The prediction of algal chlorophyll-a and water clarity in lentic ecosystems is a hot issue due to rapid deteriorations of drinking water quality and eutrophication processes. Our key objectives of the study were to predict long-term algal chlorophyll-a and transparency (water clarity), measured as Secchi depth, in spatially heterogeneous and temporally dynamic reservoirs largely influenced by the Asian monsoon during 2000–2017 and then determine the reservoir trophic state using a multiple linear regression (MLR), support vector machine (SVM) and artificial neural network (ANN). We tested the models to analyze the spatial patterns of the riverine zone (Rz), transitional zone (Tz) and lacustrine zone (Lz) and temporal variations of premonsoon, monsoon and postmonsoon. Monthly physicochemical parameters and precipitation data (2000–2017) were used to build up the models of MLR, SVM and ANN and then were confirmed by cross-validation processes. The model of SVM showed better predictive performance than the models of MLR and ANN, in both before validation and after validation. Values of root mean square error (RMSE) and mean absolute error (MAE) were lower in the SVM model, compared to the models of MLR and ANN, indicating that the SVM model has better performance than the MLR and ANN models. The coefficient of determination was higher in the SVM model, compared to the MLR and ANN models. The mean and maximum total suspended solids (TSS), nutrients (total nitrogen (TN) and total phosphorus (TP)), water temperature (WT), conductivity and algal chlorophyll (CHL-a) were in higher concentrations in the riverine zone compared to transitional and lacustrine zone due to surface run-off from the watershed. During the premonsoon and postmonsoon, the average annual rainfall was 59.50 mm and 54.73 mm whereas it was 236.66 mm during the monsoon period. From 2013 to 2017, the trophic state of the reservoir on the basis of CHL-a and SD was from mesotrophic to oligotrophic. Analysis of the importance of input variables indicated that WT, TP, TSS, TN, NP ratios and the rainfall influenced the chlorophyll-a and transparency directly in the reservoir. These findings of the algal chlorophyll-a predictions and Secchi depth may provide key clues for better management strategy in the reservoir.

Paldang Reservoir, located in the Han River basin in South Korea, is used for drinking water, fishing, irrigation, recreation, and hydroelectric power. Therefore, the water quality of the reservoir is of great importance. The main objectives of this study were to evaluate spatial and seasonal variations of surface water quality in the reservoir using multivariate statistical techniques (MSTs) along with the Trophic State Index (TSI) and Trophic State Index deviation (TSID). The empirical relationships among nutrients (total phosphorus, TP; total nitrogen, TN), chlorophyll-a (CHL-a), and annual variations of water quality parameters were also determined. To this end, 12 water quality parameters were monitored monthly at five sites along the reservoir from 1996 to 2019. Most of the parameters (all except pH, dissolved oxygen (DO), and total coliform bacteria (TCB)) showed significant spatial variations, indicating an influence of anthropogenic activities. Principal component analysis combined with factor analysis (PCA/FA) suggested that the parameters responsible for water quality variations were primarily correlated with nutrients and organic matter (anthropogenic), suspended solids (both natural and anthropogenic), and ionic concentrations (both natural and anthropogenic). Stepwise spatial discriminant analysis (DA) identified water temperature (WT), DO, electrical conductivity (EC), chemical oxygen demand (COD), the ratio of biological oxygen demand (BOD) to COD (BOD/COD), TN, TN:TP, and TCB as the parameters responsible for variations among sites, and seasonal stepwise DA identified WT, BOD, and total suspended solids (TSS) as the parameters responsible for variations among seasons. COD has increased (R² = 0.63, p < 0.01) in the reservoir since 1996, suggesting that nonbiodegradable organic loading to the water body is rising. The empirical regression models of CHL-a-TP (R² = 0.45) and CHL-a-TN (R² = 0.27) indicated that TP better explained algal growth than TN. The mean TSI values for TP, CHL-a, and Secchi depth (SD) indicated a eutrophic state of the reservoir for all seasons and sites. Analysis of TSID suggested that blue-green algae dominated the algal community in the reservoir. The present results show that a significant increase in algal chlorophyll occurs during spring in the reservoir. Our findings may facilitate the management of Paldang Reservoir.

This study assessed spatial and temporal variations of water quality to identify and quantify possible pollution sources affecting the Yeongsan River using multivariate statistical techniques (MSTs) and water quality index (WQI) values. A 15 year dataset of 11 water quality variables was used, covering 16 monitoring sites. The nutrient regime, organic matter, suspended solids, ionic contents, algal growth, and total coliform bacteria (TCB) were affected by the summer monsoon and the construction of weirs. Regression analysis showed that the algal growth was more highly regulated by total phosphorus (TP; R² = 0.37) than total nitrogen (TN, R² = 0.25) and TN/TP (R² = 0.01) ratios in the river after weir construction and indicated that the river is a P-limited system. After constructing the weirs, the mean TN/TP ratio in the river was about 40, meaning it is a P-limited system. Cluster analysis was used to classify the sampling sites into highly, moderately, and less polluted sites based on water quality features. Stepwise discriminant analysis showed that pH, dissolved oxygen (DO), TN, biological oxygen demand (BOD), chemical oxygen demand (COD), chlorophyll-a (CHL-a), and TCB are the spatially discriminating parameters, while pH, water temperature, DO, electrical conductivity, total suspended solids, and COD are the most significant for discriminating among the three seasons. The Pearson network analysis showed that nutrients flow with organic matter in the river, while CHL-a showed the highest correlation with COD (r = 0.85), followed by TP (r = 0.49) and TN (r = 0.49). Average WQI values ranged from 55 to 141, indicating poor to unsuitable water quality in the river. The Mann–Kendall test showed increasing trends in COD and CHL-a but decreasing trends for TP, TN, and BOD due to impoundment effects. The principal component analysis combined with factor analysis and positive matrix factorization (PMF) showed that two sewage treatment plants, agricultural activities, and livestock farming adversely impacted river water quality. The PMF model returned greater R² values for BOD (0.92), COD (0.87), TP (0.93), TN (0.91), CHL-a (0.93), and TCB (0.83), indicating reliable apportionment results. Our results suggest that MSTs and WQI can be effectively used for the simple interpretation of large-scale datasets to determine pollution sources and their spatiotemporal variations. The outcomes of our study may aid policymakers in managing the Yeongsan River.

Long-term variations in reservoir water chemistry could provide essential data in making sustainable water quality management decisions. Here, we analyzed the spatiotemporal variabilities of nutrients, sestonic chlorophyll-a (CHL-a), nutrient enrichment, dominant algal species, and overall chemical water health of the third-largest drinking water reservoir in South Korea during 2000–2020. Our results distinctly explained the strong influence of monsoon rainfall on spatial and annual water chemistry variations. We observed a consistent increase in the chemical oxygen demand alluding to organic matter pollutants, while a steady declining trend in the sestonic CHL-a. The long-term total phosphorus (TP) level showed a steady reduction from the riverine zone to the lacustrine area. However, a higher total coliform bacteria (TCB) was observed at the water intake tower sites. TP displayed a strong link to algal CHL-a and ambient nitrogen phosphorus ratios, suggesting a robust phosphorus-limitation state. The severe phosphorus-limitation was also corroborated by the findings of trophic state index deviation. The high and low flow dynamics exhibited the strong influence of intensive rainfall carrying many nutrients and sediments and flushing out the sestonic CHL-a. Successive eutrophic conditions prevailed along with dominating blue-green algae species (Microcystis and Anabaena). We observed a strong positive correlation (r = 0.62) between water temperature and CHL-a and between total suspended solids and TP (r = 0.65). The multi-metric water pollution index characterized the overall water quality as ‘good’ at all the study sites. In conclusion, the long-term spatiotemporal variabilities of the ecological functions based on the nutrient-CHL-a empirical models are regulated mainly by the intensive monsoon precipitation. The drinking water could become hazardous under the recurrent eutrophication events and chemical degradations due to uncontrolled and untreated inflow of sewage and wastewater treatment plant effluents. Therefore, we strongly advocate stringent criteria to mitigate phosphorus and organic pollutant influx for sustainable management of Daecheong Reservoir.

Due to rapid eutrophication, sustainable water quality management and supply are essential in drinking water sources and aquatic biota in large reservoirs. We evaluated the potentially crucial factors influencing the algal chlorophyll (CHL-a), nutrients, and the links between the rainfall and other vital elements in a large multipurpose reservoir (Yongdam Reservoir) during 2013–2019. We developed the empirical models on algal CHL-a, total phosphorus (TP), total nitrogen (TN), and TN:TP’s ambient ratios considering the maneuvering influence of Asian monsoons. The intensive rainfall during the monsoon months strongly impacted the nutrient regime and other vital factors. The seasonal patterns of algal CHL-a varied in response to the nutrient contents (TN, TP), suspended solids, and ambient N:P ratios along the longitudinal gradient. The conditional plot analysis, empirical modeling, and observations supported an overall P-limitation scenario, as was evident from the magnitude of N:P ratios (R² = 0.36, F = 24.9, p < 0.001). Furthermore, the reservoir’s trophic status alluded to the larger particles and blue-green algae during the monsoon and postmonsoon months. The correlation analysis, Mann–Kendall trend test, and principal component analysis illustrated compelling links between CHL-a, TP, and rainfall regime. The outcomes suggested the reservoir was primarily controlled by phosphorus limitation, with an increasing CHL-a tendency along with nitrogen dilution. However, a slight decline in phosphorus was also detected. The Yongdam Reservoir is under the threat of recurrent eutrophication events that could jeopardize this vital drinking water facility due to increasing agricultural and anthropic activities.

River health is one of the important issues today because of various threats by multiple anthropogenic stressors that have long-term impacts on the physical habitats, biodiversity, ecological functions, and their services. The main objectives of this study is to diagnose the chemical and biological river health in the watershed of Geum River with regard to the chemical regimes (N, P) and fish community using multi-metric chemical pollution index (CPI), and the index of biotic integrity model (IBI), respectively. The empirical models of sestonic chlorophyll, nutrients (N, P), and nutrient ratios of N:P indicated that the watershed, including all sampling sites, was a phosphorus-limited system. Analysis of fish trophic and tolerance guilds showed that the omnivore fish species and tolerant fish species were dominant in the watershed, while the sensitive fish species decreased downstream because of nutrient enrichments (such as TN, TP) and organic matter pollutions (such as BOD, COD). The chemical model of CPI showed that 11 sampling sites were in the fair—good condition, and 8 sites were in poor—very poor condition. Species composition analysis indicated that Zacco platypus was most widely distributed in the watersheds and dominated the fish community. The biological health of the watershed, based on the multimetric IBI model, was in poor condition and was getting worse downstream. The degradation of the river health was matched with the chemical health and showed a decreased abundance of insectivores and sensitive fish species. The outcomes of the river health were supported by principal component analysis (PCA) and cluster analysis (CA) of fish model metrics and the physicochemical parameters. Overall, our study suggests that river health was directly influenced by the chemical pollutions of nutrients and organic matter inputs.