Influence of Land Use/Land Cover on Surface-Water Quality of Santa Lucía River, Uruguay

Gorgoglione, Angela; Gregorio, Javier; Ríos, Agustín; Alonso, Julia R.; Chreties, Christian; Fossati, Mónica

doi:10.3390/su12114692

Cited by 58 publications

(25 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Field agriculture is a significant land use in many areas of the world and has been documented as a contributor of non-point source pollution to surface waters. In a recent study in Uruguay, [1] researchers found a strong correlation between total phosphorus (P) and agriculture land use in a river. In central Asia, remote sensing was used to analyze effects of land use on surface water contamination [2].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Impacts of Climate Change on Tile Discharge and Nitrogen Yield Using the DRAINMOD Model

et al. 2020

View full text Add to dashboard Cite

The detrimental impacts of agricultural subsurface tile flows and their associated pollutants on water quality is a major environmental issue in the Great Lakes region and many other places globally. A strong understanding of water quality indicators along with the contribution of tile-drained agriculture to water contamination is necessary to assess and reduce a significant source of non-point source pollution. In this study, DRAINMOD, a field-scale hydrology and water quality model, was applied to assess the impact of future climatic change on depth to water table, tile flow and associated nitrate loss from an 8.66 ha agricultural field near Londesborough, in Southwestern Ontario, Canada. The closest available climate data from a weather station approximately 10 km from the field site was used by the Ontario Ministry of Natural Resources and Forestry (MNRF) to generate future predictions of daily precipitation and maximum and minimum air temperatures required to create the weather files for DRAINMOD. Of the 28 models applied by MNRF, three models (CGCM3T47-Run5, GFDLCM2.0, and MIROC3.2hires) were selected based on the frequency of the models recommended for use in Ontario with SRA1B emission scenario. Results suggested that simulated tile flows and evapotranspiration (ET) in the 2071–2100 period are expected to increase by 7% and 14% compared to 1960–1990 period. Results also suggest that under future climates, significant increases in nitrate losses (about 50%) will occur along with the elevated tile flows. This work suggests that climate change will have a significant effect on field hydrology and water quality in tile-drained agricultural regions.

show abstract

Section: Introductionmentioning

confidence: 99%

Assessment of Impacts of Climate Change on Tile Discharge and Nitrogen Yield Using the DRAINMOD Model

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Uruguay has a humid subtropical climate (Cfa, according to the Köppen climate classification) with a mean temperature in the warmest month equal to 22 • C or higher [18]. The study area is characterized by total annual precipitation that varies between 1000 mm and 1500 mm and a temperature that can vary between 3 • C and 30 • C [19]. The region has a landscape of smooth hills with an average slope equal to 2.68%.…”

Section: Dataset Descriptionmentioning

confidence: 99%

Water-Quality Data Imputation with a High Percentage of Missing Values: A Machine Learning Approach

et al. 2021

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Both groups of techniques belong to the family of unsupervised methods, where information about other response variables or group belonging is not used to obtain results. This makes these techniques suitable for exploratory analysis, where the main aim is hypothesis generation rather than hypothesis verification [5,46].…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

“…It is generated from surface water bodies, which provide drinking water, preserve biodiversity, control climate, and maintain phosphorus and nitrate cycling [1]. However, in recent decades, the quality of these water bodies has been threatened by anthropogenic activities (e.g., urbanization, agriculture, water extraction, sewage discharge) [2][3][4][5]. Therefore, in the management plan of pollution control at a watershed scale, a worthwhile initial step is the identification of the pollution sources.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Linear and Non-Linear Machine Learning Techniques (PCA and SOM) for Characterizing Urban Nutrient Runoff

et al. 2021

Self Cite

View full text Add to dashboard Cite

Urban stormwater runoff represents a significant challenge for the practical assessment of diffuse pollution sources on receiving water bodies. Given the high dimensionality of the problem, the main goal of this study was the comparison of linear and non-linear machine learning (ML) methods to characterize urban nutrient runoff from impervious surfaces. In particular, the principal component analysis (PCA) for the linear technique and the self-organizing map (SOM) for the non-linear technique were chosen and compared considering the high number of successful applications in the water quality field. To strengthen this comparison, these techniques were supported by well-known linear and non-linear methods. Those techniques were applied to a complete dataset with precipitation, flow rate, and water quality (sediments and nutrients) records of 577 events gathered for a watershed located in Southern Italy. According to the results, both linear and non-linear techniques can represent build-up and wash-off, the two main processes that characterize urban nutrient runoff. In particular, non-linear methods are able to capture and represent better the rainfall-runoff process and the transport of dissolved nutrients in urban runoff (dilution process). However, their computational time is higher than the linear technique (0.0054 s vs. 15.24 s, for linear and non-linear, respectively, in our study). The outcomes of this study provide significant insights into the application of ML methods for the water quality field.

show abstract

Influence of Land Use/Land Cover on Surface-Water Quality of Santa Lucía River, Uruguay

Cited by 58 publications

References 38 publications

Assessment of Impacts of Climate Change on Tile Discharge and Nitrogen Yield Using the DRAINMOD Model

Assessment of Impacts of Climate Change on Tile Discharge and Nitrogen Yield Using the DRAINMOD Model

Water-Quality Data Imputation with a High Percentage of Missing Values: A Machine Learning Approach

A Comparison of Linear and Non-Linear Machine Learning Techniques (PCA and SOM) for Characterizing Urban Nutrient Runoff

Contact Info

Product

Resources

About