The paper presents one of the methods for calculation of temperature of a water course, downstream of the relase of a thermo-electric power-plant, taking into consideration the well-known temperatures and discharge values of the tributaries under different meteorological conditions. In view of the importance of temperature regime for the water eco-system, understanding the influence of the power plants on thermic capacity of the water course, is essential in design and use of the power plant. The author's intention is to help towards improving computer models for the computation of a water course thermic capacity. To this end a model developed at the Institute for Meteorology and Water Management in Poland and applied in many coutries has been presented.

Thermal stratification is regularly found in deep lakes, while shallow water bodies remain usually mixed. Despite comparative shallowness, Sava lake (artificial lake), Serbia (Yugoslavia) exibit a prolonged summer stable stratification. Temperature differences between epilimnion and hypolimnion can reach 10 deg C. The steep gradients of up to 3.5 deg C/meter recorded in the metalimnion. The highest differences during a single typical summer day was 1.7 deg C between 0.2 and 0.5 m. Maximum annual thermal accumulation was 112440 J/square cm in 1996.

Understanding the key drivers of eutrophication in floodplain lakes has long been a challenge. In this study, the Chlorophyll a (Chla) variations and associated relationships with environmental stressors along the temporal hydrological connectivity gradient were investigated using a 11-year dataset in a large floodplain lake (Poyang Lake). A geostatistical method was firstly used to calculate the hydrological connectivity curves for each sampling campaign that was further classified by K-means technique. Linear mixed effect (LME) models were developed through the inclusion of the site as a random effect to identify the limiting factors of Chla variations. The results identified three clear hydrological connectivity variation patterns with remarkable connecting water area changes in Poyang Lake. Furthermore, hydrological connectivity changes exerted a great influence on environmental variables in Poyang Lake, with a decrease in nutrient concentrations as the hydrological connectivity enhanced. The Chla exhibited contrast variations with nutrient variables along the temporal hydrological connectivity gradient and generally depended on WT, DO, EC and TP, for the entire study period. Nevertheless, the relative roles of nutrient and non-nutrient variables in phytoplankton growth varied with different degrees of hydrological connectivity as confirmed by the LME models. In the low hydrological connectivity phase, the Chla dynamics were controlled only by water temperature with sufficient nutrients available. In the high hydrological connectivity phase, the synergistic influences of both nutrient and physical variables jointly limited the Chla dynamics. In addition, a significant increasing trend was observed for Chla variations from 2008 to 2018 in the HHC phase, which could largely be attributed to the elevated nutrient concentrations. This study confirmed the strong influences of hydrological connectivity on the nutrient and non-nutrient limitation of phytoplankton growth in floodplain lakes. The present study could provide new insights on the driving mechanisms underlying phytoplankton growth in floodplain lakes.

Predicting the occurrence of algal blooms is of great importance in managing water quality. Moreover, the demand for predictive models, which are essential tools for understanding the drivers of algal blooms, is increasing with global warming. However, modeling cyanobacteria dynamics is a challenging task. We developed a multivariate Chain-Bernoulli-based prediction model to effectively forecast the monthly sequences of algal blooms considering hydro-environmental predictors (water temperature, total phosphorus, total nitrogen, and water velocity) at a network of stations. The proposed model effectively predicts the risk of harmful algal blooms, according to performance measures based on categorical metrics of a contingency table. More specifically, the model performance assessed by the LOO cross-validation and the skill score for the POD and CSI during the calibration period was over 0.8; FAR and MR were less than 0.15. We also explore the relationship between hydro-environmental predictors and algal blooms (based on cyanobacteria cell count) to understand the dynamics of algal blooms and the relative contribution of each potential predictor. A support vector machine is applied to delineate a plane separating the presence and absence of algal bloom occurrences determined by stochastic simulations using different combinations of predictors. The multivariate Chain-Bernoulli-based prediction model proposed here offers effective, scenario-based, and strategic options and remedies (e.g., controlling the governing environmental predictors) to relieve or reduce increases in cyanobacteria concentration and enable the development of water quality management and planning in river systems.

Cyanobacterial blooms producing toxic metabolites occur frequently in freshwater, yet the environmental behaviors of complex cyanobacterial metabolites remain largely unknown. In this study, the seasonal and spatial variations of several classes of cyanotoxins (microcystins, cylindrospermopsins, saxitoxins) and taste-and-odor (T&O) compounds (β-cyclocitral, β-ionone, geosmin, 2-methylisoborneol) in Lake Taihu were simultaneously investigated for the first time. The total cyanotoxins were dominated by microcystins with concentrations highest in November (mean 2209 ng/L) and lowest in February (mean 48.7 ng/L). Cylindrospermopsins were abundant in May with the highest content of 622.8 ng/L. Saxitoxins only occurred in May (mean 19.2 ng/L) and November (mean 198.5 ng/L). Extracellular T&O compounds were most concentrated in August, the highest being extracellular β-cyclocitral (mean 240.6 ng/L) followed by 2-methylisoborneol (mean 146.6 ng/L). Environment variables play conflicting roles in modulating the dynamics of different groups of cyanotoxins and T&O compounds. Total phosphorus (TP), total nitrogen (TN), chlorophyll-a and cyanobacteria density were important factors affecting the variation of total microcystins, β-cyclocitral and β-ionone concentrations. In contrast, total cylindrospermopsins, 2-methylisoborneol and geosmin concentrations were significantly influenced by water temperature and TP. There was a significant and linear relationship between microcystins and β-cyclocitral/β-ionone, while cylindrospermopsins were positively correlated with 2-methylisoborneol and geosmin. The perceptible odors may be good indicators for the existence of cyanotoxins. Hazard quotients revealed that potential human health risks from microcystins were high in August and November. Meanwhile, the risks from cylindrospermopsins were at moderate levels. Cylindrospermopsins and saxitoxins were first identified in this lake, suggesting that diverse cyanotoxins might co-occur more commonly than previously thought. Hence, the risks from other cyanotoxins beyond microcystins shouldn't be ignored. This study also highlights that the necessity for further assessing the combination effects of these complex metabolites.

The Yellow River is the second largest river in China. Carbon transport by the Yellow River has significant influence on riverine carbon cycles in Asia. During the wet season, the riverine carbon was mainly found in dissolved form, i.e., dissolved organic carbon (DOC), along the entire course of the river. The distinct spatial variations of DOC concentration were observed at different reaches of the mainstream (p < 0.01), while the highest mean DOC concentration was generally observed at midstream (4.13 ± 0.91 mg/L). Carbon stable isotope analysis δ¹³C and C: N ratio of DOC, evidenced the sources of DOC in headwater and upstream were primarily the terrestrial plants (94% and 61%), but it was changed to soil organic matter (SOM) in mid- and downstream (36% and 37%), and the contribution of sewage to DOC were also increased to 17% and 18%. In the whole mainstream of the Yellow River, water temperature (WT) had a significant impact on DOC concentration, and it could explain 67% of the DOC variance. However, in a large catchment, the driving mechanisms on the DOC variations in headwaters will not necessarily be those controlling DOC trends in downstream. The study firstly quantified, in headwater and upstream, the natural factors explained as much as 65% and 73% of the DOC variations, respectively. In mid- and downstream areas, DOC was significantly influenced by the amount of wastewater discharged by the industry and the use of chemical fertilizers (p < 0.05). These findings may facilitate a better assessment of global riverine carbon cycling and may help to reveal the importance of the balance between development and environmental sustainability with the changing DOC transport features in the Yellow River due to human disturbances.

Cyanobacterial harmful algal blooms (CyanoHABs) have been found to transmit from N₂ fixer-dominated to non-N₂ fixer-dominated in many freshwater environments when the supply of N decreases. To elucidate the mechanisms underlying such “counter-intuitive” CyanoHAB species succession, metatranscriptomes (biotic data) and water quality-related variables (abiotic data) were analyzed weekly during a bloom season in Harsha Lake, a multipurpose lake that serves as a drinking water source and recreational ground. Our results showed that CyanoHABs in Harsha Lake started with N₂-fixing Anabaena in June (ANA stage) when N was high, and transitioned to non-N₂-fixing Microcystis- and Planktothrix-dominated in July (MIC-PLA stage) when N became limited (low TN/TP). Meanwhile, the concentrations of cyanotoxins, i.e., microcystins were significantly higher in the MIC-PLA stage. Water quality results revealed that N species (i.e., TN, TN/TP) and water temperature were significantly correlated with cyanobacterial biomass. Expression levels of several C- and N-processing-related cyanobacterial genes were highly predictive of the biomass of their species. More importantly, the biomasses of Microcystis and Planktothrix were also significantly associated with expressions of microbial genes (mostly from heterotrophic bacteria) related to processing organic substrates (alkaline phosphatase, peptidase, carbohydrate-active enzymes) and cyanophage genes. Collectively, our results suggest that besides environmental conditions and inherent traits of specific cyanobacterial species, the development and succession of CyanoHABs are regulated by co-occurring microorganisms. Specifically, the co-occurring microorganisms can alleviate the nutrient limitation of cyanobacteria by remineralizing organic compounds.

Oil spills are a serious environmental problem. To better support risk assessment and pollution control for oil spills, a good understanding of oil transport in the environment is required. This study focused on the numerical simulation of the nearshore oil behaviors based on computational fluid dynamics. Based on the Navier-Stokes momentum equations for an incompressible viscous fluid and volume of fluid (VOF) method, a 3D numerical model of three-phase transient flow was developed. The wave number, averaged flow velocity and oil properties would affect the oil spread extent and the oil volume fraction. The higher the averaged flow velocity and wave number, the lower the oil concentration, and the faster the horizontal movement of the oil. The spilled oil may move to contact the seafloor by increasing the averaged flow velocity at the inlet boundary. Through increasing the wave number, the oil would stay near the water surface. In the nearshore, where the wave is the main seawater motion, the oil containment boom should be set preferentially to the direction of wave transmission for oil cleaning. This study shows that by doubling the wave number and increasing the averaged flow velocity (ten times) at the same time, the maximum oil volume fraction would be reduced by around 32%. Finally, the water temperature had no significant impact on the oil migration, and the impact of evaporation should be considered in the simulation.

Dissolved oxygen (DO) is an effective indicator for water pollution. However, since DO is a non-optically active parameter and has little impact on the spectrum captured by satellite sensors, research on estimating DO by remote sensing at multiple spatiotemporal scales is limited. In this study, the support vector regression (SVR) models were developed and validated using the remote sensing reflectance derived from both Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data and synchronous DO measurements (N = 188) and water temperature of Lake Huron and three other inland waterbodies (N = 282) covering latitude between 22–45 °N. Using the developed models, spatial distributions of the annual and monthly DO variability since 1984 and the annual monthly DO variability since 2000 in Lake Huron were reconstructed for the first time. The impacts of five climate factors on long-term DO trends were analyzed. Results showed that the developed SVR-based models had good robustness and generalization (average R² = 0.91, root mean square percentage error = 2.65%, mean absolute percentage error = 4.21%), and performed better than random forest and multiple linear regression. The monthly DO estimates by Landsat and MODIS data were highly consistent (average R² = 0.88). From 1984 to 2019, the oxygen loss in Lake Huron was 6.56%. Air temperature, incident shortwave radiation flux density, and precipitation were the main climate factors affecting annual DO of Lake Huron. This study demonstrated that using SVR-based models, Landsat and MODIS data could be used for long-term DO retrieval at multiple spatial and temporal scales. As data-driven models, combining spectrum and water temperature as well as extending the training set to cover more DO conditions could effectively improve model robustness and generalization.

Real-time river chloride prediction has received a lot of attention for its importance in chloride control and management. In this study, an artificial neural network model (i.e., multi-layer perceptron, MLP) and a statistical inference model (i.e., stepwise-cluster analysis, SCA) are developed for predicting chloride concentration in stream water. Then, an ensemble learning model based on MLP and SCA is proposed to further improve the modeling accuracy. A case study of hourly river chloride prediction in the Grand River, Canada is presented to demonstrate the model applicability. The results show that the proposed ensemble learning model, MLP-SCA, provides the best overall performance compared with its two ensemble members in terms of RMSE, MAPE, NSE, and R² with values of 11.58 mg/L, 27.55%, 0.90, and 0.90, respectively. Moreover, MLP-SCA is more competent for predicting extremely high chloride concentration. The prediction of observed concentrations above 150 mg/L has RMSE and MAPE values of 9.88 mg/L and 4.40%, respectively. The outstanding performance of the proposed MLP-SCA, particularly in extreme value prediction, indicates that it can provide reliable chloride prediction using commonly available data (i.e., conductivity, water temperature, river flow rate, and rainfall). The high-frequency prediction of chloride concentration in the Grand River can supplement the existing water quality monitoring programs, and further support the real-time control and management of chloride in the watershed. MLP-SCA is the first ensemble learning model for river chloride prediction and can be extended to other river systems for water quality prediction.