14 Feb 2023
 | 14 Feb 2023
Status: this preprint is open for discussion.

Sediment source and sink identification using Sentinel-2 and (kayak-based) lagrangian river turbidity profiles on the Vjosa River

Jessica Droujko, Srividya Hariharan Sudha, Gabriel Singer, and Peter Molnar

Abstract. Measurement of SSC at a basin outlet yields a basin-integrated picture of sediment fluxes, however it does not give a full spatial perspective on possible sediment sources, sinks, and pathways within the catchment. More effortsome spatially resolved estimates of suspended sediment concentrations (SSC) can be used to identify sediment sources, track erosion gradients in river basins, and quantify anthropogenic effects on catchment-scale sediment production, e.g. by dam construction or erosion control. Here we explore the use of high-resolution Sentinel-2 satellite images for this purpose in narrow and morphologically complex mountain rivers, combined with ground station turbidity sensing for calibration, and supported by a lagrangian kayak-derived river profile measurement. The study is carried out on the Vjosa River in Albania, which is one of the last intact large river systems in Europe. We developed a workflow to estimate river turbidity profiles from Sentinel-2 images including atmospheric, cloud cover, and deep water corrections, for the period May 2019 to July 2021 (106 images). In-situ turbidity measurements from four turbidity sensors located along the Vjosa River provided ground truthing. A multivariate linear regression model between turbidity and reflectance was fitted to this data. The extracted longitudinal river turbidity profiles were qualitatively validated with two descents of the river with a turbidity sensor attached to a kayak. The satellite-derived river profiles revealed variability in turbidity along the main stem with a strong seasonal signal, with the highest mean turbidity in winter along the entire length of the river. Most importantly, sediment sources and sinks could be identified and quantified from the river turbidity profiles, both for tributaries and within the reaches of the Vjosa. The river basin and network acted as a sediment source most of the time and significant sediment sinks were rare. Sediment sources were mostly tributaries following basin-wide rainfall, but also within-reach sources in river beds and banks were possible. Finally, we used the data to estimate the mean annual fine sediment yield at Dorez at ~2.5 ± 0.63 Mt/y in line with previous studies, which reveals the importance of the Vjosa River as an important sediment source into the Adriatic Sea. This work presents a proof of concept that open-access high-resolution satellite data has the potential for suspended sediment quantification not only in large water bodies but also in smaller rivers. The potential applications are many, from identifying sediment sources, activation processes, local point sources, and glacial sediment inputs, to sediment fluxes in river deltas, with a necessary future focus on improving accuracy and reducing uncertainty in such analyses.

Jessica Droujko et al.

Status: open (until 05 May 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-156', Anonymous Referee #1, 13 Mar 2023 reply
    • AC1: 'Comment on egusphere-2023-156', Jessica Droujko, 21 Mar 2023 reply

Jessica Droujko et al.

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Data used to produce this work Jessica Droujko

Model code and software

Code used to produce this work Jessica Droujko

Jessica Droujko et al.


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Short summary
We combined data from satellite images with data measured from a kayak in order to understand the propagation of fine sediment in the Vjosa river. We were able to find some storm-activated and some permanent sources of sediment. We also estimated how much fine sediment is carried into the Adriatic Sea by the Vjosa river, which is approximately 2.5 Mt of sediment per year and matches previous findings. With our work, we hope to show the potential of open-access satellite images.