No abstract

A confluence is a natural component in river and channel networks. This study deals, through field and numerical studies, with alluvial behaviors of a confluence affected by both river run-off and strong tides. Field measurements were conducted along the rivers including the confluence. Field data show that the changes in flow velocity and sediment concentration are not always in phase with each other. The concentration shows a general trend of decrease from the river mouth to the confluence. For a given location, the tides affect both the sediment concentration and transport. A two-dimensional hydrodynamic model of suspended load was set up to illustrate the combined effects of run-off and tidal flows. Modeled cases included the flood and ebb tides in a wet season. Typical features examined included tidal flow fields, bed shear stress, and scour evolution in the confluence. The confluence migration pattern of scour is dependent on the interaction between the river currents and tidal flows. The flood tides are attributable to the suspended load deposition in the confluence, while the ebb tides in combination with run-offs lead to erosion. The flood tides play a dominant role in the morphodynamic changes of the confluence.

River confluences are the key features of the drainage basins, as their hydrological, geomorphological, and ecological nature strongly influences the downstream river characteristics. The river reaches near the coastal zones, which also makes them under the influence of tidal currents in addition to their runoff. This causes a bi-directional flow and makes the study of confluences more interesting and complex in these areas. There is a reciprocal adjustment of flow, sediment, and morphology at a confluence, and its behaviors, differ greatly in tidal and non-tidal environments. Existing studies of the river junctions provide a good account of information about the hydrodynamics and bed morphology of the confluent areas, especially the unidirectional ones. The main factors which affect the flow field include the angle of confluence, flow-related ratios (velocity, discharge, and momentum) of the merging streams, and bed discordance. Hydraulically, six notable zones are identified for unidirectional confluences. However, for bi-directional (tidal) junctions, hydrodynamic zones always remain in transition but repeat in a cycle and make four different arrangements of flow features. This study discusses the hydrodynamics, sediment transport, morphological changes, and the factors affecting these processes and reviews the recent research about the confluences for these issues. All of these studies provide insights into the morpho-dynamics in tidal and non-tidal confluent areas.

Most of the hydropower dams in Sweden were built before 1980. The present dam-safety guidelines have resulted in higher design floods than their spillway discharge capacity and the need for structural upgrades. This has led to renewed laboratory model tests. For some dams, even computational fluid dynamics (CFD) simulations are performed. This provides the possibility to compare the spillway discharge data between the model tests performed a few decades apart. The paper presents the hydropower development, the needs for the ongoing dam rehabilitations and the history of physical hydraulic modeling in Sweden. More than 20 spillways, both surface and bottom types, are analyzed to evaluate their discharge modeling accuracy. The past and present model tests are compared with each other and with the CFD results if available. Discrepancies do exist in the discharges between the model tests made a few decades apart. The differences fall within the range −8.3%-+11.2%. The reasons for the discrepancies are sought from several aspects. The primary source of the errors is seemingly the model construction quality and flow measurement method. The machine milling technique and 3D printing reduce the source of construction errors and improve the model quality. Results of the CFD simulations differ, at the maximum, by 3.8% from the physical tests. They are conducted without knowledge of the physical model results in advance. Following the best practice guidelines, CFD should generate results of decent accuracy for discharge prediction.

Research Highlights: This paper proposes a new method for hemispherical forest canopy image segmentation. The method is based on a deep learning methodology and provides a robust and fully automatic technique for the segmentation of forest canopy hemispherical photography (CHP) and gap fraction (GF) calculation. Background and Objectives: CHP is widely used to estimate structural forest variables. The GF is the most important parameter for calculating the leaf area index (LAI), and its calculation requires the binary segmentation result of the CHP. Materials and Methods: Our method consists of three modules, namely, northing correction, valid region extraction, and hemispherical image segmentation. In these steps, a core procedure is hemispherical canopy image segmentation based on the U-Net convolutional neural network. Our method is compared with traditional threshold methods (e.g., the Otsu and Ridler methods), a fuzzy clustering method (FCM), commercial professional software (WinSCANOPY), and the Habitat-Net network method. Results: The experimental results show that the method presented here achieves a Dice similarity coefficient (DSC) of 89.20% and an accuracy of 98.73%. Conclusions: The method presented here outperforms the Habitat-Net and WinSCANOPY methods, along with the FCM, and it is significantly better than the Otsu and Ridler threshold methods. The method takes the original canopy hemisphere image first and then automatically executes the three modules in sequence, and finally outputs the binary segmentation map. The method presented here is a pipelined, end-to-end method.