Eutrophic Lake In China Research Articles

Microbial nitrogen cycling in Microcystis colonies and its contribution to nitrogen removal in eutrophic Lake Taihu, China

Cyanobacterial blooms induced by excessive loadings of nitrogen (N) and other nutrients are a severe ecological problem in aquatic ecosystems. Previous studies of N removal have primarily focused on sediment-water interface, yet the role of cyanobacterial colonies has recently been attracting more research attention. In this study, N cycling processes were quantified for cyanobacterial colonies (primarily Microcystis colonies) and their contribution to N removal was estimated for a large, shallow eutrophic lake in China, Lake Taihu. Various N cycling processes were determined via stable 15N isotope, together with 16S rRNA gene sequencing and quantitative microbial element cycling (QMEC) chip. Denitrification was found to be the most prominent process, estimated to be 36.63, 9.85, 3.35, and 3.15 times higher than dissimilatory nitrate reduction to ammonium (DNRA), nitrification, ammonium (NH4+) uptake and nitrate (NO3−) uptake rates, respectively. Denitrifiers accounted for a large part of the bacterial taxa (35.50 ± 24.65%), and the nirS gene was the most abundant among N cycling-related genes, with (2.54 ± 0.51) × 109 copies g−1Microcystis colonies. A field investigation revealed a positive correlation between the potential denitrification rate and the Chl-a concentration (mostly derived from Microcystis colonies). Based on a multiple stepwise regression model and historical data from 2007 to 2015 for Lake Taihu, the total amount of N removed via denitrification by Microcystis colonies was estimated at 171.72 ± 49.74 t yr−1; this suggests that Microcystis colonies have played an important role in N removal in Lake Taihu since the drinking water crisis in 2007. Overall, this study revealed the importance of denitrification within Microcystis colonies for N removal in eutrophic lakes, like Lake Taihu.

Spatial dynamics of pCO2 and CO2 emissions from eutrophic lakes

Eutrophication of lakes exhibits a more serious trend in a global scale under the climate change. Eutrophic lakes also exhibit a unique role in global carbon, yet, data on carbon dioxide (CO2) emissions from these eutrophic lakes are too scarce to accurately upscale CO2 emissions for large scale or global estimation. This study presented the results of a partial pressure of carbon dioxide (pCO2) survey covering 43 eutrophic lakes in China in the same season across diverse ecosystem types and climate zones. The first estimation of CO2 flux from Chinese eutrophic lakes was reported as − 0.7 to 1.0 Tg C a−1 based on the surveyed pCO2 values, indicating some lakes act as CO2 sinks while others are sources. The results showed that pCO2 in Chinese eutrophic lakes ranged from 0.9 to 1366.4 μatm with the mean of 356.9 ± 310.3 μatm, almost 69.3 % of the sampling sites in eutrophic lakes were undersaturated with CO2 to the atmosphere. The mean pCO2 variations in eutrophic lakes were consistent with the amount of precipitation across different climate zones, with the trend of semiarid > semihumid > humid regions, and all lower than that for atmosphere. Meanwhile, both the mean and median values of pCO2 went down with the decrease of the lake mean depth and the increasing lake area, and the highest value in deep lakes (mean depth > 10 m, 548.3 ± 353.7 μatm) and small lakes (376.6 ± 329.8 μatm). In all the studied eutrophic lakes, pCO2 values showed significant seasonal variations, and in summer and autumn showed an under-saturated seasonal mean value. This study proved that CO2 undersaturation was prevalent in the eutrophic conditions. Due to the CO2 source-sink conversion role of eutrophic lakes, it is recommended to focus on CO2 fluxs from eutrophic lakes in the first place during the research of inland waters carbon emissions.

Synergy of lacustrine groundwater discharge and algal biomass on CH4 and CO2 pathways and emissions in a large shallow eutrophic lake

Inland lakes are known to be a significant component of the global carbon (C) cycle, with C pathways in these lakes potentially changing alongside their eutrophic states. One factor contributing to eutrophication is lacustrine groundwater discharge (LGD), which is also a key conduit for CH4 and CO2 to enter lakes. However, the regulation of LGD on CH4 and CO2 behavior in these lakes remains unclear. To address this gap, we conducted continuous monitoring of 222Rn, CH4 and CO2, and δ13C at two sites with distinct algal densities in Lake Taihu, a large shallow eutrophic lake in China, and aimed to investigate the synergy of LGD and algal biomass on CH4 and CO2 pathways and emissions. We found that organic matter (OM) degradation is the dominant factor controlling CH4 and CO2 production in the lake, and this process appears to be facilitated by the presence of ample OM and reduced oxygen level at the site with high algal density. In addition, LGD is found to be a significant contributor of CH4 and CO2 to the lake, and the nutrients derived from LGD play a crucial role in regulating the proliferation of algae in the lake. As algal density increases, gas emissions such as CH4 ebullition and CO2 diffusion are amplified, while processes such as CH4 oxidation and atmospheric CO2 exchange within lakes are attenuated. The contribution of LGD to CH4 and CO2 inventories in the lake also decreases as OM degradation becomes a more significant contributor. This study highlights LGD regulation on the C flux and underscores the importance of synergistical studies from both ecological and hydrogeological perspectives in comprehending lacustrine C flux dynamics.

Effects of combined ecological restoration measures on water quality and underwater light environment of Qingshan Lake, an urban eutrophic lake in China

Ecological restoration is vital in the management of eutrophic lakes. However, the effect of combined ecological restoration measures on the improvement of water quality and the light environment of urban eutrophic lakes requires further exploration. This study considered Qingshan Lake, a typical urban eutrophic lake in the middle reaches of the Yangtze River in China and explored the changes in the water quality and light environment before and after the implementation of combined ecological restoration measures from 2021 to 2022. The results showed that: (1) Ecological restoration significantly improved the transparency of Qingshan Lake (P < 0.05), with transparency increased from 33.1 cm to 96.0 cm. Total phosphorus, total nitrogen, chlorophyll a, permanganate index, suspended solids and turbidity decreased by 52.11 %, 65.47 %, 73.84 %, 92.11 %, 83.11 % and 77.52 %; respectively (P < 0.05). (2) Following ecological restoration, Qingshan Lake converted from severe eutrophication to mesotrophication, and the trophic level index decreased from 75.29 to 49.65 (P < 0.05). (3) Ecological restoration had a significant effect on improving underwater light environments. After ecological restoration, the light attenuation coefficient changed between 1.61–3.12 m−1, and the annual mean value decreased by 61.96 % (P < 0.05). The euphotic zone depth changed from 1.85 to 6.19 m, an increase of 167.68 %. (4) Ecological restoration reduced the attenuation of blue light, which reduced the red-blue ratio (R/B) of water. In general, turbidity had the greatest impact on light attenuation and R/B of Qingshan Lake, and ecological restoration combined with multiple measures had a positive effect on the management of eutrophic lakes.

A novel algorithm for estimating phytoplankton algal density in inland eutrophic lakes based on Sentinel-3 OLCI images

As one of the optically active components, phytoplankton are common photosynthetic organisms in oceans, nearshore, and inland water bodies. The variations in phytoplankton algal density play a crucial role in understanding primary productivity, carbon cycling, and early warning of algal blooms. In this study, three typical eutrophic lakes in China, Lake Taihu, Lake Chaohu, and Lake Dianchi, were taken as the research area. Algorithms for estimating algal density of cyanobacteria-dominated and non-cyanobacteria-dominated water types were developed based on Mie theory. The results demonstrated that the developedalgorithm had favorable estimation performance for inland eutrophic lakes, with a determination coefficient (R2) of 0.88, a mean absolute percentage error (MAPE) of 51%, an unbiased mean absolute percentage error (UMAPE) of 39%, and a root mean square error (RMSE) of 23.99 × 106 cells/L. Furthermore, comparison with other algorithms for estimating algal density showed that the developed algorithm had the lowest MAPE of 60% and UMAPE of 43%, with the RMSE of 23.42 × 106cells/L. Extensive evaluation based on satellite-ground synchronous data demonstrated the applicability of the developed algorithm to the Sentinel-3 OLCI sensor, enabling the determination of spatial and temporal distribution characteristics of algal density in the three lakes from 2016 to 2022 using Sentinel-3 OLCI images. The results of algal density inversion revealed a continuous decreasing trend in algal density in Lake Dianchi from 2016 to 2022, while the algal density in Lake Taihu and Lake Chaohu both decreased after 2019.

Biotic and Abiotic Regulations of Carbon Fixation into Lacustrine Sediments with Different Nutrient Levels.

Lake sediments play a critical role in organic carbon (OC) conservation. However, the biogeochemical processes of the C cycle in lake ecosystems remain limitedly understood. In this study, Fe fractions and OC fractions, including total OC (TOC) and OC associated with iron oxides (TOCFeO), were measured for sediments from a eutrophic lake in China. The abundance and composition of bacterial communities encoding genes cbbL and cbbM were obtained by using high-throughput sequencing. We found that autochthonous algae with a low C/N ratio together with δ13C values predominantly contributed to the OC burial in sediments rather than terrigenous input. TOCFeO served as an important C sink deposited in the sediments. A significantly positive correlation (r = 0.92, p < 0.001) suggested the remarkable regulation of complexed FeO (Fep) on fixed TOC fractions, and the Fe redox shift triggered the loss of deposited OC. It should be noted that a significant correlation was not found between the absolute abundance of C-associating genera and TOC, as well as TOCFeO, and overlying water. Some rare genera, including Acidovora and Thiobacillus, served as keystone species and had a higher connected degree than the genera with high absolute abundance. These investigations synthetically concluded that the absolute abundance of functional genes did not dominate CO2 fixation into the sediments via photosynthesis catalyzed by the C-associating RuBisCO enzyme. That is, rare genera, together with high-abundance genera, control the C association and fixation in the sediments.

Purification Efficiency of Two Ecotypes of Wetland Plants on Subtropical Eutrophic Lakes in China

Purification Efficiency of Two Ecotypes of Wetland Plants on Subtropical Eutrophic Lakes in China

Retrieval of Chla Concentrations in Lake Xingkai Using OLCI Images

Lake Xingkai is a large turbid lake composed of two parts, Small Lake Xingkai and Big Lake Xingkai, on the border between Russia and China, where it represents a vital source of water, fishing, water transport, recreation, and tourism. Chlorophyll-a (Chla) is a prominent phytoplankton pigment and a proxy for phytoplankton biomass, reflecting the trophic status of waters. Regularly monitoring Chla concentrations is vital for issuing timely warnings of this lake’s eutrophication. Owing to its higher spatial and temporal coverages, remote sensing can provide a synoptic complement to traditional measurement methods by targeting the optical Chla absorption signals, especially for the lakes that lack regular in situ sampling cruises, like Lake Xingkai. This study calibrated and validated several commonly used remote sensing Chla retrieval algorithms (including the two-band ratio, three-band method, four-band method, and baseline methods) by applying them to Sentinel-3 Ocean and Land Colour Instrument (OLCI) images in Lake Xingkai. Among these algorithms, the four-band model (FBA), which removes the absorption signal of detritus and colored dissolved organic matter, was the best-performing model with an R2 of 0.64 and a mean absolute percentage difference of 38.26%. With the FBA model applied to OLCI images, the monthly and spatial distributions of Chla in Lake Xingkai were studied from 2016 to 2022. The results showed that over the seven years, the Chla concentrations in Small Lake Xingkai were higher than in Big Lake Xingkai. Unlike other eutrophic lakes in China (e.g., Lake Taihu and Lake Chaohu), Lake Xingkai did not display a stable seasonal Chla variation pattern. We also found uncertainties and limitations of the Chla algorithm models when using a larger satellite zenith angle or applying it to an algal bloom area. Recent increases in anthropogenic nutrient loading, water clarity, and warming temperatures may lead to rising phytoplankton biomass in Lake Xingkai, and the results of this study can be applied for the satellite-based monitoring of its water quality.

Bloom-induced internal release controlling phosphorus dynamics in large shallow eutrophic Lake Taihu, China

High phosphorus (P) concentrations are commonly observed in lakes during algal blooms despite massive efforts on external nutrient reduction. However, the knowledge about the relative contribution of internal P loading linked with algal blooms on lake phosphorus (P) dynamics remains limited. To quantify the effect of internal loading on P dynamics, we conducted extensive spatial and multi-frequency nutrient monitoring from 2016 to 2021 in Lake Taihu, a large shallow eutrophic lake in China, and its tributaries (2017–2021). The in-lake P stores (ILSP) and external loading were estimated and then internal P loading was quantified from the mass balance equation. The results showed that the in-lake total P stores (ILSTP) ranged from 398.5 to 1530.2 tons (t), and exhibited a dramatic intra- and inter-annual variability. The annual internal TP loading released from sediment ranged from 1054.3 to 1508.4 t, which was equivalent to 115.6% (TP loading) of the external inputs on average, and responsible for the fluctuations of ILSTP on a weekly scale. High-frequency observations exemplified that ILSTP increased by 136.4% during algal blooms in 2017, while by only 47.2% as a result of external loading after heavy precipitation in 2020. Our study demonstrated that both bloom-induced internal loading and storm-induced external loading are likely to run counter significantly to watershed nutrient reduction efforts in large shallow lakes. More importantly, bloom-induced internal loading is higher than storm-induced external loading over the short term. Given the positive feedback loop between internal P loadings and algal bloom in eutrophic lakes, which explains the significant fluctuation of P concentration while nitrogen concentration decreased. It is emphasized that internal loading and ecosystem restoration are unignorable in shallow lakes, particularly in the algal-dominated region.

Hydrochemical characteristics of groundwater in a plain river network region: Establishing linkages between source and water quality variables

The chemical characteristics of groundwater can indicate water quality condition and provide useful information for pollution source identification. This study aimed to understand the effects of dissolved organic matter (DOM) on ionic composition of groundwater and identify the main ions and sources of pollution. The Lake Taihu is a typical eutrophic lake in China. In this study, the hydrochemical composition of groundwater in the surrounding aquifer of Lake Taihu Basin was analyzed. The results showed that the values of water quality index (WQI) range from 13.29 to 56.26 (good water quality). The dominant hydrochemical type of groundwater was Ca–Mg–HCO3 type, and the rock dominance was the major mechanism controlling the groundwater chemistry. With an increasing concentration in dissolved organic carbon (DOC), the Na+, Mg2+, and HCO3– concentrations all showed a sharp increase followed by a slow increase, while the NO3– concentration showed an opposite trend, indicating the DOM can affect the ions composition. In addition, K+ was positively correlated with NO3–, As, and Cd. Hence, DOM input may directly or indirectly change the hydrochemistry of groundwater. Besides, the NO3– concentration in groundwater was much higher than that in Lake Taihu, indicating that the NO3– in groundwater mainly came from surface soil leaching. The anthropogenic sources are probably the main sources of different ions, including K+, NO3–, As, and Cd. This study can help to better understand the effects of lake eutrophication on groundwater and its pathways.

Arrhenius equation construction and nitrate source identification of denitrification at the Lake Taihu sediment − water interface with 15 N isotope

Total nitrogen in Taihu Lake, China has gradually decreased since 2015 while the total phosphorus concentration has exhibited an increasing trend, indicating an asynchronous change. The dominant nitrogen removal process in freshwater ecosystems is denitrification which primarily occurs at the sediment-water interface. In this study, 15N isotope incubation experiments were attempted to analyze the effect of water temperature on denitrification, to construct the regional denitrification Arrhenius equations considering water temperature, and to identify the nitrate source of denitrification in Lake Taihu sediments. The results indicated that the potential N2 production rates and denitrification rates generally decreased in the west to east direction, which was significantly positively correlated with the nitrate concentration of overlying water by Pearson correlation coefficient analysis (P < 0.05). In addition, when the water temperature was lower than 30°C, the rates of the potential N2 production and denitrification were higher with an increase in water temperature, but when the water temperature was overhigh, denitrification was inhibited. The ratio of the total denitrification rate of nitrate from the water column in the sediment to the total denitrification rate during the incubation experiment was above 0.5 at each sampling site. This indicated that the denitrification in the Lake Taihu sediment primarily occurred at the expense of nitrate from the water column. The research results of Arrhenius equation construction and nitrate source identification of denitization can be applied to improve the accuracy of water quality model of Taihu Lake, which is of great significance to improve Taihu Lake water quality, and can act as a reference for the water environment treatment of other shallow eutrophic lakes in China and abroad.

Algal biomass mapping of eutrophic lakes using a machine learning approach with MODIS images

Algal blooms are a widespread issue in eutrophic lakes. Compared with the satellite-derived surface algal bloom area and chlorophyll-a (Chla) concentration, algae biomass is a more stable way to reflect water quality. Although satellite data have been adopted to observe the water column integrated algal biomass, the previous methods mostly are empirical algorithms, which are not stable enough for widespread use. This paper proposed a machine learning algorithm based on Moderate Resolution Imaging Spectrometer (MODIS) data to estimate the algal biomass, which was successfully applied to a eutrophic lake in China, Lake Taihu. This algorithm was developed by linking Rayleigh-corrected reflectance to in situ algae biomass data in Lake Taihu (n = 140), and the different mainstream machine learning (ML) methods were compared and validated. The partial least squares regression (PLSR) (R2 = 0.67, mean absolute percentage error (MAPE) = 38.88 %) and support vector machines (SVM) (R2 = 0.46, MAPE = 52.02 %) performed poor satisfactory. In contrast, random forest (RF) and extremely gradient boosting tree (XGBoost) algorithms had higher accuracy (RF: R2 = 0.85, MAPE = 22.68 %; XGBoost: R2 = 0.83, MAPE = 24.06 %), demonstrating greater application potential in algal biomass estimation. Field biomass data were further used to estimate the RF algorithm, which showed acceptable precision (R2 = 0.86, MAPE < 7 mg Chla). Subsequently, sensitivity analysis showed that the RF algorithm was not sensitive to high suspension and thickness of aerosols (rate of change <2 %), and inter-day and consecutive days verification showed stability (rate of change <5 %). The algorithm was also extended to Lake Chaohu (R2 = 0.93, MAPE = 18.42 %), demonstrating its potential in other eutrophic lakes. This study for algae biomass estimation provides technical means with higher accuracy and greater universality for the management of eutrophic lakes.

Identification of major environmental factors driving phytoplankton community succession before and after the regime shift of Erhai Lake, China

In lake ecosystems, regime shifts can induce changes in physical and biological components, particularly phytoplankton community structure, as well as across multiple trophic levels. Phytoplankton community succession may be driven by different environmental factors before and after regime shift; however, owing to the abruptness of the regime shift, long-term observations encompassing both periods are scarce, limiting our ability to identify the shift of key drivers of phytoplankton community succession. Here, an analysis was conducted based on long-term observations (involving phytoplankton community and environmental variables from 1997 to 2008) in Erhai Lake, a eutrophic lake in China that may have undergone a regime shift in 2001–2003. The dynamics of hydrochemical parameters and phytoplankton community composition indicated a distinct regime shift between 2001 and 2003. The phytoplankton community evolved over a decadal period toward community structure simplification, dominance of individual species, and homogenization of species composition. Prior to the regime shift (1997–2000), the eutrophic status of Erhai Lake was oligotrophic, and the phytoplankton community was characterized by high biodiversity and low biomass, with Cyanophyta, Bacillariophyta, and Cryptophyta being the dominant species. Nutrient concentrations had a significant limiting effect on phytoplankton, with total nitrogen concentration being the primary limiting factor. Following the regime shift (2005–2008), the phytoplankton community exhibited low biodiversity and high biomass, with the dominant taxa shifting to Cyanophyta, Chlorophyta, and Bacillariophyta. Light became the primary driver of phytoplankton community succession. Our study was based on long-term observations covering regime shifts in Erhai Lake, providing strong evidence that the phytoplankton community is influenced by nutrients in oligotrophic status. However, as nutrient concentrations increase, especially once the threshold is exceeded, species interactions may shift from competition for nutrients to competition for light. This implies that the resource competition theory, which integrates nutrient- and light-based approaches, provides an effective approach to predicting phytoplankton community succession, and that measures to improve underwater light environments should be considered as an additional option for nutrient load reduction in eutrophic lakes.

Real-Time Identification of Cyanobacteria Blooms in Lakeshore Zone Using Camera and Semantic Segmentation: A Case Study of Lake Chaohu (Eastern China)

The surface water in the lakeshore zone is the primary area where cyanobacteria bloom floats intensively. In lake water environment monitoring, it has become pressing to accurately identify the distribution and accumulation coverage area of cyanobacteria blooms in the surface water of the lakeshore zone. This study proposes a real-time and dynamic monitoring technology for cyanobacteria blooms in surface water using a shore-based camera monitoring network. The specific work is as follows: Chaohu Lake, a large eutrophic lake in China, is selected as the research object. The multithreading technology is used to dynamically obtain the hourly video images of 43 cameras around Chaohu Lake. The semantic segmentation method is used to identify the cyanobacteria blooms in the video images, calculate the coverage of cyanobacteria blooms, and draw the spatial distribution map of cyanobacteria blooms in the lakeshore zone of Chaohu Lake. To improve the accuracy of cyanobacteria blooms recognition, we use the ResNet-50 network to integrate three semantic segmentation models, namely FCN, U-net, and DeeplabV3+. By comparing the cyanobacteria blooms results identified by the three methods, it is found that the boundary of the cyanobacteria blooms results identified by DeeplabV3+(ResNet-50) is clear, which is more consistent with the real spatial information of the distribution of cyanobacteria blooms and is more suitable for monitoring the hourly dynamic changes of cyanobacteria blooms in the Chaohu Lake lakeshore zone. The results demonstrated that the time requirement of monitoring cyanobacteria blooms in real time on an hourly basis could be met by utilizing technology that uses multiple threads. The OA (Overall Accuracy), MPA (Mean Pixel Accuracy), IOU (Intersection Over Union) of cyanobacteria blooms, and the IOU of water values of the DeeplabV3+(ResNet-50) were the highest, which were 0.83, 0.82, 0.71, and 0.74, and the RMSE between the predicted and real cyanobacterial blooms coverage of 43 cameras was 6.65%. The above values show that DeeplabV3+(ResNet-50) is this technology’s most suitable semantic segmentation model. This technique can provide technical support for the scientific development of a cyanobacteria blooms management plan in the lakeshore zone of Chaohu Lake by calculating the coverage area of cyanobacteria blooms and drawing the spatial distribution map of cyanobacteria blooms in the lakeshore zone.

Three-dimensional observations of particulate organic carbon in shallow eutrophic lakes from space

Particulate organic carbon (POC) storage play an essential role in determining carbon sinks and water quality in shallow eutrophic lakes. Although satellite data have been adopted to observe surface POC content, the previous remote sensing results cannot indicate water column-integrated POC storage in shallow eutrophic lakes with vertically nonuniform POC profiles. Based on 272 in-situ POC profiles collected from 19 shallow eutrophic lakes in China's Yangtze-Huai Plain (YHP), this study developed a novel process-oriented method to derive water column-integrated POC storage from OLCI/Sentinel-3A satellite data. The method included three critical steps: remotely estimating surface POC contents, identifying POC profile types (uniform, exponential decay, or power decay) through a binary decision tree, and parameterizing POC profiles using surface POC concentrations. The developed algorithms showed a bias of -25.79% for in-situ match-ups from 15 lakes (N = 88). The POC in the eutrophic lakes was mainly produced by phytoplankton (R2 = 0.87). As a result, the surface POC content presented a unimodal peak in summer. However, in addition to air temperature regulations (0.52 – 68.53%), the water level also showed apparent effects on increasing POC storage, with relative contributions of 29.29 – 95.51% for the six largest lakes. Therefore, multisource satellite data are needed to derive POC storage in global eutrophic lakes remotely. This study is the first attempt to three-dimensionally observe lake POC contents from space and is important for understanding the carbon cycle in shallow eutrophic lakes.

Multivariable integrated risk assessment for cyanobacterial blooms in eutrophic lakes and its spatiotemporal characteristics

Climate change has catalyzed the global expansion of cyanobacterial blooms in eutrophic , lakes and threatens water security. In most studies, the cyanobacterial bloom risk levels in lakes were evaluated using field-collected data from multiple indicators or spatially continuous data from one cyanobacteria-related indicator. Nevertheless, the occurrence of cyanobacterial blooms in lakes has clear spatial heterogeneity and is affected by numerous factors. Therefore, we developed a multivariable integrated risk assessment framework for cyanobacterial blooms in lakes using five spatially continuous datasets to estimate the risk level of cyanobacterial blooms at the pixel scale (250 m). The spatial and temporal variations in cyanobacterial bloom risk levels from May 1, 2002, to October 31, 2020, were investigated for three typical eutrophic lakes in China: Lakes Taihu, Chaohu, and Dianchi. Seasons and regions of high cyanobacterial bloom risk were identified for each lake. Environmental characteristics were discussed. A long-term investigation revealed that owing to its warm climate, the cyanobacterial risk levels in summer and autumn were much higher than those in the other two seasons. At the synoptic scale, Lake Taihu had a lower cyanobacterial bloom risk than Lakes Chaohu and Dianchi. A further comparison found that precipitation, wind speed, and temperature were responsible for the differences in cyanobacterial bloom risk levels among the three lakes. At the pixel scale, the risk map indicated that the cyanobacterial bloom risk levels of Lake Taihu were unevenly distributed, and the cyanobacterial bloom risk of the lakeshore was higher than that of the other subregions. Nutrient levels played the most critical role in the regional differences in cyanobacterial bloom risk levels in a lake. While the differences of cyanobacterial bloom risk levels in three lakes were resulted by the climates. Bloom events were defined and classified as “long-term bloom” or “flash bloom” according to their duration (over or below a year). Overall, this study can assist in advanced water management with a pixel-scale evaluation of cyanobacterial bloom risk levels.

A process-guided hybrid Bayesian belief network to bridge watershed modeling and BMP planning

Planning of Best Management Practices (BMPs) is increasingly dependent on Process-based Watershed Models (PWMs) and suffers from large uncertainties. The large complexity and long runtimes of such models make prudential watershed management a difficult undertaking. In this study, we aimed to bridge the gaps between PWMs and uncertainty-based BMP planning with Bayesian Belief Network (BBNs). A new modeling framework of Process-guided Hybrid BBNs (PH-BBNs) was developed to represent the probabilistic cascade of critical modules in Soil & Water Assessment Tool (SWAT), a widely-used PWM, across external stressors (e.g., weather and various BMPs), parametric uncertainties, and watershed predictors. The PH-BBN modeling framework was used for decision support of Nonpoint Source (NPS) pollution mitigation in an intensively-cultivated area adjacent to Lake Dianchi, one of the three most eutrophic lakes in China. Our findings suggest that PH-BBNs can capture the critical pathways of water, sediment, and Total Phosphorus (TP) loss. Watershed projections are subject to large uncertainties to varying degrees in different landscapes. According to variance-based sensitivity analysis, precipitation accounts for >80% of the projection variability, which underlines watershed vulnerability to climate change. As the effectiveness of parallel terraces, filter strips, and fertilization management would degrade with increasing rainfall intensity, they should be conservatively designed for BMP sustainability. Implementation of parallel terraces and filter strips is recommended as they are projected to reduce 80% of TP loads with >90% compliance confidence. PH-BBNs can render effective decision support for BMP risk assessment and adaptive watershed management under climate change.

Contrasting diversity patterns and community assembly mechanisms of bacterioplankton among different aquatic habitats in Lake Taihu, a large eutrophic shallow lake in China

Eutrophication leads to the degradation of lake habitat types from macrophyte-dominated habitats (MDH) to algae-dominated habitats (ADH), which is a common environmental problem faced by many lakes. However, the variations in diversities and community assembly processes of bacterioplankton in the process of lake eutrophication have not been thoroughly elucidated. Here, we contrasted bacterial diversity patterns and processes of community assembly among ADH, MDH, and other habitats (OH) of Lake Taihu, a large shallow eutrophic lake in China with strong wind-induced disturbances. We found that the bacterial diversity patterns and potential functions between ADH and MDH were significantly different. Moreover, the contributions of purely environmental variables to the bacterial diversity patterns of all habitat types were much higher than those of spatial variables. However, the relative importance of stochasticity in the bacterial community assembly of each habitat type was much higher than that of determinism. Intriguingly, ‘undominated’ stochastic processes shape the diversity patterns of bacterioplankton in ADH, MDH, and OH of Lake Taihu. These findings demonstrate that the degradation of lake habitats caused by eutrophication can profoundly change the diversity and potential function patterns of the bacterioplankton community in lake ecosystems. Although the distinct diversity patterns of the bacterioplankton among the different aquatic habitats in Lake Taihu can be affected by deterministic processes (local environmental variables), they were dominated by stochastic processes (drift). Our study confirms that strong, disordered, wind-induced disturbances in shallow lakes could lead to strong hydrologic mixing, thus increasing the randomness of bacterial community assembly in each habitat.

Response of planktonic diversity and stability to environmental drivers in a shallow eutrophic lake

Plankton is an important component of a healthy shallow lake aquatic system, and the diversity and stability of its community structure are impacted by numerous environmental factors. However, knowledge about the relative importance of these factors is limited. This study presents a case study conducted in semi-arid, shallow eutrophic lakes in China to evaluate the relative importance of physical, chemical, and nutritional characteristics of water quality and light effects on the spatiotemporal distributions of diversity and stability of plankton communities. The findings showed that the plankton community was the least diverse in winter and the most stable during the spring-to-summer transition. The results of partition variation showed that physical and chemical indicators, nutritional parameters, and light effects explained >70 % of the zooplankton community dynamics, compared with 37 % for phytoplankton. For phytoplankton community composition, physical and chemical parameters, followed by light, had the greatest influence. Further analysis showed that phytoplankton were influenced primarily by nutrients, water depth, and pH. Similarly, the diversity and stability of the zooplankton exhibited physical and chemical parameter responses, and the influence of nutrients also played a significant role. Structural equation modelling showed that light had minimal direct effect on the plankton and affected the plankton indirectly through changes in total nitrogen and NO3-N. This study estimates the distribution of plankton community in a shallow eutrophic lake, quantifying the relative effects of different environmental drivers on plankton diversity and stability, and provides an in-depth understanding for improving the lake water quality and controlling nutrients pollution.

Inhibitory effects of Bellamya aeruginosa on common algae in freshwater blooms

The increasingly frequent algal blooms in freshwater have become a major environmental problem in the world. In recent years, algae removal by the biological method is receiving more attention for its eco-friendly characteristics. In this study, we examined the effects of Bellamya aeruginosa, a common macrobenthic snail in eutrophic lakes in China, on the growth and photosynthesis activities of the common algae occurred in freshwater blooms, including cyanobacterium Microcystis aeruginosa, coupled with green algae Chlorella vulgaris and Scenedesmus obliquus. The main aims were to clarify the interactive relationships between B. aeruginosa and algae, and to verify feasibility of using B. aeruginosa as an algal-removing organism. The results showed that B. aeruginosa could feed a large amount of algae cells in a short period after inoculation, and reached the maximal removal rate of toxic and non-toxic M. aeruginosa as well as S. obliquus within 12 hours, which were 73.7%, 73.2%, and 51.1%, respectively. Furthermore, its feeding on C. vulgaris was stronger than on other algae, with the removal rate reaching 99.2% by the end of the experiment. The microcystins produced by the toxic M. aeruginosa accumulated in the body of B. aeruginosa induced the histopathological changes in the liver tissue, and thereby hindered the feeding of B. aeruginosa. In the late stage of the experiment, the photosynthetic activities of the algal cells under each treatment were significantly lower than that in the control, indicating that the feeding of B. aeruginosa damaged algal cells and inhibited their proliferation. In addition, in the mixture of non-toxic M. aeruginosa and S. obliquus, the selective feeding of B. aeruginosa caused the dominance of non-toxic M. aeruginosa to be replaced by S. obliquus. Therefore, B. aeruginosa could inhibit the photosynthesis and reduce the biomass of algae through feeding, and thus would eliminate or mitigate the formation of algal blooms.