The accurate estimation of Manning’s roughness coefficient (n) is critical for hydraulic modeling in open channels. In fish passes designed as close-to-nature structures, this coefficient has a strong influence on the overall design and operation. This study evaluates n for the Veľké Kozmálovce fish pass using high-resolution drone imagery and image analysis techniques to determine riverbed surface characteristics and extract a grain size distribution curve. Various empirical equations based on Strickler’s formula were applied to specific grain sizes, yielding average n values of 0.036 and 0.037. Cowan’s method, which considers surface material, irregularities, vegetation, obstructions, and meandering, provided an upper-bound estimate of 0.040. However, this method is known to overestimate roughness in some cases. The Step-by-Step method, applied with hydraulic field measurements, resulted in a narrower range of n from 0.027 to 0.037. Overall, estimated values across all methods ranged between 0.023 and 0.040, reflecting the structural complexity of the fish pass, which includes boulders embedded in concrete and coarse gravel infill. These findings highlight the limitations of using generalized tabulated values for artificial channels and demonstrate that drone-based photogrammetry combined with empirical and analytical approaches can effectively capture spatial variability in hydraulic roughness.

Photogrammetric scanning is a technique used to create 3D models by capturing the dimensions and textures of objects or areas. This study applies photogrammetry to scan a fish pass, focusing on capturing the channel's micro-relief at optimal resolution. A drone was used for measurements, supplemented by terrestrial images to generate a dense point cloud. The point cloud was processed for various applications, including hydraulic flow modeling. In HEC-RAS simulation software, the point cloud, as a raster file, serves as the terrain base for defining the channel's geometry. The raster resolution directly affects the quality and precision of the model's geometry. The study explores how geometry settings, such as the number of defined cross-sections and terrain detail, influence flow simulations in fish passes. Special attention is given to perturbation boulders, which affect flow dynamics and help meet the required parameters for different fish pass types and target fish species. By analyzing the interaction of geometric inputs, an understanding of their effectiveness in accurately representing the river bed and their impact on the results of simulated water levels was sought. This research provides insights into effective flow modeling based on data from photogrammetric scanning, contributing to the design and monitoring of fish passes.

This study investigates the application of photogrammetric scanning to generate high-quality raster datasets for one-dimensional hydraulic modeling. By processing point clouds, detailed raster files accurately capture open channel morphology. The research evaluates the efficiency, technical complexity, and impact of raster resolution on simulation results using HEC-RAS code. Recommendations address optimizing point cloud processing and selecting raster resolution to balance detail and computational efficiency. The study demonstrates photogrammetry's effectiveness through a one-dimensional numerical model of a nature-like fish pass, showcasing its potential for enhancing hydraulic structure design and monitoring. Results provide insights into future applications in hydraulic engineering.

High-resolution topographic data are crucial for accurately modeling hydraulic structures, particularly fish passes, where subtle geomorphic features govern flow heterogeneity and fish navigation. Conventional remote sensing approaches often lack the spatial resolution to resolve such microrelief. This study demonstrates the application of unmanned aerial vehicle (UAV) photogrammetry using Structure-from-Motion (SfM), which generates dense point clouds and digital elevation models (DEMs) with sub-centimeter accuracy. The UAV-SfM workflow enables rapid, non-invasive acquisition under controlled field conditions, followed by point-cloud processing in CloudCompare and rasterization into hydraulically suitable elevation models. These datasets support high-fidelity numerical modeling in 1D, 2D, and 3D, as well as the creation of physical models via 3D printing for laboratory hydraulic experiments. The study highlights the advantages of UAV-SfM in capturing complex terrain, estimating grain characteristics, and improving hydraulic model reliability, while also noting limitations such as restricted visibility areas and computational demands of large point-cloud datasets. By integrating high-resolution UAV-based topographic data, new opportunities for routine, high-detail hydraulic modeling and environmental engineering practice are opened.