Key Points: The pdf of impulse events is well described by a log normal distribution The local streamwise velocity plays a dominant role on particle dislodgement Wake flows increase particle entrainment via enhanced lift and increased TI

Bed load transport is a highly nonlinear phenomenon. Numerous stress‐transport power relations, with exponents varying between 1.5 and 16, have been proposed to capture the entire range of solid discharge trends exhibited by experimental data. A physics‐based explanation of the variation in exponent values is provided here. The concept of time‐resolved local stream power is used to determine the above‐threshold energy available for mobilizing bed materials, giving rise to solid discharge estimates. The generated transport capacity records, analyzed by a long‐term averaging process, allow for the construction of bed load curves that resemble the trends frequently reported in prior experimental studies. Under such conditions, use of long‐term averaged bed shear stress and bed load transport rates provide practical, yet oversimplified accounts of the transport phenomenon. The limitation of this methodology is particularly evident in low‐to‐moderate transport rates, where the calculated bed shear stress and consequent bed load transport rates are underestimated compared to values based on active periods of sediment movement. As the degree of intermittency in bed load transport increases, so does the exponent to compensate for the inactive periods of bed mobility. Conditionally averaged stress‐transport data, based on the active periods of bed load transport alone, however, exhibit a constant trend, reasonably well represented by a 1.5 power formula across the entire transport range. This approach better reflects the prevailing cause and effect relation by properly accounting for varied transport timescales. Furthermore, the resulting transport trend signifies a nearly constant efficiency in entraining and transporting sediment particles.

The measurement of the morphologic characteristics of evolving sediment beds around hydraulic structures is crucial for the understanding of the physical processes that drive scour. Although there has been significant progress towards the experimental characterization of the flow field in setups with complex geometries, little has been done with respect to the quantitative investigation of dynamic sediment bed geometry, mainly due to the limited capabilities of conventional instrumentation. Here, a recently developed computer vision technique is applied to obtain high-resolution topographic measurements of the evolving bed at the base of a cylinder during clear water scour, without interrupting the experiment. The topographic data is processed to derive the morphologic characteristics of the bed such as the excavated volume and the slopes of the bed. Subsequently, the rates of scour and the bathymetry at multiple locations are statistically investigated. The results of this investigation are compared with existing flow measurements from previous studies to describe the physical processes that take place inside a developing scour hole. Two distinct temporal phases (initial and development) as well as three spatial regions (front, side and wake) are defined and expressions for the statistical modelling of the bed features are derived.

The development of new strategies for the operation of existing dams is essential for restoring ecosystems that have already been affected by the construction and current regulation of these dams. The assessment of environmental flows (EFs) is significant in guiding such strategic dam operations. However, most of the existing methods just focus on one species or guild without simultaneously considering the interactions with other aquatic organisms. This study proposes an innovative method to determine comprehensive environmental flows (CEFs) for multi‐guilds (fish, macroinvertebrates, and hygrophytes) based on the habitat simulation model. We focused on the riparian habitats in the middle reaches of the Yangtze River, coupled the biological preferences of multi‐guilds with suitable habitat factors, including velocity, depth, and substrate, and then analyzed the relationships of flow and weighted usable areas to determine the CEFs for multi‐guilds. The results showed that while the optimal CEFs could be satisfied only in Spring and Summer, the threshold CEF conditions could be met in the Jianli reach throughout the year after the impoundment of Three Gorges Dam (TGD). The results obtained by using the CEFs assessment could better satisfy the EFs for multi‐guilds than the ones developed for individual species. The CEFs could be also used to evaluate the impact of TGD on multi‐guilds’ habitats and optimize ecologically‐friendly operations in already designed dams. This study is helpful for maintaining ecosystem integrity in Yangtze River and provides a good example for use in other dams facing similar issues.

This paper critically compares the use of laboratory tests against in situ tests combined with numerical seepage modeling to determine the hydraulic conductivity of natural soil deposits. Laboratory determination of hydraulic conductivity used the constant head permeability and oedometer tests on undisturbed Shelby tube and block soil samples. The auger hole method and Guelph permeameter tests were performed in the field. Groundwater table elevations in natural soil deposits with different hydraulic conductivity values were predicted using finite element seepage modeling and compared with field measurements to assess the various test results. Hydraulic conductivity values obtained by the auger hole method provide predictions that best match the groundwater table’s observed location at the field site. This observation indicates that hydraulic conductivity determined by the in situ test represents the actual conditions in the field better than that determined in a laboratory setting. The differences between the laboratory and in situ hydraulic conductivity values can be attributed to factors such as sample disturbance, soil anisotropy, fissures and cracks, and soil structure in addition to the conceptual and procedural differences in testing methods and effects of sample size.