The monitoring of surface-water quality followed by water-quality modeling and analysis are essential for generating effective strategies in surface-water-resource management. However, worldwide, particularly in developing countries, water-quality studies are limited due to the lack of a complete and reliable dataset of surface-water-quality variables. In this context, several statistical and machine-learning models were assessed for imputing water-quality data at six monitoring stations located in the Santa Lucía Chico river (Uruguay), a mixed lotic and lentic river system. The challenge of this study is represented by the high percentage of missing data (between 50% and 70%) and the high temporal and spatial variability that characterizes the water-quality variables. The competing algorithms implement univariate and multivariate imputation methods (inverse distance weighting (IDW), Random Forest Regressor (RFR), Ridge (R), Bayesian Ridge (BR), AdaBoost (AB), Hubber Regressor (HR), Support Vector Regressor (SVR) and K-nearest neighbors Regressor (KNNR)). According to the results, more than 76% of the imputation outcomes are considered “satisfactory” (NSE > 0.45). The imputation performance shows better results at the monitoring stations located inside the reservoir than those positioned along the mainstream. IDW was the model with the best imputation results, followed by RFR, HR and SVR. The approach proposed in this study is expected to aid water-resource researchers and managers in augmenting water-quality datasets and overcoming the missing data issue to increase the number of future studies related to the water-quality matter.

Land use/land cover is one of the critical factors that affects surface-water quality at catchment scale. Effective mitigation strategies require an in-depth understanding of the leading causes of water pollution to improve community well-being and ecosystem health. The main aim of this study is to assess the relationship between land use/land cover and biophysical and chemical water-quality parameters in the Santa Lucía catchment (Uruguay, South America). The Santa Lucía river is the primary potable source of the country and, in the last few years, has had eutrophication issues. Several multivariate statistical analyses were adopted to accomplish the specific objectives of this study. The principal component analysis (PCA), coupled with k-means cluster analysis (CA), helped to identify a seasonal variation (fall/winter and spring/summer) of the water quality. The hierarchical cluster analysis (HCA) allowed one to classify the water-quality monitoring stations in three groups in the fall/winter season. The factor analysis (FA) with a rotation of the axis (varimax) was adopted to identify the most significant water-quality variables of the system (turbidity and flow). Finally, another PCA was run to link water-quality variables to the dominant land uses of the watershed. Strong correlations between TP and agriculture-land use, TP and livestock farming, NT and urban areas arose. It was found that these multivariate exploratory tools can provide a proper overview of the water-quality behavior in space and time and the correlations between water-quality variables and land use.

The Río de la Plata is a large fluvial–estuarine–sea system discharging into the southwestern Atlantic Ocean, which has relevant features such as high fluvial discharge, microtidal astronomical tidal scales, a relevant meteorological tide, and a strong atmospheric forcing effect, due to its large width. The objective of this study is to advance the understanding of the estuarine hydrodynamics and salt transport, as well as discussing the main characteristics of the spatiotemporal variability of the salinity field. To achieve this, the results of a 3D model of the Río de la Plata and its maritime front were used, simulating an extensive period of 10 years. In this study, the model was validated using vertical salinity profiles collected at different locations in the estuary. The temporal variability of the salinity stratification was characterised at different temporal scales: annual, monthly, and storm. At the same time, the influences of fluvial flow and winds were determined. The correlation analysis between fluvial flow and the salinity field showed that high annual fluvial flows generate an extension of the freshwater area, with larger longitudinal salinity gradients and a shift of the salinity front towards the ocean. The tendency at the monthly scale is not as clear as that observed at the annual scale. The results show that the effect of a storm coming from the northwest is quite similar to that of storms coming from the southwest, especially in the central and southern zones of the Río de la Plata, where mixing increases and stratification decreases, according to the intensity of the storm. The effect of south–southeasterly storms increases the mixing process and reduces stratification; the opposite effect was identified with respect to northeasterly storms, under the influence of which the stratified area increases. Synthesising the obtained results, a global zonification of the vertical salinity stratification for the Río de la Plata is proposed.