Determining the probability of cyanobacterial blooms: the application of Bayesian networks in multiple lake systems

Historical and projected response of Southeast Asian lakes surface water temperature to warming climate

The temperature of surface and epilimnetic waters, closely related to regional air temperatures, responds quickly and directly to climatic changes. As a result, lake surface temperature (LSWT) can be considered an effective indicator of climate change. In this study, we reconstructed and investigated historical and future LSWT across different scenarios for over 80 major lakes in mainland Southeast Asia (SEA), an ecologically diverse region vulnerable to climate impacts. Five different predicting models, incorporating statistical, machine and deep learning approaches, were trained and validated using ERA5 and CHIRPS climatic feature datasets as predictors and 8-day MODIS-derived LSWT from 2000 to 2020 as reference dataset. Best performing model was then applied to predict both historical (1986-2020) and future (2020-2100) LSWT for SEA lakes, utilizing downscaled climatic CORDEX-SEA feature data and multiple Representative Concentration Pathway (RCP). The analysis uncovered historical and future thermal dynamics and long-term trends for both daytime and nighttime LSWT. Among 5 models, XGboost results the most performant (NSE 0.85, RMSE 1.14 °C, MAE 0.69 °C, MBE -0.08 °C) and it has been used for historical reconstruction and future LSWT prediction. The historical analysis revealed a warming trend in SEA lakes, with daytime LSWT increasing at a rate of +0.18 °C/decade and nighttime LSWT at +0.13 °C/decade over the past three decades. These trends appeared of smaller magnitude compared to global estimates of LSWT change rates and less pronounced than concurrent air temperature and LSWT increases in neighbouring regions. Projections under various RCP scenarios indicated continued LSWT warming. Daytime LSWT is projected to increase at varying rates per decade: +0.03 °C under RCP2.6, +0.14 °C under RCP4.5, and +0.29 °C under RCP8.5. Similarly, nighttime LSWT projections under these scenarios are: +0.03 °C, +0.10 °C, and +0.16 °C per decade, respectively. The most optimistic scenario predicted marginal increases of +0.38 °C on average, while the most pessimistic scenario indicated an average LSWT increase of +2.29 °C by end of the century. This study highlights the relevance of LSWT as a climate change indicator in major SEA's freshwater ecosystems. The integration of satellite-derived LSWT, historical and projected climate data into data-driven modelling has enabled new and a more nuanced understanding of LSWT dynamics in relation to climate throughout the entire SEA region.

Artificial intelligence to explain the variables that favor the cyanobacteria steady-state in tropical ecosystems: A Bayeasian network approach

The steady-state is a situation of little variability of species dominance and total biomass over time. Maintenance of cyanobacteria are often observed in tropical and eutrophic ecosystems and can cause imbalances in aquatic ecosystem. Bayeasian networks allow the construction of simpls models that summarizes a large amount of variables and can predict the probability of occurrence of a given event. Studies considering Bayeasian networks built from environmental data to predict the occurrence of steady-state in aquatic ecosystems are scarce. This study aims to propose a Bayeasian network model to assess the occurrence, composition and duration of cyanobacteria steady-state in a tropical and eutrophic ecosystem. It was hypothesized long lasting steady-state events, composed by filamentous cyanobacteria species and directly influenced by eutrophication and drought. Our model showed steady-state lasting between 3 and 17 weeks with the monodominance or co-dominance of filamentous species, mainly Raphidiopsis raciborskii and Planktothrix agardhii. These evens occurred frequently under drought and high turbidity, however higher nutrients concentrations did not increase the probability steady-state occurrence or longer duration. The proposed model appears as a tool to assess the effects of future warming on steady-state occurrence and it can be a useful to more traditional process-based models for reservoirs.

A modelling approach to evaluate the present and future effectiveness of hypolimnetic withdrawal for the restoration of eutrophic Lake Varese (Northern Italy)

Hypolimnetic withdrawal has been applied as a restoration measure in lakes subject to eutrophication together with external load reduction, to decrease internal load by removing limiting nutrient phosphorus (P) from anoxic deep waters and contributing to the unloading of bottom sediments from previously deposited nutrients and organic matter. The aim of this study is to evaluate the effect of hypolimnetic withdrawal on Lake Varese, a 24 m-deep and 14.8 km2-large subalpine lake in North-Western Italy. The lake suffered from extended eutrophication in the second half of the 20th century due to uncontrolled delivery of untreated urban sewage. Several restoration measures have been implemented during the years, including hypolimnetic withdrawal. In 2019, a cooperative programme for the protection and management of the lake and its surroundings was launched, establishing a systematic annual hypolimnetic withdrawal in the stratified season since 2020. In this research, we calibrated a one-dimensional (1D) coupled ecological-hydrodynamic model (General Lake Model/Aquatic EcoDynamics - GLM/AED2) of Lake Varese with data surveyed in the lake in 2019-2021. Model simulations of the period 2020-2021 with and without the performed withdrawal proved the effectiveness of this measure on hypolimnetic P concentration reduction. Then, future simulations of 2023-2085 were carried out to predict the future efficiency of hypolimnetic withdrawal and of reductions in external nutrient loads under climate change scenarios. Results show that the prescribed withdrawal increases hypolimnetic temperatures. This effect, coupled with thermocline deepening due to global warming, will possibly lead to decreasing water mass stability in autumn and shorter stratification in the moderately deep Lake Varese, with an eventual decrease of P concentrations in the water column. The future effectiveness of hypolimnetic withdrawal is further discussed considering the possible role of dry periods.

Two machine learning approaches for predicting cyanobacteria abundance in aquaculture ponds

Cyanobacteria blooms in aquaculture ponds harm the harvesting of aquatic animals and threaten human health. Therefore, it is crucial to identify key drivers and develop methods to predict cyanobacteria blooms in aquaculture water management. In this study, we analyzed monitoring data from 331 aquaculture ponds in central China and developed two machine learning models - the least absolute shrinkage and selection operator (LASSO) regression model and the random forest (RF) model - to predict cyanobacterial abundance by identifying the key drivers. Simulation results demonstrated that both machine learning models are feasible for predicting cyanobacterial abundance in aquaculture ponds. The LASSO model (R² = 0.918, MSE = 0.354) outperformed the RF model (R² = 0.798, MSE = 0.875) in predicting cyanobacteria abundance. Farmers with well-equipped aquaculture ponds that have abundant water monitoring data can use the nine environmental variables identified by the LASSO model as an operational solution to accurately predict cyanobacteria abundance. For crude ponds with limited monitoring data, the three environmental variables identified by the RF model provide a convenient solution for useful cyanobacteria prediction. Our findings revealed that chemical oxygen demand (COD) and total organic carbon (TOC) were the two most important predictors in both models, indicating that organic carbon concentration had a close relationship with cyanobacteria growth and should be considered a key metric in water monitoring and pond management of these aquaculture ponds. We suggest that monitoring of organic carbon coupled with phosphorus reduction in feed usage can be an effective management approach for cyanobacteria prevention and to maintain a healthy ecological state in aquaculture ponds.

Nutrient reduction mitigated the expansion of cyanobacterial blooms caused by climate change in Lake Taihu according to Bayesian network models

Although nutrient reduction has been used for lake eutrophication mitigation worldwide, the use of this practice alone has been shown to be less effective in combatting cyanobacterial blooms, primarily because of climate change. In addition, quantifying the climate change contribution to cyanobacterial blooms is difficult, further complicating efforts to set nutrient reduction goals for mitigating blooms in freshwater lakes. This study employed a continuous variable Bayesian modeling framework to develop a model to predict spring cyanobacterial bloom areas and frequencies (the responses) using nutrient levels and climatic factors as predictors. Our results suggested that both spring climatic factors (e.g., increasing temperature and decreasing wind speed) and nutrients (e.g., total phosphorus) played vital roles in spring blooms in Lake Taihu, with climatic factors being the primary drivers for both bloom areas and frequencies. Climate change in spring had a 90% probability of increasing the bloom area from 35 km² to 180 km² during our study period, while nutrient reduction limited the bloom area to 170 km², which helped mitigate expansion of cyanobacterial blooms. For lake management, to ensure a 90% probability of the mean spring bloom areas remaining under 154 km² (the 75th percentile of the bloom areas in spring), the total phosphorus should be maintained below 0.073 mg·L^-1 under current climatic conditions, which is a 46.3% reduction from the current level. Our modeling approach is an effective method for deriving dynamic nutrient thresholds for lake management under different climatic scenarios and management goals.

Use of a Bayesian network as a decision support tool for watershed management: a case study in a highly managed river-dominated estuary

Decision making in water resource management has many dimensions including water supply, flood protection, and meeting ecological needs, therefore, is complex, full of uncertainties, and often contentious due to competing needs and distrust among stakeholders. It benefits from robust tools for supporting the decision-making process and for communicating with stakeholders. This paper presents a Bayesian network (BN) modeling framework for analyzing various management interventions regulating freshwater discharges to an estuary. This BN was constructed using empirical data from 98 months of monitoring the Caloosahatchee River Estuary in south Florida during the period 2008-2021 as a case study to illustrate the potential advantages of the BN approach. Results from three different management scenarios and their implications on down-estuary conditions as they affected eastern oysters (Crassostrea virginica) and seagrass (Halodule wrightii) are presented and discussed. Finally, the directions for future applications of the BN modeling framework to support management in similar systems are offered.

A probabilistic decision support tool for prediction and management of rainfall-related poor water quality events for a drinking water treatment plant

A data-driven Bayesian Network (BN) model was developed for a large Australian drinking water treatment plant, whose raw water comes from a river into which a number of upstream dams outflow water and smaller tributaries flow. During wet weather events, the spatial distribution of rainfall has a crucial role on the incoming raw water quality, as runoff from specific sub-catchments usually causes significant turbidity and conductivity issues, as opposed to larger dam outflows which have typically better water quality. The BN relies on a conceptual model developed following expert consultation, as well as a combination of different types (e.g. water quality, flow, rainfall) and amount (e.g. high-frequency, daily, scarce depending on variable) of historical data. The validated model proved to have acceptable accuracy in predicting the probability of different incoming raw water quality ranges, and can be used to assess different scenarios (e.g. timing, flow) of dam water releases, for the purpose of achieving dilution of the tributary's poor-quality water and mitigate related drinking water treatment challenges.

Identifying lakes at risk of toxic cyanobacterial blooms using satellite imagery and field surveys across the United States

Harmful algal blooms caused by cyanobacteria are a threat to global water resources and human health. Satellite remote sensing has vastly expanded spatial and temporal data on lake cyanobacteria, yet there is still acute need for tools that identify which waterbodies are at-risk for toxic cyanobacterial blooms. Algal toxins cannot be directly detected through imagery but monitoring toxins associated with cyanobacterial blooms is critical for assessing risk to the environment, animals, and people. The objective of this study is to address this need by developing an approach relating satellite imagery on cyanobacteria with field surveys to model the risk of toxic blooms among lakes. The Medium Resolution Imaging Spectrometer (MERIS) and United States (US) National Lakes Assessments are leveraged to model the probability among lakes of exceeding lower and higher demonstration thresholds for microcystin toxin, cyanobacteria, and chlorophyll a. By leveraging the large spatial variation among lakes using two national-scale data sources, rather than focusing on temporal variability, this approach avoids many of the previous challenges in relating satellite imagery to cyanotoxins. For every satellite-derived lake-level Cyanobacteria Index (CI_cyano) increase of 0.01 CI_cyano/km2, the odds of exceeding six bloom thresholds increased by 23-54 %. When the models were applied to the 2192 satellite monitored lakes in the US, the number of lakes identified with ≥75 % probability of exceeding the thresholds included as many as 335 lakes for the lower thresholds and 70 lakes for the higher thresholds, respectively. For microcystin, the models identified 162 and 70 lakes with ≥75 % probability of exceeding the lower (0.2 μg/L) and higher (1.0 μg/L) thresholds, respectively. This approach represents a critical advancement in using satellite imagery and field data to identify lakes at risk for developing toxic cyanobacteria blooms. Such models can help translate satellite data to aid water quality monitoring and management.

Response of aquatic plant decomposition to invasive algal organic matter mediated by the co-metabolism effect in eutrophic lakes

The decomposition of aquatic plant residues changes by the invasive algal organic matter in eutrophic lakes, however, the driving mechanisms of these biogeochemistry processes are still far from clear. In this study, a series of microcosms was constructed to simulate the mixed decomposition processes of aquatic plant residues with invasive algae as long as 205 days. Three aquatic plants (Potamogeton malaianus, Nymphoides peltatum, and Phragmites australis) and algae were collected from a typical eutrophic lake. The addition of algae promoted the decomposition of three plant residues based on the mass loss, and the positive co-metabolism effect was produced. The co-metabolism intensity was 8%-25% on the water surface and 19%-45% on the water-sediment interface, respectively. In addition, the response of three aquatic plant residues to the algal organic matter was different with their co-metabolism intensities in the order of P. australis > P. malaianus > N. peltatum on both the water surface and water-sediment interface. The phylum number of bacteria attached to the surface of plant residues increased from 27 to 52. The abundance of Bacteroidetes, which had the function of decomposing refractory organic matter, increased most significantly at the final incubation. At present, shallow lakes are under the double pressure of eutrophication and global warming, and the intensity and duration of algal blooms are increasing. Therefore, the co-metabolism effect of the residue decomposition process described here may change the carbon cycle strength and increase the greenhouse gas emissions of lakes and need to be taken into account in future lake management.

Waterborne virus transport and the associated risks in a large lake

Waterborne enteric viruses in lakes, especially at recreational water sites, may have a negative impact on human health. However, their fate and transport in lakes are poorly understood. In this study, we propose a coupled water quality and quantitative microbial risk assessment (QMRA) model to study the transport, fate and infection risk of four common waterborne viruses (adenovirus, enterovirus, norovirus and rotavirus), using Lake Geneva as a study site. The measured virus load in raw sewage entering the lake was used as the source term in the water quality simulations for a hypothetical scenario of discharging raw wastewater at the lake surface. After discharge into the lake, virus inactivation was modeled as a function of water temperature and solar irradiance that varied both spatially and temporally during transport throughout the lake. Finally, the probability of infection, while swimming at a popular beach, was quantified and compared among the four viruses. Norovirus was found to be the most abundant virus that causes an infection probability that is at least 10 times greater than the other viruses studied. Furthermore, environmental inactivation was found to be an essential determinant in the infection risks posed by viruses to recreational water users. We determined that infection risks by enterovirus and rotavirus could be up to 1000 times lower when virus inactivation by environmental stressors was accounted for compared with the scenarios considering hydrodynamic transport only. Finally, the model highlighted the role of the wind field in conveying the contamination plume and hence in determining infection probability. Our simulations revealed that for beaches located west of the sewage discharge, the infection probability under eastward wind was 43% lower than that under westward wind conditions. This study highlights the potential of combining water quality simulation and virus-specific risk assessment for a safe water resources usage and management.

Performance and validation of water surface temperature estimates from Landsat 8 of the Itaipu Reservoir, State of Paraná, Brazil

Studies on water surface temperature (WST) from thermal infrared remote sensing are still incipient in Brazil, and for many water resources, they do not exist. Many algorithms have been developed to estimate surface temperature in satellite images. There are also many difficulties in implementing these algorithms due to their complexity, especially in free software, which restricts the satisfactory processing of these data by users of the technique. Thus, this work aimed to validate an algorithm used to estimate land surface temperature (LST) when applied to the surface of inland water bodies. Water surface temperature estimates (WSTe) were generated from Itaipu State of Paraná (PR) reservoir, Brazil, calculated from Landsat 8 - TIRS satellite images (WSTs) and water surface temperature data from 37 in situ stations (WSTi). A linear regression model of the WSTe was generated in 60% of the samples and its validation with the remaining 40%, subject to prior evaluation of some statistical indicators. The model was considered significant since the coefficient of determination (r²) was 0.90 (95% of confidence), root mean square deviation (RMSD) 0.8 °C, Willmott Index (d) = 0.97, and Nash-Sutcliffe efficiency coefficient (NSE) = 0.89. The methodology used to extract WSTs from the Python QGIS plugin was relatively quick to apply, easy to understand, and had a better performance of the estimates than those presented in the literature review.

Near-term phytoplankton forecasts reveal the effects of model time step and forecast horizon on predictability

As climate and land use increase the variability of many ecosystems, forecasts of ecological variables are needed to inform management and use of ecosystem services. In particular, forecasts of phytoplankton would be especially useful for drinking water management, as phytoplankton populations are exhibiting greater fluctuations due to human activities. While phytoplankton forecasts are increasing in number, many questions remain regarding the optimal model time step (the temporal frequency of the forecast model output), time horizon (the length of time into the future a prediction is made) for maximizing forecast performance, as well as what factors contribute to uncertainty in forecasts and their scalability among sites. To answer these questions, we developed near-term, iterative forecasts of phytoplankton 1-14 days into the future using forecast models with three different time steps (daily, weekly, fortnightly), that included a full uncertainty partitioning analysis at two drinking water reservoirs. We found that forecast accuracy varies with model time step and forecast horizon, and that forecast models can outperform null estimates under most conditions. Weekly and fortnightly forecasts consistently outperformed daily forecasts at 7-day and 14-day horizons, a trend that increased up to the 14-day forecast horizon. Importantly, our work suggests that forecast accuracy can be increased by matching the forecast model time step to the forecast horizon for which predictions are needed. We found that model process uncertainty was the primary source of uncertainty in our phytoplankton forecasts over the forecast period, but parameter uncertainty increased during phytoplankton blooms and when scaling the forecast model to a new site. Overall, our scalability analysis shows promising results that simple models can be transferred to produce forecasts at additional sites. Altogether, our study advances our understanding of how forecast model time step and forecast horizon influence the forecastability of phytoplankton dynamics in aquatic systems and adds to the growing body of work regarding the predictability of ecological systems broadly.

Can correlational analyses help determine the drivers of microcystin occurrence in freshwater ecosystems? A meta-analysis of microcystin and associated water quality parameters

Microcystin (MC) is a toxic secondary metabolite produced by select cyanobacteria that threatens aquatic and terrestrial organisms over a diverse range of freshwater systems. To assess the relationship between environmental parameters and MC, researchers frequently utilize correlational analyses. This statistical methodology has proved useful when summarizing complex water quality monitoring datasets, but the correlations between select parameters and MC have been documented to vary widely across studies and systems. Such variation within the peer-reviewed literature leaves uncertainty for resource managers when developing a MC monitoring program. The objective of this research is to determine if correlational analyses between environmental parameters and MC are helpful to resource managers desiring to understand the drivers of MC. Environmental (i.e., physical, chemical, and biological) and MC correlation data were retrieved from an estimated 2,643 waterbodies (largely from the north temperate region) and synthesized using a Fisher's z meta-analysis. Common water quality parameters, such as chlorophyll, temperature, and pH, were positively correlated with MC, while transparency was negatively correlated. Interestingly, 12 of the 15 studied nitrogen parameters, including total nitrogen, were not significantly correlated with MC. In contrast, three of the four studied phosphorus parameters, including total phosphorus, were positively related to MC. Results from this synthesis quantitatively reinforces the usefulness of commonly measured environmental parameters to monitor for conditions related to MC occurrence; however, correlational analyses by themselves are often ineffective and considering what role a parameter plays in the ecology of cyanobacterial blooms in addition to MC production is vital.

Sub-Saharan Africa Freshwater Fisheries under Climate Change: A Review of Impacts, Adaptation, and Mitigation Measures

Sub-Saharan Africa’s freshwater fisheries contribute significantly to the livelihoods and food security of millions of people within the region. However, freshwater fisheries are experiencing multiple anthropogenic stressors such as overfishing, illegal fishing, pollution, and climate change. There is a substantial body of literature on the effects of climate change on freshwater fisheries in Sub-Saharan Africa. This study reviews the existing literature and highlights the effects of climate change on freshwater fisheries, the adaptation strategies of fishery-dependent households in response to the effects, and fisheries’ management and mitigation efforts in the face of climate change. The general effects of climate change on freshwater environments include warming water temperatures, increased stratification, modified hydrological processes, and increased pollutants. These effects adversely affect the physiological processes of fish and the overall wellbeing of fishery-dependent people. To cope with the effects of fluctuating fishery resources due to climate change, fishery-dependent people have adopted several adaptation strategies including livelihood diversification, changing their fishing gear, increasing their fishing efforts, and targeting new species. Several management attempts have been made to enhance the sustainability of fishery resources, from local to regional levels. This study recommends the participation of the resource users in the formulation of policies aimed at promoting climate change adaptation and the resilience of freshwater fisheries for sustainable development.

A review of plant-based coagulants for turbidity and cyanobacteria blooms removal

In recent years, the proliferation of Harmful Cyanobacterial Blooms (CyanoHABs) has increased with water eutrophication and climate change, impairing human health and the environment in relation to water supply. In drinking water treatment plants (DWTPs), the bio-coagulation based on natural coagulants has been studied as an eco-friendly alternative technology to conventional coagulants for both turbidity and CyanoHABs removal. Plant-based coagulants have demonstrated their coagulation efficiency in turbidity removal, as reported in several papers but its ability in cyanobacterial removal is still limited. This paper mainly reviewed the application of plant-based coagulants in DWTPs, with focus on turbidity removal, including cyanobacterial cells. The future potential uses of these green coagulants to reduce noxious effects of cyanobacterial proliferation are presented. Green coagulants advantages and limitations in DWTPs are reviewed and discussed summarizing more than 10 years of knowledge.

Modeling the sensitivity of cyanobacteria blooms to plausible changes in precipitation and air temperature variability

Many recent studies have attributed the observed variability of cyanobacteria blooms to meteorological drivers and have projected blooms with worsening societal and ecological impacts under future climate scenarios. Nonetheless, few studies have jointly examined their sensitivity to projected changes in both precipitation and temperature variability. Using an Integrated Assessment Model (IAM) of Lake Champlain's eutrophic Missisquoi Bay, we demonstrate a factorial design approach for evaluating the sensitivity of concentrations of chlorophyll a (chl-a), a cyanobacteria surrogate, to global climate model-informed changes in the central tendency and variability of daily precipitation and air temperature. An Analysis of Variance (ANOVA) and multivariate contour plots highlight synergistic effects of these climatic changes on exceedances of the World Health Organization's moderate 50 μg/L concentration threshold for recreational contact. Although increased precipitation produces greater riverine total phosphorus loads, warmer and drier scenarios produce the most severe blooms due to the greater mobilization and cyanobacteria uptake of legacy phosphorus under these conditions. Increases in daily precipitation variability aggravate blooms most under warmer and wetter scenarios. Greater temperature variability raises exceedances under current air temperatures but reduces them under more severe warming when water temperatures exceed optimal values for cyanobacteria growth more often. Our experiments, controlled for wind-induced changes to lake water quality, signal the importance of larger summer runoff events for curtailing bloom growth through reductions of water temperature, sunlight penetration and stratification. Finally, the importance of sequences of wet and dry periods in generating cyanobacteria blooms motivates future research on bloom responses to changes in interannual climate persistence.

The benthic toxic dinoflagellate Ostreopsis cf. ovata in the NW Mediterranean Sea: Relationship between sea surface temperature and bloom phenology

Blooms of the toxic benthic dinoflagellate Ostreopsis cf. ovata can induce ecological and human health issues in certain temperate areas. In order to prevent these negative effects, long-term monitoring studies of O. cf. ovata blooms have been conducted in several impacted areas to have a comprehensive understanding of bloom dynamics and efficient tools for risk management. O. cf. ovata blooms were monitored every summer (from mid-June to the end of August) on five identified sites in Larvotto beach (Monaco, NW Mediterranean Sea), between 2007 and 2019. This time-series represents one of the largest time-series in the world describing blooms of this species. Bloom phenological features (timing, duration, maximum cell abundance and growth rate), were found to be highly variable throughout the studied period, and were analyzed as a function of different hydroclimatic parameters, including sea surface temperature (SST). The highest net growth rates were related to temperatures ranging between 21°C and 25°C, and did not coincide with maximal temperature records (27.5°C). Such results suggest that, although global warming possibly influences the expansion of O. cf. ovata from tropical to temperate waters, the definite impact of temperature on bloom dynamics might be more complex than a simple facilitation factor for algal growth, at least in NW Mediterranean waters. Furthermore, monthly SST anomalies calculated over this 13-year survey showed a strong positive correlation between spring SST positive anomalies and the bloom starting date, indicating that blooms occurred earlier in the season when spring SSTs were warmer than usual. Overall results provide tools to modelers and managers who are facing crucial challenges to predict the distribution and phenology of O. cf. ovata blooms in European coastal waters, moreover in a context of global warming.

Climate Change Impacts on Microbiota in Beach Sand and Water: Looking Ahead

Beach sand and water have both shown relevance for human health and their microbiology have been the subjects of study for decades. Recently, the World Health Organization recommended that recreational beach sands be added to the matrices monitored for enterococci and Fungi. Global climate change is affecting beach microbial contamination, via changes to conditions like water temperature, sea level, precipitation, and waves. In addition, the world is changing, and humans travel and relocate, often carrying endemic allochthonous microbiota. Coastal areas are amongst the most frequent relocation choices, especially in regions where desertification is taking place. A warmer future will likely require looking beyond the use of traditional water quality indicators to protect human health, in order to guarantee that waterways are safe to use for bathing and recreation. Finally, since sand is a complex matrix, an alternative set of microbial standards is necessary to guarantee that the health of beach users is protected from both sand and water contaminants. We need to plan for the future safer use of beaches by adapting regulations to a climate-changing world.

Maximum lake surface water temperatures changing characteristics under climate change

Lake surface water temperature (LSWT) plays an important role in the metabolism of aquatic organisms, and is also an important indicator in lake ecosystems which affects the ecology and biogeochemical processes of lakes. The current research mainly focuses on the long-term trends of LSWT and the impact of climate warming on LSWT. Researchers have not paid enough attention to the study of extreme changes in trend of LSWT. An ice-free lake in China called Dianchi Lake was selected as our research area. We carried out a quantitative analysis of and provided a discussion on the changes in the maximum lake surface water temperature (MLSWT) from 2001 to 2018 at two timescales (month and year) based on MODIS 11A2 composite product data and water temperature environment of cyanobacteria outbreaks. The results showed that the MLSWT of Dianchi Lake increased between 2001 and 2018 and continued to exceed the temperature threshold (17.6°C) for cyanobacterial outbreaks during some timeframes and that the duration of high temperatures also increased. This phenomenon will extend the suitable growth period of cyanobacteria and will have a complex and long-term impact on water quality, the lake ecological environment, and the growth of aquatic organisms.

Environmental factors associated with cyanobacterial assemblages in a mesotrophic subtropical plateau lake: A focus on bloom toxicity

Harmful algal blooms caused by cyanobacteria have been increasing in frequency worldwide. However, the main environmental drivers of this change are often difficult to identify because of the effects of the interaction between eutrophication and climate change. Recently, filamentous N₂-fixing cyanobacteria and non-diazotrophic Microcystis have been observed to be co-existing and undergoing succession in some eutrophic lakes. However, the succession patterns of dominant cyanobacteria and the factors driving this in mesotrophic lakes are not well understood. We hypothesized that the changes in cyanobacterial assemblages in mesotrophic lakes could result in a relatively high risks of toxic blooms, and that these changes are associated with the global climatic changes. We tested these hypotheses using data from the subtropical mesotrophic Lake Erhai. We found that the high spatiotemporal variability in the cyanobacterial community, and the increase in biomass were driven primarily by the growth of bloom-forming cyanobacterial taxa. Species-specific biomasses were related to a different environmental stressor; increases in dissolved organic carbon (DOC) concentrations were statistically associated with an increase of Microcystis biomass, whereas increases in surface water temperature favored higher biomass of Pseudanabaena at low transparency and high concentration of phosphorus. In addition, low nitrogen- to- phosphorus ratios were identified as potential determinants of the abundance of N₂-fixing Dolichospermum. Furthermore, changes in the concentration of DOC, total nitrogen, pH and water transparency levels were found to affect the composition of Microcystis morphotypes and genotypes mostly. This study highlights that the toxic to non-toxic Microcystis ratio might increase with the water darkening and browning (which occurs in many subtropical plateau lakes). Lake management strategies, therefore, need to consider the toxicity of cyanobacterial assemblages in mesotrophic lakes over the intensity of the cyanobacterial blooms.

Interactive effect of water level and flushing rate on population dynamics of a harmful cyanobacterial species: Raphidiopsis raciborskii

Changes in water level and flushing rate directly affect to a large extent the biomass of harmful cyanobacteria, and drive the shift of phytoplankton composition between cyanobacteria dominance/non-dominance in eutrophic waters. Here, we gave a theoretical formula describing the combinational effect of water level and flushing rate on cyanobacterial biomass in eutrophic and well-mixed waters. We also formulated an equation predicting the water level and flushing rate at which cyanobacteria become non-dominating in such water columns. The formulae were confronted with field observations of a low-light adapted cyanobacterium in a large coastal reservoir of southern China. Our formulae demonstrate that water level and flushing rate have an interactive effect on the equilibrium biomass of low-light adapted cyanobacteria in mixed and turbid waters. The formulae were well fitted to the field observation of Raphidiopsis raciborskii population in the reservoir during four dry seasons. In agreement with the theoretical analysis, multiple regression analysis also showed that the interaction between water level and flushing rate is able to interpret the variation of R. raciborskii biomass in the water column. The two formulae are applicable for predicting the response of low-light adapted cyanobacteria to local climate change. Our findings have practical significance in designing measures against the dominance of low light-adapted cyanobacteria in reservoirs.

An application of Bayesian Belief Networks to assess management scenarios for aquaculture in a complex tropical lake system in Indonesia

A Bayesian Belief Network, validated using past observational data, is applied to conceptualize the ecological response of Lake Maninjau, a tropical lake ecosystem in Indonesia, to tilapia cage farms operating on the lake and to quantify its impacts to assist decision making. The model captures ecosystem services trade-offs between cage farming and native fish loss. It is used to appraise options for lake management related to the minimization of the impacts of the cage farms. The constructed model overcomes difficulties with limited data availability to illustrate the complex physical and biogeochemical interactions contributing to triggering mass fish kills due to upwelling and the loss in the production of native fish related to the operation of cage farming. The model highlights existing information gaps in the research related to the management of the farms in the study area, which is applicable to other tropical lakes in general. Model results suggest that internal phosphorous loading (IPL) should be recognized as one of the primary targets of the deep eutrophic tropical lake restoration efforts. Theoretical and practical contributions of the model and model expansions are discussed. Short- and longer-term actions to contribute to a more sustainable management are recommended and include epilimnion aeration and sediment capping.

Modelling the impact of weather parameters on the microbial quality of water in distribution systems

In this study, a framework for integrating weather variables and seasons into the modelling and prediction of the microbial quality in drinking water distribution networks is presented. Statistical analysis and Bayesian network (BN) modelling were used to evaluate relationships among water quality parameters in distribution pipes and their dependencies on weather parameters. Two robust predictive models for Total Bacteria in the network were built based on a deep learning approach (Long Short-Term Memory (LSTM)). The first model included water quality parameters alone as inputs while the second model included weather parameters. The seven-year dataset used in this study constituted water quality parameters measured at seven location in the water distribution network for the city of Ålesund in Norway, and weather data for the same period. Results of the initial statistical analysis and the BN models showed that, air temperature, the summer season, precipitation, as well as water quality parameters namely, residual chlorine, water temperature, alkalinity and electrical conductivity have strong relations with the counts of Total Bacteria in the distribution networks studied. It was found that the integration of the weather parameters in the Total Bacteria prediction models significantly improved the quality of the predictions. Compared to the LSTM 1, LSTM 2 achieved MAE and MSE values as high as to 6.8 and 4.9 times respectively when the model was tested on the seven locations. In addition, the R² values were marginally higher in LSTM 2 (0.92-0.95) than in LSTM (0.81-0.86). The prediction results demonstrate the relevance of integrating weather parameters such as air temperature seasons in predicting bacteria levels in water distribution systems. This suggests that changes in the microbial quality of water in distribution systems and potentially drinking water sources could be reliably assessed by integrating online sensors of water quality and weather parameters with efficient models such as the LSTM. Applying this efficient modelling approach in the management of water supply systems could offer immense support in addressing current challenges in assessing the microbial quality of water and minimizing associated health risks.

CyanoPATH: a knowledgebase of genome-scale functional repertoire for toxic cyanobacterial blooms

CyanoPATH is a database that curates and analyzes the common genomic functional repertoire for cyanobacteria harmful algal blooms (CyanoHABs) in eutrophic waters. Based on the literature of empirical studies and genome/protein databases, it summarizes four types of information: common biological functions (pathways) driving CyanoHABs, customized pathway maps, classification of blooming type based on databases and the genomes of cyanobacteria. A total of 19 pathways are reconstructed, which are involved in the utilization of macronutrients (e.g. carbon, nitrogen, phosphorus and sulfur), micronutrients (e.g. zinc, magnesium, iron, etc.) and other resources (e.g. light and vitamins) and in stress resistance (e.g. lead and copper). These pathways, comprised of both transport and biochemical reactions, are reconstructed with proteins from NCBI and reactions from KEGG and visualized with self-created transport/reaction maps. The pathways are hierarchical and consist of subpathways, protein/enzyme complexes and constituent proteins. New cyanobacterial genomes can be annotated and visualized for these pathways and compared with existing species. This set of genomic functional repertoire is useful in analyzing aquatic metagenomes and metatranscriptomes in CyanoHAB research. Most importantly, it establishes a link between genome and ecology. All these reference proteins, pathways and maps and genomes are free to download at http://www.csbg-jlu.info/CyanoPATH.

Modeling cyanobacterial blooms in tropical reservoirs: The role of physicochemical variables and trophic interactions

Understanding the importance of environmental variables on the dominance of cyanobacteria is crucial for appropriately managing water resources. Although studies about temperate and subtropical regions show a high influence of nutrients and temperature on blooms, this relationship is still unclear for the tropics. Accordingly, we hypothesized that nutrients and temperature are the main factors driving cyanobacterial blooms in tropical reservoirs, and those relationships are intensified by the zooplankton. To test these hypotheses, we constructed a structural equation model based on the monitoring of ten reservoirs from Northeast Brazil. We analyzed the effects of physicochemical variables and zooplankton on cyanobacterial blooms and the biomass of four morphotypes. Cyanobacterial biomass varied within the reservoirs, with bloom records (0.2-268.4 mg L^-1) in all of them, primarily constituted by the colonial morphotype, followed by picocyanobacteria, heterocyted, and non-heterocyted filaments. The cyanobacterial community was driven mainly by chemical variables (55.14% of the variation), followed by physical (48.28%), and zooplankton (39.47%). Through the structural equation model, we demonstrated that total cyanobacterial biomass, as well as the morphotypes, were mainly influenced by omnivorous crustaceans and total dissolved phosphorus. Solar radiation, air temperature, mixing zone, and salinity were important to explain the biomass of the morphotypes. The model explained most of the variation in the picocyanobacterial blooms (79.8%), followed by total cyanobacteria (62.4%), heterocyted filaments (59.1%), non-heterocyted filaments (58.2%), and coccoids (55.1%). Zooplankton groups were also influenced by the physicochemical variables, which presented direct and indirect effects on cyanobacteria. Given the predictions of increased eutrophication, warming, and salinization, cyanobacterial blooms will become more intense in tropical reservoirs. Thus, restoring measures must be adopted to reduce bloom development, such as external phosphorus and salt loadings, and biomanipulation.

Evaluation of algal chlorophyll and nutrient relations and the N:P ratios along with trophic status and light regime in 60 Korea reservoirs

The present study aimed to determine the spatial and temporal variations in trophic state and identify potential causes for these variations in 60 Korean reservoirs. Empirical models were developed using the relations of nutrients (total phosphorus, TP, and total nitrogen, TN) with chlorophyll-a (CHL-a) for efficient lake managements. The empirical models indicated that TP was the key regulating factor for algal growth in agricultural (R² = 0.69) and power generation (R² = 0.50) reservoirs. The CHL-a:TP and TN:TP ratios, indicators of phosphorus limitation, were used to validate the phosphorus reduction approach. The mean CHL-a:TP ratio of agricultural reservoirs was 0.60, indicating that algal chlorophyll is potentially limited by TP than any other factors. Agricultural, multipurpose, and power generation reservoirs, based on the N:P ratios, were more P- limited systems than natural lakes and estuarine reservoirs. The trophic state index (TSI) of Korean reservoirs varied between mesotrophy to hypereutrophy based on values of TSI (TP), TSI (CHL-a), and TSI (SD). Agricultural reservoirs were hypereutrophic using the criteria of TSI (CHL-a) and blue-green algae dominated the algal community. Analysis of trophic state index deviation (TSID) indicated that agricultural reservoirs were primarily P limited and other factors had minor effect. In contrast, the trophic status of estuarine and power generation reservoirs and natural lakes was largely modified by non-algal turbidity. Our outcomes may be effectively used for Korean lakes and reservoirs management.

The role of phosphorus and nitrogen on chlorophyll a: Evidence from hundreds of lakes

The effect of nutrients on phytoplankton biomass in lakes continues to be a subject of debate by aquatic scientists. However, determining whether or not chlorophyll a (CHL) is limited by phosphorus (P) and/or nitrogen (N) is rarely considered using a probabilistic method in studies of hundreds of lakes across broad spatial extents. Several studies have applied a unified CHL-nutrient relationship to determine nutrient limitation, but pose a risk of ecological fallacy because they neglect spatial heterogeneity in ecological contexts. To examine whether or not CHL is limited by P, N, or both nutrients in hundreds of lakes and across diverse ecological settings, a probabilistic machine learning method, Bayesian Network, was applied. Spatial heterogeneity in ecological context was accommodated by the probabilistic nature of the results. We analyzed data from 1382 lakes in 17 US states to evaluate the cause-effect relationships between CHL and nutrients. Observations of CHL, total phosphorus (TP), and total nitrogen (TN) were discretized into three trophic states (oligo-mesotrophic, eutrophic, and hypereutrophic) to train the model. We found that although both nutrients were related to CHL trophic state, TP was more related to CHL than TN, especially under oligo-mesotrophic and eutrophic CHL conditions. However, when the CHL trophic state was hypereutrophic, both TP and TN were important. These results provide additional evidence that P-limitation is more likely under oligo-mesotrophic or eutrophic CHL conditions and that co-limitation of P and N occurs under hypereutrophic CHL conditions. We also found a decreasing pattern of the TN/TP ratio with increasing CHL concentrations, which might be a key driver for the role change of nutrients. Previous work performed at smaller scales support our findings, indicating potential for extension of our findings to other regions. Our findings enhance the understanding of nutrient limitation at macroscales and revealed that the current debate on the limiting nutrient might be caused by failure to consider CHL trophic state. Our findings also provide prior information for the site-specific eutrophication management of unsampled or data-limited lakes.

Effects of the manipulation of submerged macrophytes, large zooplankton, and nutrients on a cyanobacterial bloom: A mesocosm study in a tropical shallow reservoir

Biomanipulation is an efficient tool to control eutrophication and cyanobacterial blooms in temperate lakes. However, the effects of this technique are still unclear for tropical ecosystems. Herein, we evaluated the effects of the biomanipulation on cyanobacterial biomass in a tropical shallow reservoir in Northeast Brazil. A mesocosm experiment was conducted in Tapacurá reservoir (Pernambuco) with eight treatments, in which we factorially manipulated the presence of submerged macrophytes (Ceratophyllum demersum), large herbivorous zooplankton (Sarsilatona serricauda), and nutrients (0.4 mg L^-1 of nitrogen and 0.5 mg L^-1 of phosphorus). On the first, fifth, and tenth days, we analyzed the total biomass of cyanobacteria, and the morphotypes coccoid, heterocyted filamentous, and non-heterocyted filamentous cyanobacteria; these components were compared through a three-way ANOVA. The bloom was composed mainly of five Microcystis morphospecies (coccoids) and Raphidiopsis raciborskii (heterocyted filaments). On the fifth day of the experiment, the combined addition of macrophytes and zooplankton was more efficient at controlling cyanobacterial biomass. On the tenth day, all macrophyte treatments showed significant cyanobacterial biomass reduction, decreasing up to 84.8%. On the other hand, nutrients and zooplankton, both isolated and combined, had no significant effect. Macrophytes also reduced the biomass of coccoids, heterocyted filaments, and non-heterocyted filaments when analyzed separately on the tenth day. Ceratophyllum demersum was more efficient at controlling the bloom than the addition of large herbivorous zooplankton, which could be related to allelopathy since cyanobacterial biomass was also reduced when nutrients were added. The addition of submerged macrophytes with allelopathic potential, associated with the increase of large herbivorous zooplankton, proved to be an efficient technique for controlling tropical cyanobacterial blooms.

A systematic literature review of forecasting and predictive models for cyanobacteria blooms in freshwater lakes

Cyanobacteria harmful blooms (CyanoHABs) in lakes and reservoirs represent a major risk for water authorities globally due to their toxicity and economic impacts. Anticipating bloom occurrence and understanding the main drivers of CyanoHABs are needed to optimize water resources management. An extensive review of the application of CyanoHABs forecasting and predictive models was performed, and a summary of the current state of knowledge, limitations and research opportunities on this topic is provided through analysis of case studies. Two modelling approaches were used to achieve CyanoHABs anticipation; process-based (PB) and data-driven (DD) models. The objective of the model was a determining factor for the choice of modelling approach. PB models were more frequently used to predict future scenarios whereas DD models were employed for short-term forecasts. Each modelling approach presented multiple variations that may be applied for more specific, targeted purposes. Most models reviewed were site-specific. The monitoring methodologies, including data frequency, uncertainty and precision, were identified as a major limitation to improve model performance. A lack of standardization of both model output and performance metrics was observed. CyanoHAB modelling is an interdisciplinary topic and communication between disciplines should be improved to facilitate model comparisons. These shortcomings can hinder the adoption of modelling tools by practitioners. We suggest that water managers should focus on generalising models for lakes with similar characteristics and where possible use high frequency monitoring for model development and validation.

Cyanobacterial Abundance and Microcystin Profiles in Two Southern British Lakes: The Importance of Abiotic and Biotic Interactions

Freshwater cyanobacteria blooms represent a risk to ecological and human health through induction of anoxia and release of potent toxins; both conditions require water management to mitigate risks. Many cyanobacteria taxa may produce microcystins, a group of toxic cyclic heptapeptides. Understanding the relationships between the abiotic drivers of microcystins and their occurrence would assist in the implementation of targeted, cost-effective solutions to maintain safe drinking and recreational waters. Cyanobacteria and microcystins were measured by flow cytometry and liquid chromatography coupled to tandem mass spectrometry in two interconnected reservoirs varying in age and management regimes, in southern Britain over a 12-month period. Microcystins were detected in both reservoirs, with significantly higher concentrations in the southern lake (maximum concentration >7 µg L^-1). Elevated microcystin concentrations were not positively correlated with numbers of cyanobacterial cells, but multiple linear regression analysis suggested temperature and dissolved oxygen explained a significant amount of the variability in microcystin across both reservoirs. The presence of a managed fishery in one lake was associated with decreased microcystin levels, suggestive of top down control on cyanobacterial populations. This study supports the need to develop inclusive, multifactor holistic water management strategies to control cyanobacterial risks in freshwater bodies.

Comparing key drivers of cyanobacteria biomass in temperate and tropical systems

There is growing evidence that cyanobacterial blooms are becoming more common in different parts of the world; within this context, predictive cyanobacteria models have an essential role in lake management. Several models have been successfully used in temperate systems to describe the main drivers of cyanobacterial blooms, but relatively less work has been conducted in the Tropics. We analyzed data from six Brazilian reservoirs and from five Canadian lakes using a combination of regression tree analyses and variation partitioning to evaluate the similarities and differences between regions. Our results, together with a synthesis of the literature from different latitudes, showed that trophic state (i.e. nutrients), climatic variables (e.g., temperature and/or precipitation) and hydrodynamic regimes (i.e. water residence time) are significant drivers of cyanobacteria biomass over several scales. Nutrients came out as the primary predictor in both regions, followed by climate, but when all systems were pooled together, water residence time came out as most important. The consistency in variables identified between regions suggests that these drivers are widely important and cyanobacteria responded quite similarly in different geographical settings and waterbody types (i.e. lakes or reservoirs). However, more work is needed to identify key thresholds across latitudinal gradients. Taken together, these results suggest that multi-region syntheses can help identify drivers that predict broad-scale patterns of cyanobacteria biomass.

Predicting cyanobacterial biovolume from water temperature and conductivity using a Bayesian compound Poisson-Gamma model

Eutrophication and climate change scenarios engender the need to develop good predictive models for harmful cyanobacterial blooms (CyanoHABs). Nevertheless, modeling cyanobacterial biomass is a challenging task due to strongly skewed distributions that include many absences as well as extreme values (dense blooms). Most modeling approaches alter the natural distribution of the data by splitting them into zeros (absences) and positive values, assuming that different processes underlie these two components. Our objectives were (1) to develop a probabilistic model relating cyanobacterial biovolume to environmental variables in the Río de la Plata Estuary (35°S, 56°W, n = 205 observations) considering all biovolume values (zeros and positive biomass) as part of the same process; and (2) to use the model to predict cyanobacterial biovolume under different risk level scenarios using water temperature and conductivity as explanatory variables. We developed a compound Poisson-Gamma (CPG) regression model, an approach that has not previously been used for modeling phytoplankton biovolume, within a Bayesian hierarchical framework. Posterior predictive checks showed that the fitted model had a good overall fit to the observed cyanobacterial biovolume and to more specific features of the data, such as the proportion of samples crossing three threshold risk levels (0.2, 1 and 2 mm³ L^-1) at different water temperatures and conductivities. The CPG model highlights the strong control of cyanobacterial biovolume by nonlinear and interactive effects of water temperature and conductivity. The highest probability of crossing the three biovolume levels occurred at 22.2 °C and at the lowest observed conductivity (∼0.1 mS cm^-1). Cross-validation of the fitted model using out-of-sample observations (n = 72) showed the model's potential to be used in situ, as it enabled prediction of cyanobacterial biomass based on two readily measured variables (temperature and conductivity), making it an interesting tool for early alert systems and management strategies. Furthermore, this novel application demonstrates the potential of the Bayesian CPG approach for predicting cyanobacterial dynamics in response to environmental change.

The Occurrence of Potential Harmful Cyanobacteria and Cyanotoxins in the Obrzyca River (Poland), a Source of Drinking Water

Harmful cyanobacteria and their cyanotoxins may contaminate drinking water resources and their effective control remains challenging. The present study reports on cyanobacterial blooms and associated cyanotoxins in the Obrzyca River, a source of drinking water in Poland. The river was examined from July to October 2019 and concentrations of microcystins, anatoxin-a, and cylindrospermopsin were monitored. The toxicity of water samples was also tested using an ecotoxicological assay. All studied cyanotoxins were detected with microcystins revealing the highest levels. Maximal microcystin concentrations (3.97 μg/L) were determined in September at Uście point, exceeding the provisional guideline. Extracts from Uście point, where the dominant species were Dolichospermum flos-aquae (August), Microcystis aeruginosa (September), and Planktothrix agardhii (October), were toxic for Dugesia tigrina Girard. Microcystin concentrations (MC-LR and MC-RR) were positively correlated with cyanobacteria biovolume. Analysis of the chemical indicators of water quality has shown relationships between them and microcystins as well as cyanobacteria abundance.

Use statistical machine learning to detect nutrient thresholds in Microcystis blooms and microcystin management

The frequency of toxin-producing cyanobacterial blooms has increased in recent decades due to nutrient enrichment and climate change. Because Microcystis blooms are related to different environmental conditions, identifying potential nutrient control targets can facilitate water quality managers to reduce the likelihood of microcystins (MCs) risk. However, complex biotic interactions and field data limitations have constrained our understanding of the nutrient-microcystin relationship. This study develops a Bayesian modelling framework with intracellular and extracellular MCs that characterize the relationships between different environmental and biological factors. This model was fit to the across-lake dataset including three bloom-plagued lakes in China and estimated the putative thresholds of total nitrogen (TN) and total phosphorus (TP). The lake-specific nutrient thresholds were estimated using Bayesian updating process. Our results suggested dual N and P reduction in controlling cyanotoxin risks. The total Microcystis biomass can be substantially suppressed by achieving the putative thresholds of TP (0.10 mg/L) in Lakes Taihu and Chaohu, but a stricter TP target (0.05 mg/L) in Dianchi Lake. To maintain MCs concentrations below 1.0 μg/L, the estimated TN threshold in three lakes was 1.8 mg/L, but the effect can be counteracted by the increase of temperature. Overall, the present approach provides an efficient way to integrate empirical knowledge into the data-driven model and is helpful for the management of water resources.

Predicting fish kills and toxic blooms in an intensive mariculture site in the Philippines using a machine learning model

Harmful algal blooms (HABs) that produce toxins and those that lead to fish kills are global problems that appear to be increasing in frequency and expanding in area. One way to help mitigate their impacts on people's health and livelihoods is to develop early-warning systems. Models to predict and manage HABs typically make use of complex multi-model structures incorporating satellite imagery and frequent monitoring data with different levels of detail into hydrodynamic models. These relatively more sophisticated methods are not necessarily applicable in countries like the Philippines. Empirical statistical models can be simpler alternatives that have also been successful for HAB forecasting of toxic blooms. Here, we present the use of the random forest, a machine learning algorithm, to develop an early-warning system for the prediction of two different types of HABs: fish kill and toxic bloom occurrences in Bolinao-Anda, Philippines, using data that can be obtained from in situ sensors. This site features intensive and extensive mariculture activities, as well as a long history of HABs. Data on temperature, salinity, dissolved oxygen, pH and chlorophyll from 2015 to 2017 were analyzed together with shellfish ban and fish kill occurrences. The random forest algorithm performed well: the fish kill and toxic bloom models were 96.1% and 97.8% accurate in predicting fish kill and shellfish ban occurrences, respectively. For both models, the most important predictive variable was a decrease in dissolved oxygen. Fish kills were more likely during higher salinity and temperature levels, whereas the toxic blooms occurred more at relatively lower salinity and higher chlorophyll conditions. This demonstrates a step towards integrating information from data that can be obtained through real-time sensors into a an early-warning system for two different types of HABs. Further testing of these models through times and different areas are recommended.

Perspective: Advancing the research agenda for improving understanding of cyanobacteria in a future of global change

Harmful cyanobacterial blooms (=cyanoHABs) are an increasing feature of many waterbodies throughout the world. Many bloom-forming species produce toxins, making them of particular concern for drinking water supplies, recreation and fisheries in waterbodies along the freshwater to marine continuum. Global changes resulting from human impacts, such as climate change, over-enrichment and hydrological alterations of waterways, are major drivers of cyanoHAB proliferation and persistence. This review advocates that to better predict and manage cyanoHABs in a changing world, researchers need to leverage studies undertaken to date, but adopt a more complex and definitive suite of experiments, observations, and models which can effectively capture the temporal scales of processes driven by eutrophication and a changing climate. Better integration of laboratory culture and field experiments, as well as whole system and multiple-system studies are needed to improve confidence in models predicting impacts of climate change and anthropogenic over-enrichment and hydrological modifications. Recent studies examining adaptation of species and strains to long-term perturbations, e.g. temperature and carbon dioxide (CO₂) levels, as well as incorporating multi-species and multi-stressor approaches emphasize the limitations of approaches focused on single stressors and individual species. There are also emerging species of concern, such as toxic benthic cyanobacteria, for which the effects of global change are less well understood, and require more detailed study. This review provides approaches and examples of studies tackling the challenging issue of understanding how global changes will affect cyanoHABs, and identifies critical information needs for effective prediction and management.

Nitrate-dependent Uranium mobilisation in groundwater

Nitrate is a critical substance that determines the prevailing redox conditions in groundwater, and in turn the behaviour of Uranium (U). Therefore, the excessive use of nitrate-fertiliser in agricultural catchments could exert a significant influence on U mobilisation. This is a significant issue in catchments, where groundwater resources are increasingly being exploited for drinking water production. Past studies on U mobility in groundwater have considered individual hydro-geochemical factors influencing U concentrations, rather than as a single system with multiple factors. This research study investigated nitrate-dependent U mobility within a catchment in Brazil, where a range of intensive agricultural activities are undertaken and the giant Guarani aquifer is located. The study used direct measurements of groundwater redox conditions and other hydro-geochemical parameters. The research outcomes indicated that U could have two hydro-geochemical systems based on positive and negative redox potential of groundwater. The pH, HCO₃^- and temperature pose the largest influence, respectively, on U mobilisation, and these impacts are greater in agricultural lands than urban areas. Acidic and less reducing (positive redox) groundwater across the aquifer and basic and highly reducing (negative redox) groundwater in agricultural areas make U more mobile. The alkalinity increases U mobility in less reducing groundwater across the aquifer and in highly reducing groundwater in agricultural areas. Further, U can be mobile in hot and less reducing groundwater across the aquifer, but hot and highly reducing groundwater in agricultural areas can limit U mobility. More importantly, the study revealed that U can be mobile under high NO₃^- concentrations in reducing groundwater in non-agricultural areas. However, anthropogenic inputs of NO₃^- are expected to be lower than natural NO₃^- inputs in areas where the groundwater is highly reducing. Hence, fertiliser use in agricultural lands is less likely to increase U mobility in highly reducing groundwater.

Long-Term Spatial and Temporal Monitoring of Cyanobacteria Blooms Using MODIS on Google Earth Engine: A Case Study in Taihu Lake

As cyanobacteria blooms occur in many types of inland water, routine monitoring that is fast and accurate is important for environment and drinking water protection. Compared to field investigations, satellite remote sensing is an efficient and effective method for monitoring cyanobacteria blooms. However, conventional remote sensing monitoring methods are labor intensive and time consuming, especially when processing long-term images. In this study, we embedded related processing procedures in Google Earth Engine, developed an operational cyanobacteria bloom monitoring workflow. Using this workflow, we measured the spatiotemporal patterns of cyanobacteria blooms in China’s Taihu Lake from 2000 to 2018. The results show that cyanobacteria bloom patterns in Taihu Lake have significant spatial and temporal differentiation: the interannual coverage of cyanobacteria blooms had two peaks, and the condition was moderate before 2006, peaked in 2007, declined rapidly after 2008, remained moderate and stable until 2015, and then reached another peak around 2017; bays and northwest lake areas had heavier cyanobacteria blooms than open lake areas; most cyanobacteria blooms primarily occurred in April, worsened in July and August, then improved after October. Our analysis of the relationship between cyanobacteria bloom characteristics and environmental driving factors indicates that: from both monthly and interannual perspectives, meteorological factors are positively correlated with cyanobacteria bloom characteristics, but as for nutrient loadings, they are only positively correlated with cyanobacteria bloom characteristics from an interannual perspective. We believe reducing total phosphorous, together with restoring macrophyte ecosystem, would be the necessary long-term management strategies for Taihu Lake. Our workflow provides an automatic and rapid approach for the long-term monitoring of cyanobacteria blooms, which can improve the automation and efficiency of routine environmental management of Taihu Lake and may be applied to other similar inland waters.

Predicting Lake Quality for the Next Generation: Impacts of Catchment Management and Climatic Factors in a Probabilistic Model Framework

Lake ecosystems across the world are under combined pressures of eutrophication and climate change, which increase the risk of harmful cyanobacteria blooms, reduced ecological status, and degraded ecosystem services. In Europe, the third cycle of river basin management plans (2021–2027) according to the Water Framework Directive must take into account the potential impacts of climate change on water quality, including effects on relevant biological indicators. Here, we applied a Bayesian network as a meta-model for linking future climate and land-use scenarios for the time horizon 2050–2070, via process-based catchment and lake models, to cyanobacteria abundance and ecological status of a eutrophic lake. Building upon previous applications of the model, a new version was developed to include relevant climatic variables such as wind speed. Explorative scenarios showed that the combination of low wind and high temperature gave the most synergistic effects on cyanobacteria under high levels of eutrophication (Chl-a concentration). Considering the management target of good ecological status, however, the climate-related promotion of cyanobacteria blooms contributed most to degrading the ecological status at intermediate levels of eutrophication. Future developments of this model will aim to strengthen the link between climate variables and ecological responses, to make the model also useful for seasonal forecasting.

Nutrients and metals interactions between water and sediment phases: An urban river case study

The provision of water to meet the needs of an ever increasing urban population is a significant challenge. This is because urban receiving waters are constantly at risk from pollutant inputs via stormwater runoff and wastewater discharge. This research study employed multiple approaches including principal component analysis, Bayesian Networks (BNs) modelling and geospatial analysis to identify patterns in the distributions of nutrients and metals in water and sediments in an urban river and the interactions between the two phases. In both, water and sediments, nutrient concentrations/loads varied in the order of total carbon (TC) > total nitrogen (TN) > total phosphorus (TP). The river sediments were found to contain the highest crustal metal loads, while in water, the marine-related metals had the highest concentrations. The BNs modelling of pollutant interactions between water and sediment phases indicated that nitrogen is more likely to be transferred from water to sediment than the opposite, while anthropogenic metals are more likely to be transferred from sediments to water. Further, geospatial analysis showed that TN, crustal metals and anthropogenic metal loads in sediments increased from upstream to downstream, while having a decreasing pattern in water. However, marine-related metals in both, water and sediments had increasing concentrations/loads from upstream to downstream. These spatial patterns are attributed to the interactions between water and sediment phases, sediment transport along the river and seawater intrusion in the estuarine area. The study outcomes are expected to contribute to enhancing the knowledge required for developing mitigation strategies to improve urban receiving water quality.

The Distribution and Prediction of Summer Near-Surface Water Temperatures in Lakes of the Coterminous United States and Southern Canada

The goals of the study were: (i) To describe the distribution of summer near-surface water temperatures in lakes of the coterminous United States and southern Canada (ii) to determine the geographic, meteorological and limnological factors related to summer water temperatures and (iii) to develop and test predictive models for summer near-surface water temperatures. We used data from the United States National Lakes Assessments of 2007 and 2012 as well as data collected from several different studies of Canadian lakes. Using multiple regressions, we quantified the general observations that summer water temperatures decreased when going from south to north, from east to west, and from lower elevations to higher elevations. Our empirical model using 8-day average air temperatures, latitude, longitude, elevations and month was able to predict water temperatures in individual lakes on individual summer days with a standard deviation of 1.7 °C for United States lakes and 2.3 °C for lakes in the southern regions of Canada.

Predicting cyanobacteria bloom occurrence in lakes and reservoirs before blooms occur

With increased global warming, cyanobacteria are blooming more frequently in lakes and reservoirs, severely damaging the health and stability of aquatic ecosystems and threatening drinking water safety and human health. There is an urgent demand for the effective prediction and prevention of cyanobacterial blooms. However, it is difficult to effectively reduce the risks and loss caused by cyanobacterial blooms because most methods are unable to successfully predict cyanobacteria blooms. Therefore, in this study, we proposed a new cyanobacterial bloom occurrence prediction method to analyze the probability and driving factors of the blooms for effective prevention and control. Dominant cyanobacterial species with bloom capabilities were initially determined using a dominant species identification model, and the principal driving factors of the dominant species were then analyzed using canonical correspondence analysis (CCA). Cyanobacterial bloom probability was calculated using a newly-developed model, after which, the probable mutation points were identified and thresholds for the principal driving factors of cyanobacterial blooms were predicted. A total of 141 phytoplankton data sets from 90 stations were collected from six large-scale hydrology, water-quality ecology, integrated field surveys in Jinan City, China in 2014-2015 and used for model application and verification. The results showed that there were six dominant cyanobacterial species in the study area, and that the principal driving factors were water temperature, pH, total phosphorus, ammonia nitrogen, chemical oxygen demand, and dissolved oxygen. The cyanobacterial blooms corresponded to a threshold water temperature range, pH, total phosphorus (TP), ammonium nitrogen level, chemical oxygen demand, and dissolved oxygen levels of 19.5-32.5 °C, 7.0-9.38, 0.13-0.22 mg L^-1, 0.38-0.63 mg L^-1, 10.5-17.5 mg L^-1, and 4.97-8.28 mg L^-1, respectively. Comparison with research results from other global regions further supported the use of these thresholds, indicating that this method could be used in habitats beyond China. We found that the probability of cyanobacterial bloom was 0.75, a critical point for prevention and control. When this critical point was exceeded, cyanobacteria could proliferate rapidly, increasing the risk of cyanobacterial blooms. Changes in driving factors need to be rapidly controlled, based on these thresholds, to prevent cyanobacterial blooms. Temporal and spatial scales were critical factors potentially affecting the selection of driving factors. This method is versatile and can help determine the risk of cyanobacterial blooms and the thresholds of the principal driving factors. It can effectively predict and help prevent cyanobacterial blooms to reduce the global probability of occurrence, protect the health and stability of water ecosystems, ensure drinking water safety, and protect human health.

Analysis of environmental drivers influencing interspecific variations and associations among bloom-forming cyanobacteria in large, shallow eutrophic lakes

Non-diazotrophic Microcystis and filamentous N₂-fixing Aphanizomenon and Dolichospermum (formerly Anabaena) co-occur or successively dominate freshwaters globally. Previous studies indicate that dual nitrogen (N) and phosphorus (P) reduction is needed to control cyanobacterial blooms; however, N limitation may cause replacement of non-N₂-fixing by N₂-fixing taxa. To evaluate potentially counterproductive scenarios, the effects of temperature, nutrients, and zooplankton on the spatio-temporal variations of cyanobacteria were investigated in three large, shallow eutrophic lakes in China. The results illustrate that the community composition of cyanobacteria is primarily driven by physical factors and the zooplankton community, and their interactions. Niche differentiation between Microcystis and two N₂-fixing taxa in Lake Taihu and Lake Chaohu was observed, whereas small temperature fluctuations in Lake Dianchi supported co-dominance. Through structural equation modelling, predictor variables were aggregated into 'composites' representing their combined effects on species-specific biomass. The model results showed that Microcystis biomass was affected by water temperature and P concentrations across the studied lakes. The biomass of two filamentous taxa, by contrast, exhibited lake-specific responses. Understanding of driving forces of the succession and competition among bloom-forming cyanobacteria will help to guide lake restoration in the context of climate warming and N:P stoichiometry imbalances.

Application of Bayesian network including Microcystis morphospecies for microcystin risk assessment in three cyanobacterial bloom-plagued lakes, China

Microcystis spp., which occur as colonies of different sizes under natural conditions, have expanded in temperate and tropical freshwater ecosystems and caused seriously environmental and ecological problems. In the current study, a Bayesian network (BN) framework was developed to access the probability of microcystins (MCs) risk in large shallow eutrophic lakes in China, namely, Taihu Lake, Chaohu Lake, and Dianchi Lake. By means of a knowledge-supported way, physicochemical factors, Microcystis morphospecies, and MCs were integrated into different network structures. The sensitive analysis illustrated that Microcystis aeruginosa biomass was overall the best predictor of MCs risk, and its high biomass relied on the combined condition that water temperature exceeded 24 °C and total phosphorus was above 0.2 mg/L. Simulated scenarios suggested that the probability of hazardous MCs (≥1.0 μg/L) was higher under interactive effect of temperature increase and nutrients (nitrogen and phosphorus) imbalance than that of warming alone. Likewise, data-driven model development using a naïve Bayes classifier and equal frequency discretization resulted in a substantial technical performance (CCI = 0.83, K = 0.60), but the performance significantly decreased when model excluded species-specific biomasses from input variables (CCI = 0.76, K = 0.40). The BN framework provided a useful screening tool to evaluate cyanotoxin in three studied lakes in China, and it can also be used in other lakes suffering from cyanobacterial blooms dominated by Microcystis.

Chlorophyll a predictability and relative importance of factors governing lake phytoplankton at different timescales

Assessing the key drivers of eutrophication in lakes and reservoirs has long been a challenge, and many studies have developed empirical models for predicting the relative importance of these drivers. However, the relative roles of various parameters might differ not only spatially (between regions or localities) but also at a temporal scale. In this study, the relative roles of total phosphorus, total nitrogen, ammonia, wind speed and water temperature were selected as potential drivers of phytoplankton biomass by using chlorophyll a as a proxy for biomass. A generalized additive model (GAM) and a random forest model (RF) were developed to assess the predictability of chlorophyll a and the relative importance of various predictors driving algal blooms at different timescales in a freshwater lake. The results showed that the daily datasets yielded better predictability than the monthly datasets. In addition, at a daily scale, water temperature was a more important predictor of chlorophyll a than nutrients, and the importance of phosphorus was comparable to that of nitrogen. In contrast, at a monthly scale, nutrients are more important predictors than water temperature and phosphorus is a better predictor than nitrogen. This study indicates that the drivers of phytoplankton fluctuations vary at different timescales and that timescale has an influence on the relative roles of nitrogen and phosphorus limitation in lakes, which suggests that the temporal scale should be considered when explaining phytoplankton fluctuations. Moreover, this study provides a reference for the monitoring of phytoplankton fluctuations and for understanding the mechanisms underlying phytoplankton fluctuations at different timescales.

Assessing mercury pollution in Amazon River tributaries using a Bayesian Network approach

Mercury pollution of water bodies exerts significant human and ecosystem health impacts due to high toxicity. Relatively high levels of mercury have been detected in the Amazon River and its tributaries and associated lakes. The study employed a Bayesian Network approach to investigate the contribution from geogenic sources to mercury pollution of lakes in the Madeira River basin, which is the largest tributary of the Amazon River. It was found that the source indicators of naturally occurring mercury have both, positive and negative relationships with mercury in lake sediments. Although the positive relationships indicated the influence of geological and soil formations, the negative relationships implied that the use of mercury amalgam for gold extraction in artisanal and small-scale mining (ASM), which is the primary anthropogenic source of mercury, also contribute to mercury in Amazon tributaries. This was further evident as mercury concentrations in lake sediments were found to be significantly higher than those in the surrounding rocks. However, potential anthropogenic mercury was attributed to historical inputs from gold mining due to the recent decline of ASM mining practice in the region.

Influence of urbanisation characteristics on the variability of particle-bound heavy metals build-up: A comparative study between China and Australia

Heavy metal pollution of urban stormwater poses potential risks to human and ecosystem health. The design of reliable pollution mitigation strategies requires reliable stormwater modelling approaches. Current modelling practices do not consider the influence of urbanisation characteristics on stormwater quality. This could undermine the accuracy of stormwater quality modelling results. This research study used a database consisting of over 1000 datasets to compare the characteristics of heavy metal build-up (one of the most important stormwater pollutant processes) on urban surfaces under the influence of anthropogenic and natural factors specific to different urban regions from China (Shenzhen) and Australia (Gold Coast), using Bayesian Networks. The outcomes show that the differences in heavy metals build-up loads between the two regions (mean value for Shenzhen - mean value for Gold Coast)/mean value for Shenzhen) were 0.45 (Al), 0.88 (Cr), 0.99 (Mn), 0.68 (Fe), 0.98 (Ni), 0.24 (Cu), 0.47 (Zn) and 0.13 (Pb), respectively. The research outcomes also confirmed that the influence of traffic on the build-up of different sized particles varies between Shenzhen and Gold Coast, and traffic plays distinct roles as a source and as a factor that drives heavy metal re-distribution. The road surface roughness was also found to influence build-up process differently between the two regions. More importantly, the assessment of inherent process uncertainty revealed that heavy metal build-up between different road sites in Shenzhen varies over a wider range than in Gold Coast. The study highlighted a clear distinction in the influence of sources and key anthropogenic factors on the variability of particle-bound heavy metals build-up between geographically different urban regions. The study outcomes provide new knowledge to enhance the accuracy of urban stormwater quality modelling.

Fluorescence probes for real-time remote cyanobacteria monitoring: A review of challenges and opportunities

In recent years, there has been a widespread deployment of submersible fluorescence sensors by water utilities. They are used to measure diagnostic pigments and estimate algae and cyanobacteria abundance in near real-time. Despite being useful and promising tools, operators and decision-makers often rely on the data provided by these probes without a full understanding of their limitations. As a result, this may lead to wrong and misleading estimations which, in turn, means that researchers and technicians distrust these sensors. In this review paper, we list and discuss the main limitations of such probes, as well as identifying the effect of environmental factors on pigment production, and in turn, the conversion to cyanobacteria abundance estimation. We argue that a comprehensive calibration approach to obtain reliable readings goes well beyond manufacturers' recommendations, and should involve several context-specific experiments. We also believe that if such a comprehensive set of experiments is conducted, the data collected from fluorescence sensors could be used in artificial intelligence modelling approaches to reliably predict, in near real-time, the presence and abundance of different cyanobacteria species. This would have significant benefits for both drinking and recreational water management, given that cyanobacterial toxicity, and taste and odour compounds production, are species-dependent.

Influence of traffic on build-up of polycyclic aromatic hydrocarbons on urban road surfaces: A Bayesian network modelling approach

Due to their carcinogenic effects, Polycyclic Aromatic Hydrocarbons (PAHs) deposited on urban surfaces are a major concern in the context of stormwater pollution. However, the design of effective pollution mitigation strategies is challenging due to the lack of reliability in stormwater quality modelling outcomes. Current modelling approaches do not adequately replicate the interdependencies between pollutant processes and their influential factors. Using Bayesian Network modelling, this research study characterised the influence of vehicular traffic on the build-up of the sixteen US EPA classified priority PAHs. The predictive analysis was conditional on the structure of the proposed BN, which can be further improved by including more variables. This novel modelling approach facilitated the characterisation of the influence of traffic as a source of origin and also as a key factor that influences the re-distribution of PAHs, with positive or negative relationship between traffic volume and PAH build-up. It was evident that the re-distribution of particle-bound PAHs is determined by the particle size rather than the chemical characteristics such as volatility. Moreover, compared to commercial and residential land uses, mostly industrial land use contributes to the PAHs load released to the environment. Carcinogenic PAHs in industrial areas are likely to be associated with finer particles, while PAHs, which are not classified as human carcinogens, are likely to be found in the coarser particle fraction.