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.

Downstream of the confluence of rivers, complex hydrological and morphological processes control the flow and sediment transport. This study aimed to analyze the spatio-temporal dynamics of suspended sediment in the confluence area of the Tisza and its main tributary Maros River using Sentinel-2 images and to reveal the correlation between the hydrological parameters and the mixing process through a relatively long period (2015–2021). The surficial suspended sediment dynamism was analyzed by applying K-means unsupervised classification algorithm on 143 images. The percentages of the Tisza (TW) and Maros (MW) waters and their mixture (MIX) were calculated and compared with the hydrological parameters in both rivers. The main results revealed that the areal, lateral, and longitudinal extensions of TW and MIX have a better correlation with the hydrological parameters than the MW. The Pearson correlation matrix revealed that the discharge ratio between the rivers controls the mixing process significantly. Altogether, 11 mixing patterns were identified in the confluence area throughout the studied period. The TW usually dominates the confluence in November and January, MW in June and July, and MIX in August and September. Predictive equations for the areal distribution of the three classes were derived to support future water sampling in the confluence area.

Five countries share the catchment of the Tisza River (Central Europe). In most households electricity and water are available, and by washing synthetic clothes they can produce a large number of microfibers. However, in many sub-catchments of the river, the wastewater treatment is insufficient; therefore, microplastics (MP), especially plastic microfiber emissions into rivers, represent a problem. Our goal was to analyze the suspended sediment and microfiber transport at the low stage, making repeated (2021 and 2022) measurements in the Tisza River (946 km) at 26 sites across three countries. Water sampling was performed by pumping 1 m³ of water through sieves (90–200 µm). The mean MP transport in 2021 was 19 ± 13.6 items/m³, but it increased by 17% in 2022 (22.4 ± 14.8 items/m³). The most polluted sections were the Upper Tisza (Ukraine, Hungary) and the Lower Tisza (Serbia), where wastewater treatment is not satisfactory, whereas the Middle Tisza (Hungary) was less polluted. The tributaries increased the sediment and MP budget of the main river. Microfibers dominate (84–97%) the suspended MP transport, and thus it can be determined that they originated from wastewater. The MP transport was influenced by the availability of wastewater treatment plants, dams, tributaries, and mobilization of bottom sediments. At the low stage, no connection was found between the suspended sediment and MP particle transport.