Quantifying climate, streamflow, and watershed control on water quality across Southeastern US watersheds

A model based on a multivariate classification for assessing impacts on water quality in a DOCE river watershed after the Fundão tailings dam failure

The main purpose of this study was to build multivariate classification models using water quality monitoring data for the hydrographic basin of the Gualaxo do Norte River, Minas Gerais state, Brazil, which was impacted in 2015 by the rupture of a containment structure for iron ore tailings. A total of 27 points were evaluated, covering areas affected and unaffected by the disaster, with monitoring of chemical, physical, and microbiological variables during the period from July 2016 to June 2017. Multivariate classification techniques were applied to the data, with the aim of developing models to determine when the impacted locations would present characteristics equivalent to those existing prior to the rupture. Classification models constructed using PLS-DA and LDA were able to predict three classes: unaffected main river, affected main river, and tributaries. The first technique was able to clearly differentiate the three classes for the data evaluated, achieving averages corresponding to 90% accuracy. The second method was consistent with the first, identifying the chloride content, conductivity, turbidity, and alkalinity as discriminatory variables, among those monitored, with the relationships among the parameters being coherent with the environmental conditions of the region. The model, with a correct classification rate of 91.67%, enabled identification of the behavior of new samples, using only these easily measured variables. In summary, application of the multivariate statistical tools allowed the development of models capable of providing information about the recovery process of an ecosystem impacted by the greatest environmental disaster to have occurred in Brazil.

Investigating extreme hydrological risk impact on water quality; evidence from Buffalo catchment headwater, Eastern Cape, South Africa

Evidence from increasing mineralization, micropollutant concentrations, waterborne epidemics, an algal boom, and dissolved organic matter has provided substantial evidence that climate change impacts water quality. While the impact of the extreme hydrological event (EHE) on water quality (WQ) has aroused considerable research interest, research uncertainty has been premised on WQ data scarcity, a short time frame, data non-linearity, data structure, and environmental biases on WQ. This study conceptualized a categorical and periodic correlation using confusion matrices and wavelet coherence for varying standard hydrological drought index (SHDI; 1971-2010) and daily WQ series (1977-2011) of four spatially distinct basins. By condensing the WQ variables using chemometric analyses, confusion matrices were assessed by cascading the SHDI series into 2-, 3-, and 5-phase scenarios. 2-phase revealed an overall accuracy (0.43-0.73), sensitivity analysis (0.52-1.00), and Kappa coefficient (- 0.13 to 0.14), which declines substantially with the phase increase, suggesting the disruptive impact of EHE on WQ. Wavelet coherence depicted the substantial ([Formula: see text]) mid- and long-term (8-32 days; 6-128 days) co-movement of streamflow over WQ, confirming the varying sensitivity of WQ variables. Land use/land cover mapping and the Gibbs diagram corroborate the eventful WQ evolution by EHE and their spatial variability concerning landscape transformation. Overall, the study deduced that hydrologic extreme triggers substantial WQ disruption with dissimilar WQ sensitivity. Consequently, suitable chemometric indicators of EHE impacts such as WQ index, nitrate-nitrogen, and Larson index at designated landscapes were identified for extreme chemodynamics impact assessment. This study proffers a recommendation for monitoring and managing the impact of climate change, floods, and drought on water quality.

Assessing the impact of watershed characteristics and management on nutrient concentrations in tropical rivers using a machine learning method

Excessive loadings of terrestrial nitrogen and phosphorus, as well as their imbalances with silicon, have been recognized as one of the major causes of water quality and ecosystem deterioration in receiving waters. In this study, a periodic water quality monitoring was conducted in the rivers and streams of a tropical island (Ishigaki Island, Japan) to identify the factors controlling the concentrations of dissolved inorganic nitrogen (DIN), total phosphorus (TP) and dissolved silicon (DSi) with a special focus on the catchment characteristics (e.g., land use, surface geology, topography). Random Forest (RF) machine learning algorithm was employed to develop predictive models for nutrient concentrations from the catchment properties. The developed models could predict nutrient concentrations with sufficient accuracy, demonstrating that the studied nutrients are strongly affected by catchment properties. Agricultural land uses (e.g., livestock barn, sugarcane field) were ranked as the most important parameters for DIN and TP, while broadleaf forest was the most influential factor for DSi. Using the RF models, the contributions of DIN originating from sugarcane fields (i.e., fertilizers) and barns (i.e., manure) to riverine DIN were estimated, which were up to 60% in total in the studied river basins. Furthermore, the yield of DIN from sugarcane fields, calculated as the concentration of DIN derived from sugarcane fields divided by the percent area of sugarcane fields, strongly positively correlated with the areal coverage of limestone, suggesting that fertilizer-derived DIN is more prone to leaching out from cropland soil to groundwater and rivers in catchments with a higher dominance of calcareous geology. These results, including the methodology employed, have implications for water quality assessment and management in inland and coastal waters not only at the study site but also other regions.

Water quality analysis based on phytoplankton and metal indices: a case study in the Sauce Grande River Basin (Argentina)

The increasing landscape alterations due to anthropogenic activities is of global concern since it affects aquatic ecosystems, often resulting in compromise of the ecological integrity and the water quality. In this sense, the evaluation, monitoring, and prediction of the aquatic ecosystem quality becomes an important research subject. This study presents the first integrated water quality assessment of the Sauce Grande River Basin, in Argentina, based on the spatial distribution of the phytoplankton community, the physicochemical parameters, and the metal concentrations (Cd, Cu, Cr, Fe, Mn, Ni, Pb, and Zn) found in the particulate fraction. According to the trophic indices and the phytoplankton abundance, composition, and diversity, the water quality showed significant deterioration in the lower basin after the Sauce Grande lake. The trophic state index indicated that water was oligotrophic in over 75% of the sampling sites, increasing downstream, where two sites were characterized as mesotrophic, and one described as hypertrophic. The phytoplankton community was dominated by diatoms in zones with low anthropogenic impact and conductivity, whereas high densities of Euglenophyta, Chlorophyta, and Cyanobacteria were found in the middle-lower basin, associated with higher organic matter and eutrophication. The conductivity, turbidity, and most metal concentrations also increased towards the downstream area, even exceeding recommended levels for the metals Cu, Cr, Mn, and Pb in the middle and lower reaches of the basin (Cu: 3.5 µg L^-1; Cr: 2.4 µg L^-1; Pb: 1.2 µg L^-1; Mn 170 µg L^-1). This study generates a database for the water quality of the Sauce Grande River Basin and sets an example of how the water quality varies along a basin that crosses different topographic environments, land covers, and anthropogenic influences.

Catchment-scale impacts of shallow landslides on stream water chemistry

Catchment water quality plays an important role in ecosystem and water resource management in mountainous areas. Shallow landslides triggered by earthquakes or heavy rainfall can cause a sudden and long-term deterioration in stream water quality by releasing contaminants into streams. Although many studies have been undertaken on the relationship between a single landslide and the water chemistry of a nearby river, little is known about the impact of densely distributed shallow landslides on stream water chemistry at the catchment scale. To this end, this study determined the major ion concentrations and isotopic compositions of stream water along with the shallow landslide area/catchment area ratio (LCR) in 37 headwater subcatchments in the southern part of Hokkaido, Japan, where an earthquake caused more than 6000 shallow landslides on September 6, 2018. In subcatchments with a high LCR, stream water exhibited significantly higher Ca²⁺ and HCO₃^- concentrations, while there was no correlation between the LCR and concentrations of Na⁺ and Cl^-. The δ¹⁸O and δD values of stream water plotted between the local meteoric water lines of summer and winter precipitation, indicating that they originated from meteoric water. Shallow landslides formed sliding surfaces, landslide deposits, and landslide-dammed lakes, which enhanced the interaction between the surface soil and stream water, leading to Ca-HCO₃ type water. The results showed that shallow-landslide-driven changes in stream water quality could be linearly approximated by the fraction of the landslide area at the catchment scale, which is a more versatile approach than the local framework of a single landslide and a nearby stream. In future research, these findings could be combined with a slope stability model and the background climatic, geological, topographical, and water quality conditions of a watershed to evaluate water pollution triggered by shallow landslides at the catchment scale.

Effects of change in streamflow patterns on water quality

Streamflow patterns are closely linked with the quality of stream water, but they are often dealt separately. Due to this, the effects of change in streamflow patterns resulting from river regulation and flow diversion on stream water quality remain under-investigated. This study models change in water quality indicators including pollutants (total suspended solids and turbidity), nutrients (total nitrogen and phosphorus), dissolved oxygen, nitrogen (kjeldahl), pH, and salinity caused by the change in streamflow patterns under different scenarios of river regulation, flow diversion, and rainfall. The generalized additive model was used and the Goulburn-Broken catchment, Australia was chosen as the case study. It was found that concentrations of pollutants and nutrients increased by 38% while dissolved oxygen and nitrogen (kjeldahl) decreased by 35% during the period 1990-2018. These changes were associated with an average increase of 20% in low and medium flows, an average decline of 22% in high and overbank flows and a 15% decline in rainfall. Under the scenario of climate change, river regulation and flow diversion, the overbank flow patterns would mimic the effects of low and medium flows on the water quality indicators that would raise the concentration of pollutants, nutrients, and salinity by 19%. Restoration of high flows would decrease these concentrations by 28% relative to current concentrations, however, it would also reduce dissolved oxygen, nitrogen (kjeldahl), and pH. Effects of streamflow patterns on water quality have implications for environmental flow management, thus, this study recommends critical adjustments in low, medium, and high flows for improving water quality.

Spatiotemporal Evaluation of Water Quality and Hazardous Substances in Small Coastal Streams According to Watershed Characteristics

In this study, spatial and temporal changes of eight water quality indicators and 30 types of hazardous substances including volatile organic compounds (VOCs), semi-volatile organic compounds (SVOCs), pesticides, and inorganic matters for the small coastal streams along the West Coast of South Korea were investigated. In coastal streams with clear seasonal changes in water quality, larger watershed areas led to greater contamination by particulate matter (i.e., suspended solids, r = 0.89), and smaller watershed areas led to greater contamination by organic matter (i.e., BOD, r = −0.78). The concentration of VOCs and pesticides was higher in agricultural areas, and those of SVOCs and metals were often higher in urban areas. According to the principal component analysis (PCA), during the wet season, the fluctuation in the water quality of coastal streams was higher in urban areas than in agricultural areas. Furthermore, coastal streams in residential areas exhibited higher levels of SVOCs, and those in industrial areas exhibited higher levels of metallic substances. Based on these results, the spatial and temporal trends of water quality and hazardous substances were obtained according to watershed characteristics, thereby clarifying the pollution characteristics of small-scale coastal streams and the major influencing factors.

Identifying Key Watershed Characteristics That Affect the Biological Integrity of Streams in the Han River Watershed, Korea

Understanding the complex human and natural processes that occur in watersheds and stream ecosystems is critical for decision makers and planners to ensure healthy stream ecosystems. This study aims to characterize the Han River watershed in Korea and extract key relationships among watershed attributes and biological indicators of streams using principal component analysis (PCA) and self-organizing maps (SOM). This study integrated watershed attributes and biological indicators of streams to delineate the watershed and stream biological status. Results from PCA strongly suggested that the proportions of watershed and riparian land use are key factors that explain the total variance in the datasets. Forest land in the watershed appeared to be the most significant factor. Furthermore, SOM planes showed that the biological indicators of streams have strong positive relationships with forest land, well-drained soil, and slope, whereas they have inverse relationships with urban areas, agricultural areas, and poorly drained soil. Hierarchical clustering classified the watersheds into three clusters, exclusively located in the study areas depending on the degree of forest, urban, and agricultural areas. The findings of this study suggest that different management strategies should be established depending on the characteristics of a cluster to improve the biological condition of streams.