The relative importance of environmental factors in predicting phytoplankton shifting and cyanobacteria abundance in regulated shallow lakes

Ecological evolution of algae in connected reservoirs under the influence of water transfer: Algal density, community structure, and assembly processes

Reservoir connectivity provides a solution for regional water shortages. Understanding the water quality of reservoirs and the response of algal communities to water transfer could provide the basis for a long-term evolutionary model of reservoirs. In this study, a water-algal community model was established to study the effects of water transfer on water quality and algal communities in reservoirs. The results showed that water transfer significantly decreased total nitrogen and nitrate concentrations. However, the water transfer resulted in an increase in the CODMn concentration and conductivity in the receiving reservoir. Additionally, the algal density and chlorophyll-a (chl-a) concentration showed an increase with water transfer. Bacillariophyta, Cyanophyta, and Chlorophyta were the dominant algal phyllum in all three reservoirs. Water transfer induced the evolution of the algal community by driving changes in the chemical parameters of the receiving reservoir and led to more complex relationships within the algal community. The effects of stochastic processes on algal communities were also enhanced in the receiving reservoirs. These results provide specific information for water quality safety management and eutrophication prevention in connected reservoirs.

Hysteresis analysis reveals how phytoplankton assemblage shifts with the nutrient dynamics during and between precipitation patterns

The escalation of global eutrophication has significantly increased due to the impact of climate change, particularly the increased frequency of extreme rainfall events. Predicting and managing eutrophication requires understanding the consequences of precipitation events on algal dynamics. Here, we assessed the influence of precipitation events throughout the year on nutrient and phytoplankton dynamics in a drinking water reservoir from January 2020 to January 2022. Four distinct precipitation patterns, namely early spring flood rain (THX), Plum rain (MY), Typhoon rain (TF), and Dry season (DS), were identified based on rainfall intensity, duration time, and cumulative rainfall. The study findings indicate that rainfall is the primary driver of algal dynamics by altering nutrient levels and TN:TP ratios during wet seasons, while water temperature becomes more critical during the Dry season. Combining precipitation characteristics with the lag periods between algal proliferation and rainfall occurrence is essential for accurately assessing the impact of rainfall on algal blooms. The highest algae proliferation occurred approximately 20 and 30 days after the peak rainfall during the MY and DS periods, respectively. This was influenced by the intensity and cumulative precipitation. The reservoir exhibited two distinct TN/TP ratio stages, with average values of 52 and 19, respectively. These stages were determined by various forms of nitrogen and phosphorus in rainfall-driven inflows and were associated with shifts from Bacillariophyta-dominated to Cyanophyta-dominated blooms during the MY and DS seasons. Our findings underscore the interconnected effects of nutrients, temperature, and hydrological conditions driven by diverse rainfall patterns in shaping algal dynamics. This study provides valuable insights into forecasting algal bloom risks in the context of climate change and developing sustainable strategies for lake or reservoir restoration.

Investigation of phytoplankton community structure and formation mechanism: a case study of Lake Longhu in Jinjiang

In order to explore the species composition, spatial distribution and relationship between the phytoplankton community and environmental factors in Lake Longhu, the phytoplankton community structures and environmental factors were investigated in July 2020. Clustering analysis (CA) and analysis of similarities (ANOSIM) were used to identify differences in phytoplankton community composition. Generalized additive model (GAM) and variance partitioning analysis (VPA) were further analyzed the contribution of spatial distribution and environmental factors in phytoplankton community composition. The critical environmental factors influencing phytoplankton community were identified using redundancy analysis (RDA). The results showed that a total of 68 species of phytoplankton were found in 7 phyla in Lake Longhu. Phytoplankton density ranged from 4.43 × 105 to 2.89 × 106 ind./L, with the average density of 2.56 × 106 ind./L; the biomass ranged from 0.58-71.28 mg/L, with the average biomass of 29.38 mg/L. Chlorophyta, Bacillariophyta and Cyanophyta contributed more to the total density, while Chlorophyta and Cryptophyta contributed more to the total biomass. The CA and ANOSIM analysis indicated that there were obvious differences in the spatial distribution of phytoplankton communities. The GAM and VPA analysis demonstrated that the phytoplankton community had obvious distance attenuation effect, and environmental factors had spatial autocorrelation phenomenon, which significantly affected the phytoplankton community construction. There were significant distance attenuation effects and spatial autocorrelation of environmental factors that together drove the composition and distribution of phytoplankton community structure. In addition, pH, water temperature, nitrate nitrogen, nitrite nitrogen and chemical oxygen demand were the main environmental factors affecting the composition of phytoplankton species in Lake Longhu.

Algal community structure prediction by machine learning

The algal community structure is vital for aquatic management. However, the complicated environmental and biological processes make modeling challenging. To cope with this difficulty, we investigated using random forests (RF) to predict phytoplankton community shifting based on multi-source environmental factors (including physicochemical, hydrological, and meteorological variables). The RF models robustly predicted the algal communities composed by 13 major classes (Bray-Curtis dissimilarity = 9.2 ± 7.0%, validation NRMSE mostly <10%), with accurate simulations to the total biomass (validation R² > 0.74) in Norway's largest lake, Lake Mjosa. The importance analysis showed that the hydro-meteorological variables (Standardized MSE and Node Purity mostly >0.5) were the most influential factors in regulating the phytoplankton. Furthermore, an in-depth ecological interpretation uncovered the interactive stress-response effect on the algal community learned by the RF models. The interpretation results disclosed that the environmental drivers (i.e., temperature, lake inflow, and nutrients) can jointly pose strong influence on the algal community shifts. This study highlighted the power of machine learning in predicting complex algal community structures and provided insights into the model interpretability.

Estimation of nutrient sources and fate in groundwater near a large weir-regulated river using multiple isotopes and microbial signatures

The excessive input of nutrients into groundwater can accelerate eutrophication in associated surface water systems. This study combined hydrogeochemistry, multi isotope tracers, and microbiological data to estimate nutrient sources and the effects of groundwater-surface water interactions on the spatiotemporal variation of nutrients in groundwater connected to a large weir-regulated river in South Korea. δ11B and δ15N-NO3- values, in combination with a Bayesian mixing model, revealed that manure and sewage contributed 40 % and 25 % respectively to groundwater nitrate, and 42 % and 27 % to nitrate in surface water during the wet season. In the dry season, the source apportionment was similar for groundwater while the sewage contribution increased to 52 % of nitrate in river water. River water displayed a high correlation between NO3- concentration and cyanobacteria (Microcystis and Prochlorococcus) in the wet season. The mixing model using multiple isotopes indicated that manure-derived nutrients delivered with increased contributions of groundwater to the river during the wet season governed the occurrence of cyanobacterial blooms in the river. We postulate that the integrated approach using multi-isotopic and microbiological data is highly effective for evaluating nutrient sources and for delineating hydrological interactions between groundwater and surface water, as well as for investigating surface water quality including eutrophication in riverine and other surface water systems.

Novel insights in seasonal dynamics and co-existence patterns of phytoplankton and micro-eukaryotes in drinking water reservoir, Northwest China: DNA data and ecological model

Although associations between phytoplankton and micro-eukaryotes have been studied in aquatic ecosystems, there are still knowledge gaps in comprehending their dynamics and interactions in drinking water reservoirs. Here, the seasonal dynamics of phytoplankton and micro-eukaryotic diversities and their co-existence patterns were studied in a drinking water reservoir, Northwest China. The highest phytoplankton diversity was observed in summer, and Chlorella sp. that belongs to Chlorophyta was the most abundant genus. The highest eukaryotic diversity was also detected in summer, and Rimostrombidium sp. that belongs to Ciliophora was the most dominant genus. Mantel test showed that the phytoplankton diversity was significantly correlated with ammonia nitrogen (r = 0.561, p = 0.001) and dissolved organic carbon (r = 0.267, p = 0.017), while the eukaryotic diversity was significantly associated with ammonia nitrogen (r = 0.265, p = 0.034) and temperature (r = 0.208, p = 0.046). PLS-PM (Partial Least Squares Path Modeling) further revealed that nutrients (P < 0.01) significantly affected the phytoplankton diversity, while nutrients (P < 0.01) and temperature (P < 0.01) significantly influenced the eukaryotic diversity. Co-occurrence network displayed the primarily positive interactions (77.66% positive and 22.34% negative) between phytoplankton and micro-eukaryotes. These findings could deepen our understanding of interactions between phytoplankton and micro-eukaryotes and their driving factors under changing aquatic environments of drinking water reservoirs.

The spatiotemporal variations in microalgae communities in vertical waters of a subtropical reservoir

The construction of cascade reservoirs increases eutrophication and exacerbates algal blooms and thus threatens water quality. Previous studies on the microalgae in reservoir have mainly focused on the spatio-temporal patterns of surface microalgae communities at the horizontal scale, while few studies have simultaneously considered the successions of microalgae in vertical profiles including the sediments and the effects of the nutrients release and microalgae in sediments on microalgae in upper waters. In this study, we investigated the effects of microalgae and physico-chemical parameters in waters and sediments on the successions of vertical microalgae communities in Xipi Reservoir, Southeast China. The seasonal variations in microalgae compositions decreased gradually from the surface water (the dominance of Cryptophyta and Chlorophyta in spring, Chlorophyta and Cyanophyta in summer, and relatively uniform in autumn and winter) to the sediment (the dominance of Bacillariophyta throughout the year), which was influenced by the variations of physico-chemical factors in different layers. The spatio-temporal variations in microalgae communities in waters was attributing to not only the heterogeneities of the stratification, and the physico-chemical factors such as water temperature, pH, and nutrient concentrations, especially for phosphorus in the water column, but also the combinations of phosphorus release and microalgae composition in sediments. Environmental changes would be especially problematic for microalgae groups such as Cryptophyta, Dinophyta and Chlorophyta that were sensitive to the changes of temperature and nutrients. Our results are helpful for an extensive understanding of the dynamics of microalgae communities in reservoir, and contribute to reservoir management for ensuring the safety of drinking water.

Prediction of Total Nitrogen and Phosphorus in Surface Water by Deep Learning Methods Based on Multi-Scale Feature Extraction

To improve the precision of water quality forecasting, the variational mode decomposition (VMD) method was used to denoise the total nitrogen (TN) and total phosphorus (TP) time series and obtained several high- and low-frequency components at four online surface water quality monitoring stations in Poyang Lake. For each of the aforementioned high-frequency components, a long short-term memory (LSTM) network was introduced to achieve excellent prediction results. Meanwhile, a novel metaheuristic optimization algorithm, called the chaos sparrow search algorithm (CSSA), was implemented to compute the optimal hyperparameters for the LSTM model. For each low-frequency component with periodic changes, the multiple linear regression model (MLR) was adopted for rapid and effective prediction. Finally, a novel combined water quality prediction model based on VMD-CSSA-LSTM-MLR (VCLM) was proposed and compared with nine prediction models. Results indicated that (1), for the three standalone models, LSTM performed best in terms of mean absolute error (MAE), mean absolute percentage error (MAPE), and the root mean square error (RMSE), as well as the Nash–Sutcliffe efficiency coefficient (NSE) and Kling–Gupta efficiency (KGE). (2) Compared with the standalone model, the decomposition and prediction of TN and TP into relatively stable sub-sequences can evidently improve the performance of the model. (3) Compared with CEEMDAN, VMD can extract the multiscale period and nonlinear information of the time series better. The experimental results proved that the averages of MAE, MAPE, RMSE, NSE, and KGE predicted by the VCLM model for TN are 0.1272, 8.09%, 0.1541, 0.9194, and 0.8862, respectively; those predicted by the VCLM model for TP are 0.0048, 10.83%, 0.0062, 0.9238, and 0.8914, respectively. The comprehensive performance of the model shows that the proposed hybrid VCLM model can be recommended as a promising model for online water quality prediction and comprehensive water environment management in lake systems.

Comprehensive modelling and cost-benefit optimization for joint regulation of algae in urban water system

Algal blooms in urban water system is an international concern, which especially in China, have become a major obstacle to the urban water environment improvement since the preliminary achievements were made in the treatment of black and odorous water bodies. The complex blooming mechanisms require a joint regulation plan. This study established a framework that consisted of three steps, i.e., simulation, optimization, and verification, to build an optimal joint regulation plan. By taking the urban river network in Suzhou Pingjiang Xincheng as a case study, the cost-benefits of six alternative regulation measures were assessed using an algal bloom mechanism model and the discounted cash flow model based on 70 regulation scenarios. The joint regulation plan was optimized using the marginal-cost-based greedy strategy on the basis of the cost-benefits of different measures. The optimized joint plans, which were verified to be global optima, were more cost-effective than the designed regulation scenarios, and reduced the average chlorophyll-a concentrations by 55.3%-60.1% compared with the status quo. Applying the optimized cost allocation ratios of each measure to adjust the existing regulation scheme of another similar case verified that the optimization results had great generalizability.