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Intensive agricultural activities favor eutrophication and harmful phytoplankton blooms due to the high export of nutrients and damming of rivers. Productive watersheds used for water purification can have multiple reservoirs with phytoplankton blooms, which constitutes a high health risk. In general, water quality monitoring does not cover small- and medium-sized reservoirs (0.25–100 ha) of productive use due to their large number and location in private properties. In this work, the in situ trophic state of fourteen reservoirs was simultaneously assessed using Sentinel-2 images in the Santa Lucía River Basin, the main drinking water basin in Uruguay. These reservoirs are hypereutrophic (0.18–5.22 mg total P L−1) with high phytoplankton biomasses (2.8–4439 µg chlorophyll-a L−1), mainly cyanobacteria. Based on data generated in situ and Sentinel-2 imagery, models were fitted to estimate satellite Chl-a and transparency in all the basin reservoirs (n = 486). The best fits were obtained with the green-to-red band ratio (560 and 665 nm, R2 = 0.84) to estimate chlorophyll-a and reflectance at 833 nm (R2 = 0.73) to determine transparency. The spatial distribution of the trophic state was explored by spatial autocorrelation and hotspot analysis, and the variation in spatial patterns could be determined prior and subsequent to a maximum cyanobacteria value in water treatment plant intakes. Therefore, reservoirs with greater potential for phytoplankton biomass export were identified. This work provides the first fitted tool for satellite monitoring of numerous reservoirs and strengthens the country's ability to respond to harmful phytoplankton blooms in its main drinking water basin. [ABSTRACT FROM AUTHOR]

In recent decades, the Río de la Plata Estuary has shown an increase in the frequency and intensity of phytoplankton blooms with negative impacts on production activities, human health, and biodiversity. Water quality monitoring programs provide samples from the coastal zone alone, which limits the collection of data inside the Estuary and the analysis of the spatio-temporal dynamics of phytoplankton blooms, as well as their relationship with flow rate. In this work, a systematic satellite monitoring of the Estuary was carried out for the first time. Sentinel-2 images captured during 2016–2021 were used along with the Normalized Difference Chlorophyll Index. It included one year of El Niño, one neutral year and two consecutive years of La Niña. Four zones with the highest frequency of bloom occurrence were delimited. Data on the extent and intensity in which blooms occurred were extracted and related to the flow rates of the main tributaries using Bayesian models. The most intense and frequent blooms were detected on the southern and northern coasts, respectively (maximum values of 515 km2 in January 2021, NDCI>0.06), followed by a wide area of intense phytoplankton development inside the Estuary. Blooms were more frequent in warmer months, with elevated Chl- a concentrations in 75% of the months of the study period on the Argentine coast, and 50% on the Uruguayan coast. Blooms were positively correlated with low flows. Therefore, the most extensive and intense bloom episodes occur during La Niña events. During El Niño, the high flows transport the biomass originating in the Estuary and in the hydroelectric reservoirs located upstream, which can even be transported along the northern coast. This work identified a recurrent pattern of phytoplankton blooms and the hydro-meteorological conditions that favor their magnification in a context of strong climate variability in the region and estuarine eutrophication. [Display omitted] • Eutrophication in the Río de la Plata estuary (RdlP) show signs of worsening. • Analysis of Sentinel-2 images revealed extensive phytoplankton blooms from 2016 to 2021. • Four zones of high frequency phytoplankton bloom occurrence were delimited. • Bayesian modeling show greatest blooms under La Niña and high transport in El Niño. • Recurring pattern of blooms was identified in RdlP under extreme climate variability. [ABSTRACT FROM AUTHOR]