Vegetation growing in the water along watercourses has been the subject of several studies since it was recognized that it could have a significant impact on the water flow. It may increase resistance to flow and cause higher water levels. Also, it has an effect on the velocity profiles. Previous investigations on the flow of water through emergent vegetation have shown different results. The purpose of this paper is to investigate, and determine how aquatic vegetation influences flow resistance, water depth and discharge in the Chotárny channel at the Žitný Ostrov area. This area is part of the Danube Lowland (south-west of Slovakia). The channel network at the Žitný Ostrov region was built up for drainage and also to provide irrigation water. The Chotárny channel is one of three main channels of this network. Measurements performed during six years at this channel were used for an evaluation of vegetation impact on flow conditions. The roughness coefficient was used as one way of quantifying this impact. The results show variation of this parameter during the growing season. Vegetation causes resistance to flow; it reduces flow velocities, discharge and increases water depth.
Analytical solutions of the one-dimensional (1D) advection–dispersion equations, describing the substance transport in streams, are often used because of their simplicity and computational speed. Practical computations, however, clearly show the limits and the inaccuracies of this approach. These are especially visible in cases where the streams deform concentration distribution of the transported substance due to hydraulic and morphological conditions, e.g., by transient storage zones (dead zones), vegetation, and irregularities in the stream hydromorphology. In this paper, a new approach to the simulation of 1D substance transport is presented, adapted, and tested on tracer experiments available in the published research, and carried out in three small streams in Slovakia with dead zones. Evaluation of the proposed methods, based on different probability distributions, confirmed that they approximate the measured concentrations significantly better than those based upon the commonly used Gaussian distribution. Finally, an example of the application of the proposed methods to an iterative (inverse) task is presented.
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