The geographical environment fundamentally influences the transport and deposition of sediments, including microplastics. In addition, the socioeconomic differences inherent in transboundary catchments result in various waste management strategies among the different countries influencing the input of microplastics into rivers. The catchment of the Tisza River in Central Europe is shared by five countries with different economic statuses and wastewater treatment practices. The aim of this research is to study the spatial changes in microplastic debris deposition along the Tisza and its main tributaries. The mean number of microplastic particles in the sediments of the Tisza was 3177 ± 1970 items/kg, while 3808 ± 1605 items/kg were counted in the sediments of the tributaries. Most of the particles were fibres, indicating the dominance of municipal wastewater input; this is especially the case in the upstream sub-catchments, where there are low degrees of wastewater management. The highest amount of microplastics was found in the sediments of the most-upstream section, where a low number of households are connected to wastewater treatment plants. Thus, it is hypothesized that suburban areas where high population densities and improper waste management co-exist may contribute to the direct input of microplastics into river systems and the development of local microplastic contamination hotspots. In addition, a high microplastic concentration was measured at the furthest downstream section, resulting from the decreased flow velocity related to impoundment by a dam. The efficiency of the microplastic trapping of the various depositionary forms varies along the river, suggesting various influencing factors and the complexity of the process. The higher concentration of microplastics observed in the tributaries compared to that observed in sediments of the main stream may reflect the importance of local sources and the complexity of source-to-sink relations for microplastic routes through the fluvial system; these processes do not always reflect progressive downstream increases.

The spatio-temporal dynamism of sediment discharge (Q_s) in rivers is influenced by various natural and anthropogenic factors. Unfortunately, most rivers are only monitored at a limited number of stations or not gauged at all. Therefore, this study aims to provide a remote-sensing-based alternative for Q_s monitoring. The at-a-station hydraulic geometry (AHG) power–law method was compared to the at-many-stations hydraulic geometry (AMHG) method; in addition, a novel AHG machine-learning (ML) method was introduced to estimate water discharge at three gauging stations in the Tisza (Szeged and Algyő) and Maros (Makó) Rivers in Hungary. The surface reflectance of Sentinel-2 images was correlated to in situ suspended sediment concentration (SSC) by support vector machine (SVM), random forest (RF), artificial neural network (ANN), and combined algorithms. The best performing water discharge and SSC models were employed to estimate the Q_s. Our novel AHG ML method gave the best estimations of water discharge (Szeged: R² = 0.87; Algyő: R² = 0.75; Makó: R² = 0.61). Furthermore, the RF (R² = 0.9) and combined models (R² = 0.82) showed the best SSC estimations for the Maros and Tisza Rivers. The highest Q_s were detected during floods; however, there is usually a clockwise hysteresis between the SSC and water discharge, especially in the Tisza River.

Rivers transport terrestrial microplastics (MP) to the marine system, demanding cost-effective and frequent monitoring, which is attainable through remote sensing. This study aims to develop and test microplastic concentration (MPC) models directly by satellite images and indirectly through suspended sediment concentration (SSC) as a proxy employing a neural network algorithm. These models relied upon high spatial (26 sites) and temporal (198 samples) SSC and MPC data in the Tisza River, along with optical and active sensor reflectance/backscattering. A feedforward MLP neural network was used to calibrate and validate the direct models employing k-fold cross-validation (five data folds) and the Optuna library for hyperparameter optimization. The spatiotemporal generalization capability of the developed models was assessed under various hydrological scenarios. The findings revealed that hydrology fundamentally influences the SSC and MPC. The indirect estimation method of MPC using SSC as a proxy demonstrated higher accuracy (R² = 0.17–0.88) than the direct method (R² = 0–0.2), due to the limitations of satellite sensors to directly estimate the very low MPCs in rivers. However, the estimation accuracy of the indirect method varied with lower accuracy (R² = 0.17, RMSE = 12.9 item/m³ and MAE = 9.4 item/m³) during low stages and very high (R² = 0.88, RMSE = 7.8 item/m³ and MAE = 10.8 item/m³) during floods. The worst estimates were achieved based on Sentinel-1. Although the accuracy of the MPC models is moderate, it still has practical applicability, especially during floods and employing proxy models. This study is one of the very initial attempts towards MPC quantification, thus more studies incorporating denser spatiotemporal data, additional water quality parameters, and surface roughness data are warranted to improve the estimation accuracy.

Stone and concrete revetments are widely constructed to control bank erosion and thus stabilize river banks. The consequences include accelerated erosion at unrevetted downstream channel sections and in-channel incision at revetted sections. The studied section of the Tisza River (Hungary) was revetted along 49% of its banks in the 20th century with stepped-block and placed-rock revetments. We therefore aimed (1) to study the effects of revetment constructions on channel processes and (2) to evaluate the state and collapse of revetments using a DEM of the channel and ADCP measurements. In the late 19th century, the river had an equilibrium meandering channel, with alternating eroding and aggrading banks. Currently, erosional processes dominate. Thus, 65% of the total channel length is affected by bank erosion. The revetments reduced the active lateral erosional processes only temporarily, as 58% of the revetments, mainly placed-rock, are already damaged. The flow characteristics at the revetted sections were found to depend on the rate and type of revetment collapse. Large pools developed in front of the revetments, playing an important role in initiating their erosion. The placed-rock revetments can collapse by slides or by stones falling into the pool one-by-one. In this case, a knickpoint develops, propagating upstream. Thus, the collapse and accelerated bank erosion also propagate upstream. The increased hazard created by the failure of the revetments requires the re-planning of bank stabilization practices.

The suspended sediment (SS) and microplastic (MP) transport in rivers is quite a complex process, influenced by several spatially and temporally changing factors (e.g., hydrology, sediment availability, human impact). Researchers usually investigate these factors individually and based on limited repetition in space and time. Therefore, this study aims to compare the driving factors of SS and MP transport by applying dense temporal (72 measurements) and spatial monitoring (at 26 sites). This study was performed on the medium-sized Tisza River, Central Europe. The suspended sediment concentration (SSC) was measured by water sampling and estimated based on Sentinel-2 images, while MP concentration was measured by pumping of water (1 m³). The SSC of the Tisza varied between 12.6 and 322.5 g/m³, whereas the MP concentration ranged 0-129 item/m³. Most of the transported particles were fibers (81-98 %), thus, it was assumed that MPs originated from wastewater. The results reflect that the hydrological conditions basically influence the SS and MP concentrations, as a strong positive correlation was found (ρ_SSC-MP = 0.6) between them during a year; however, the correlation during floods (minor floods: ρ = 0.63; medium floods: ρ = 0.41) was higher than at low stages (ρ = 0.1). It was assumed that run-off and mobilization of channel materials both contribute to increased SS and MP transport during floods. In contrary, the importance of mobilization of channel materials and wastewater input increase during low stages. The repeated measurements revealed that slope and velocity conditions, proximity of sources, tributaries, and dams influence the longitudinal changes in SS and MP concentrations. However, the effects of tributaries and dams are ambiguous (especially for MP) and require further research. The longitudinal measurements were conducted at low stages; hence, moderate negative correlations (ρ₂₀₂₁ = -0.35; ρ₂₀₂₂ = -0.41) were found between the SS and MP concentrations. Therefore, additional monitoring during (overbank) floods and denser spatial sampling are required to precisely reveal the spatiotemporal changes of SS and MP concentrations in rivers.