Spatial and temporal distribution of satellite turbidity in response to different environmental variables in the Bahía Blanca Estuary, South-Western Atlantic

ABSTRACT

This work combines in situ measurements with a time series of satellite turbidity derived from Landsat 8-OLI images to provide a first synoptic overview of the main hydrometeorological drivers of turbidity in the Bahía Blanca Estuary (Argentina). An empirical relationship between turbidity and SPM concentrations was established for the study area (R² = 0.92; RMSE = 0.098 mg m⁻³; NMAE = 6.2%). Several atmospheric correction schemes and turbidity retrieval algorithms were tested and the combination of the SWIR-v and the retrieval algorithm by Dogliotti et al. (2015) were applied to 121 Landsat 8-OLI scenes (2013–2021). The effects of tides, winds, and rainfall on satellite turbidity were evaluated through Generalized Linear Models (GLM) built for three different sectors along Canal Principal, from the inner zone to its mouth. Regardless of the zone, cumulative rainfall had negligible effects on turbidity. Tides had a significant effect in the inner and middle zones. In the inner zone, higher turbidity values significantly associated with ebb tide conditions, which produce erosion. In the middle section tidal current speeds positively correlated with turbidity, suggesting sediment resuspension over shallow banks. Close to the mouth of the estuary, turbidity responded entirely to winds. Winds blowing from de NW, aligned with the azimuth of Canal Principal, would aid to the export of estuarine sediments to the shelf.

KEYWORDS:

• turbidity
• Landsat 8-OLI
• Bahía Blanca Estuary
• sediment transport
• South-western Atlantic

Acknowledgments

The authors would like to thank the Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET), Argentina for their financial support. We specially thank to Martin Amodeo for the statistical support and the Cámara de Pescadores Artesanales de Monte Hermoso y Pehuen Co (Eduardo Flores, “Juancito” and “Flacuchin”) for their valuable help, since the collection of in situ samples would not have been possible without their continual contribution. Finally, we thank to the USGS for providing the Landsat‐8/OLI data, the atmospheric correction software ACOLITE provided by RBINS and the meteological data supplied by the Consorcio de Gestión del Puerto de Bahía Blanca (https://puertobahiablanca.com/).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Agencia Nacional de Promoción Científica y Tecnológica [PICT 2016-0817]; Secretaría General de Ciencia y Tecnología, Universidad Nacional del Sur [PGI 24B/236];