing body of load and volume autoregulation research has accelerated recently, with several meta-analyses suggesting that autoregulation may provide a small advantage over traditional RT. Nonetheless, the existing literature has typically conceptualized these current autoregulation methods as standalone practices, which has limited their extensive utility in research and applied settings. The primary purpose of this review was three-fold. Initially, we synthesized the current methods of load and volume autoregulation, while disseminating each method’s main advantages and limitations. Second, we conceptualized a theoretical Integrated Velocity Model (IVM) that integrates the current methods for a more holistic perspective of autoregulation that may potentially augment its benefits. Lastly, we illustrated how the IVM may be compared to the current methods for future directions and how it may be implemented for practical applications. We hope that this review assists to contextualize a novel autoregulation framework to help inform future investigations for researchers and practices for RT professionals.

PurposeIn this paper, a deduced flow resistance equation for open-channel flow was tested using measurements carried out in mobile bed rills with sediment-laden flows and fixed bed rills. The main aims were to (i) assess the effect of sediment transport on rill flow resistance, and (ii) test the slope-flow velocity relationship in fixed bed rills.MethodsThe following analysis was developed: (i) a relationship between the Γ function of the velocity profile, the rill slope and the Froude number was calibrated using measurements carried out on fixed bed rills; (ii) the component of Darcy-Weisbach friction factor due to sediment transport was deduced using the corresponding measurements carried out on mobile bed rills (grain resistance and sediment transport) and the values estimated by flow resistance equation (grain resistance) for fixed bed rills in the same slope and hydraulic conditions; (iii) the Γ function relationship was calibrated using measurements carried out on mobile bed rills and the data of Jiang et al. (2018).ResultsThis analysis demonstrated that the effect of sediment transport on rill flow resistance law is appreciable only for 7.7% of the examined cases and that the theoretical approach allows for an accurate estimate of the Darcy-Weisbach friction factor. Furthermore, for both fixed and mobile beds, the mean flow velocity was independent of channel slope, as suggested by Govers (1992) for mobile bed rills.ConclusionsThe investigation highlighted that the effect of sediment transport on rill flow resistance is almost negligible for most of the cases and that the experimental procedure for fixing rills caused the unexpected slope independence of flow velocity.

The velocity dip in river systems occurs due to the three-dimensional nature of flow and the presence of secondary currents. These secondary currents are influenced by the complex river bathymetry and the roughness, causing the maximum velocity to occur below the water surface due to the cross-momentum transfers. The evaluation of dip positions, while estimating the vertical velocity profiles, is essential for accurately estimating the flow rate across the river cross-section. This work presents a dip-informed coupled quasi-three-dimensional hydrodynamic model for predicting velocities in an ungauged natural river section with limited datasets. The model is capable of providing the longitudinal and lateral velocity (ux, uy) along with the dip-integrated vertical velocity profile. Information entropy is utilized to estimate the initial dip positions across the river cross-sections with the Principle of Maximum Entropy (POME), considering the dimensionless dip position as the random variable. An iterative framework is developed in conjunction with the predicted hydrodynamic model outputs to estimate the dip positions in the velocity profile across four river sections in the Brahmaputra River, Assam, India. The stability of the model is ensured by the Courant–Friedrichs–Lewy (CFL) criterion. With the observed survey datasets, the model results are validated. The estimated results indicate satisfactory robustness and stability of the model, with a mean percentage error ranging from 2.24 to 4.99% in estimating the depth-averaged velocities. The model's efficacy is also investigated by comparing the model-predicted velocity dip with the observed data, and improved accuracy is observed with respect to the existing approach present in the literature.Research highlightsDeveloped a quasi-3D model integrating 2D hydrodynamic and entropy-based model for predicting dip-integrated vertical velocity profiles in data-scarce regions.The model could predict the velocities in a natural river in x and y direction along with the vertical velocity profile across flow depth.The location of occurrence of maximum velocities along the flow depth throughout the cross section is evaluated.The velocity dip triggered by the difference in bed level is estimated by an iterative technique.

Non-uniform sound velocity profiles (SVPs) cause sound-ray refraction and propagation-path bending. The straight-line mapping among propagation time, propagation distance, and target position is, therefore, disrupted, leading to systematic errors in constant-sound-speed localization. To improve the consistency between propagation correction and geometric localization, an iterative ray-path correction method based on propagation-time consistency is proposed. The method contains three coupled steps. First, a path-dependent local layered SVP model is constructed for each target-to-base-station path, rather than using a global or fixed sound-speed model. Second, the ray parameter is inverted under the constraint of measured time-of-arrival (TOA), so that the corrected ray path remains consistent with the observed propagation time. Third, the corrected slant range obtained by layered ray tracing is fed back into a known-depth weighted least squares (WLS) localization model, forming a closed-loop position update. The method is evaluated through long-baseline (LBL) simulations with multiple SVPs and propagation geometries and is validated using measured TOA data and an observation-derived SVP. The simulation results show that sub-meter accuracy can be achieved under the tested TOA-noise conditions. In measured-data validation, the planar localization error is reduced from 4.6866 m to 0.1923 m. No divergence is observed in the tested small SVP-perturbation cases.

Riparian vegetation could be an appropriate solution for the flood control and sustainable river management technique as it is useful for the energy dissipation of the stream flows. The present experimental investigation is conducted to understand the flow configuration and energy dissipation of stream flows considering the flexible and rigid vegetation. The laboratory-based physical models are tested in a rectangular flume to observe the flow field in the upstream and downstream of both the vegetations. In this study, rigid vegetation is considered of wooden dowels of equal height and almost uniform diameter whereas for flexible vegetation paddy plants were used. Acoustic Doppler Velocimeter is utilized to observe the velocity profiles at different sections and then it is compared with and without the vegetation. It is noticed that around 24% reduction in stream velocity occurs due to introduction of rigid vegetation whereas 90% reduction happens due to flexible vegetation in the channel. Additionally, energy dissipation at all the sections of both vegetation types was found to give a more comprehensive understanding of the flow field. Overall, the present study provides an insight into the fact that with the help of flexible and rigid vegetation one can restore the ecological balance in the river. The vegetation with high density, height and flexibility will be useful for the dissipation of energy in effective manner. This study also suggests that flexible vegetation will be an effective tool in river management by decreasing scouring in the channel, thereby reducing erosion and sediment discontinuity.

Since S-wave velocity of the subsurface is an important parameter in near surface applications, many studies have been conducted for its estimation. Among the various methods that use surface waves or body waves, Rayleigh wave inversion is the most popular. In practice, the densities and P-wave velocities of different layers are usually assumed to be known to avoid ill-posed problems, as they have less influence on the dispersion curves. However, improper assignment of these two groups of parameters leads to inaccurate estimation of the S-wave velocity profile. In order to address this problem, the all-parameters Rayleigh wave inversion strategy is proposed in which the S-wave velocities, layer thicknesses, densities and P-wave velocities of different layers are included as the unknown parameters for inversion. Meanwhile, the transitional Markov Chain Monte Carlo (TMCMC) algorithm is applied for the implementation of all-parameters Rayleigh wave inversion. One simulated example and two real-test applications are demonstrated to verify the capability of the proposed method in the estimation of the S-wave velocity profile, the densities and the P-wave velocities. Furthermore, it is verified that the proposed method achieved more accurate S-wave velocity profile estimation than the traditional approach.