Longitudinal River Monitoring and Modelling Substantiate the Impact of Weirs on Nitrogen Dynamics

The fluvial nitrogen dynamics at locations around weirs are still rarely studied in detail. Eulerian data, often used by conventional river monitoring and modelling approaches, lags the spatial resolution for an unambiguous representation. With the aim to address this knowledge gap, the present study applies a coupled 1D hydrodynamic–water quality model to a 26.9 km stretch of an upland river. Tailored simulations were performed for river sections with water retention and free-flow conditions to quantify the weirs’ influences on nitrogen dynamics. The water quality data were sampled with Eulerian and Lagrangian strategies. Despite the limitations in terms of required spatial discretization and simulation time, refined model calibrations with high spatiotemporal resolution corroborated the high ammonification rates (0.015 d−1) on river sections without weirs and high nitrification rates (0.17 d−1 ammonium to nitrate, 0.78 d−1 nitrate to nitrite) on river sections with weirs. Additionally, using estimations of denitrification based on typical values for riverbed sediment as a reference, we could demonstrate that in our case study, weirs can improve denitrification substantially. The produced backwater lengths can induce a means of additional nitrogen removal of 0.2-ton d−1 (10.9%) during warm and low-flow periods.

State of pollution of Ogosta River waters

The present research is related to one of the basic component of the environment – waters with study area the Ogosta river catchment. It is based on the investigation of water samples collected during field research in the river monitoring area and laboratory analysis. The research is focused on the state of the pollution and quality of the water. The results can be used in the processes of environmental optimization and realization of the strategies for sustainable development in the region.

TP-River: Monitoring and quantifying total river runoff from the Third Pole

CapsuleThe TP-River project is constructing a monitoring network for 13 major rivers at the Third Pole to quantify the total river runoff and its response to monsoon and westerly winds.

Global River Monitoring Using Semantic Fusion Networks

Global river monitoring is an important mission within the remote sensing society. One of the main challenges faced by this mission is generating an accurate water mask from remote sensing images (RSI) of rivers (RSIR), especially on a global scale with various river features. Aiming at better water area classification using semantic information, this paper presents a segmentation method for global river monitoring based on semantic clustering and semantic fusion. Firstly, an encoder–decoder network (AEN)-based architecture is proposed to obtain the semantic features from RSIR. Secondly, a clustering-based semantic fusion method is proposed to divide semantic features of RSIR into groups and train convolutional neural networks (CNN) models corresponding to each group using data augmentation and semi-supervised learning. Thirdly, a semantic distance-based segmentation fusion method is proposed for fusing the CNN models result into final segmentation mask. We built a global river dataset that contains multiple river segments from each continent of the world based on Sentinel-2 satellite imagery. The result shows that the F1-score of the proposed segmentation method is 93.32%, which outperforms several state-of-the-art algorithms, and demonstrates that grouping semantic information helps better segment the RSIR in global scale.