To study the fish behavioral response to up- and downstream fish passage structures, live-fish tests are conducted in large flumes in various laboratories around the world. The use of multiple fisheye cameras to cover the full width and length of a flume, low color contrast between fish and flume bottom and non-uniform illumination leading to fish shadows, air bubbles wrongly identified as fish as well as fish being partially hidden behind each other are the main challenges for video-based fish tracking. This study improves an existing open-source fish tracking code to better address these issues by using a modified Mask Regional-Convolutional Neural Network (Mask R-CNN) as a tracking method. The developed workflow, FishSeg, consists of four parts: (1) stereo camera calibration, (2) background subtraction, (3) multi-fish tracking using Mask R-CNN, and (4) 3D conversion to flume coordinates. The Mask R-CNN model was trained and validated with datasets manually annotated from background subtracted videos from the live-fish tests. Brown trout and European eel were selected as target fish species to evaluate the performance of FishSeg with different types of body shapes and sizes. Comparison with the previous method illustrates that the tracks generated by FishSeg are about three times more continuous with higher accuracy. Furthermore, the code runs more stable since fish shadows and air bubbles are not misidentified as fish. The trout and eel models produced from FishSeg have mean Average Precisions (mAPs) of 0.837 and 0.876, respectively. Comparisons of mAPs with other R-CNN-based models show the reliability of FishSeg with a small training dataset. FishSeg is a ready-to-use open-source code for tracking any fish species with similar body shapes as trout and eel, and further fish shapes can be added with moderate effort. The generated fish tracks allow researchers to analyze the fish behavior in detail, even in large experimental facilities.

Curved bar rack ‐ bypass systems (CBR‐BS) are a promising technology for diverting downstream moving fish away from hydropower intakes. A CBR functions as a mechanical behavioral barrier by creating high velocity and pressure gradients along its bars, guiding approaching fish that exhibit avoidance reactions towards the bypass. The original CBR design features a bar shape with spacings that narrow from the upstream to the downstream tip, which raises concerns about clogging due to foliage and floating debris. To address this issue, new foil‐shaped bars (f‐CBR) were developed with similar hydraulics while maintaining a constant bar spacing along the rack. To test its performance, a 1:1 physical model of a f‐CBR with a bar spacing of 25 mm was installed in an etho‐hydraulic flume. Live‐fish tests using brown trout were conducted at approach flow velocities of , 0.3, 0.6 m/s, with bypass‐to‐approach flow velocity ratios set at and 1.2. Additional tests with leaves and driftwood were performed to evaluate clogging probability of the f‐CBR. Flow fields were also numerically simulated for the tested flow conditions and linked to the fish behavior. Over all tested setups, fish guidance efficiencies between 47% and 66% were observed, while fish protection efficiencies ranged from 65% to 86%. These findings are compared with those from other types of behavioral fish guidance racks tested in both laboratory and field settings. Finally, potential measures for the optimal design of a f‐CBR‐BS are presented.

The settlement of non-spherical particles, such as propagules of plants and natural sediments, are commonly observed in riverine ecosystems. The settling process is influenced by both particle properties (size, density and shape) and fluid properties (density and viscosity). Therefore, the drag law of non-spherical particles is a function of both particle Reynolds number and particle shape. Herein, a total of 828 settling data are collected from the literatures, which cover a wide range of particle Reynolds number (0.008–10000). To characterize the influence of particle shapes, sphericity is adopted as the general shape factor, which varies from 0.421 to 1.0. By comparing the measured drag with the standard drag curve of spheres, we modify the spherical drag law with three shape-dependent functions to develop a new drag law for non-spherical particles. Combined with an iterative procedure, a new model is thus obtained to predict the settling velocity of non-spherical particles of various shapes and materials. Further applications in hydrochorous propagule dispersal and sediment transport are projected based on deeper understanding of the settling process.