Estimating cyanobacterial bloom transport by coupling remotely sensed imagery and a hydrodynamic model

Short-term Lake Erie algal bloom prediction by classification and regression models

The recent outbreaks of harmful algal blooms in the western Lake Erie Basin (WLEB) have drawn tremendous attention to bloom prediction for better control and management. Many weekly to annual bloom prediction models have been reported, but they only employ small datasets, have limited types of input features, build linear regression or probabilistic models, or require complex process-based computations. To address these limitations, we conducted a comprehensive literature review, complied a large dataset containing chlorophyll-a index (from 2002 to 2019) as the output and a novel combination of riverine (the Maumee & Detroit Rivers) and meteorological (WLEB) features as the input, and built machine learning-based classification and regression models for 10-d scale bloom predictions. By analyzing the feature importance, we identified 8 most important features for the HAB control, including nitrogen loads, time, water levels, soluble reactive phosphorus load, and solar irradiance. Here, both long- and short-term nitrogen loads were for the first time considered in HAB models for Lake Erie. Based on these features, the 2-, 3-, and 4-level random forest classification models achieved an accuracy of 89.6%, 77.0%, and 66.7%, respectively, and the regression model achieved an R² value of 0.69. In addition, long-short term memory (LSTM) was implemented to predict temporal trends of four short-term features (N, solar irradiance, and two water levels) and achieved the Nash-Sutcliffe efficiency of 0.12-0.97. Feeding the LSTM model predictions for these features into the 2-level classification model reached an accuracy of 86.0% for predicting the HABs in 2017-2018, suggesting that we can provide short-term HAB forecasts even when the feature values are not available.

Learning and inferring the diurnal variability of cyanobacterial blooms from high-frequency time-series satellite-based observations

Observational evidences have suggested that the surface scums of cyanobacterial harmful blooms (CyanoHABs) are highly patchy, and their spatial patterns can vary significantly within hours. This stresses the need for the capacity to monitor and predict their occurrence with better spatiotemporal continuity, in order to understand and mitigate their causes and impacts. Although polar-orbiting satellites have long been used to monitor CyanoHABs, these sensors cannot be used to capture the diurnal variability of the bloom patchiness due to their long revisit periods. In this study, we use the Himawari-8 geostationary satellite to generate high-frequency time-series observations of CyanoHABs on a sub-daily basis not possible from previous satellites. On top of that, we introduce a spatiotemporal deep learning method (ConvLSTM) to predict the dynamics of bloom patchiness at a lead time of 10 min. Our results show that the bloom scums were highly patchy and dynamic, and the diurnal variability was assumed to be largely associated with the migratory behavior of cyanobacteria. We also show that, ConvLSTM displayed fairly satisfactory performance with promising predictive capability, with Root Mean Square Error (RMSE) and determination coefficient (R²) varying between 0.66∼1.84 μg/L and 0.71∼0.94, respectively. This suggests that, by adequately capturing spatiotemporal features, the diurnal variability of CyanoHABs can be well learned and inferred by ConvLSTM. These results may have important practical implications, because they suggest that spatiotemporal deep learning integrated with high-frequency satellite observations could provide a new methodological paradigm in nowcasting of CyanoHABs.

Advancing freshwater ecological forecasts: Harmful algal blooms in Lake Erie

Ecological models help provide forecasts of ecosystem responses to natural and anthropogenic stresses. However, their ability to create reliable predictions requires forecasts with track records sufficiently long to build confidence, skill assessments, and treating uncertainty quantitatively. We use Lake Erie harmful algal blooms as a case study to help formalize ecological forecasting. Key challenges for models include uncertainty in the deterministic structure of the load-bloom relationship and the need to assess alternative drivers (e.g., biologically available phosphorus load, spring load, longer term cumulative load) with a larger dataset. We enhanced a Bayesian model considering new information and an expanded data set, test it through cross validation and blind forecasts, quantify and discuss its uncertainties, and apply it for assessing historical and future scenarios. Allowing a segmented relationship between bloom size and spring load indicates that loading above 0.15 Gg/month will have a substantially higher marginal impact on bloom size. The new model explains 84 % of interannual variability (9.09 Gg RMSE) when calibrated to the 19-year data set and 66 % of variability in cross validation (12.58 Gg RMSE). Blind forecasts explain 84 % of HAB variability between 2014 and 2020, which is substantially better than the actual forecast track record (R² = 0.32) over this same period. Because of internal phosphorus recycling, represented by the long-term cumulative load, it could take over a decade for HABs to fully respond to loading reductions, depending on the pace of those reductions. Thus, the desired speed and endpoint of the lake's recovery should be considered when updating and adaptively managing load reduction targets. Results are discussed in the context of ecological forecasting best pactices: incorporate new knowledge and data in model construction; account for multiple sources of uncertainty; evaluate predictive skill through validation and hindcasting; and answer management questions related to both short-term forecasts and long-term scenarios.

Development of a web-based modelling framework for harmful algal blooms transport simulation using open-source technologies

Desktop-based modelling packages presented typical limitations in interactive simulation. This study presents a web-based modelling framework that fully consolidated the simulation work-flow into a WebGIS application, providing a one-step solution for HABs transport simulation within an intuitive and interactive modelling environment. An improved Lagrangian particle-tracking scheme was proposed using fractional Brownian motion technique. The presented model was devoted to quickly forecast the transport pathways in both temporal and spatial dimensions, and evaluate the approximate trends and qualitative understanding of HABs development in data-poor situations. The web modelling platform was developed using multiple open-source JavaScript libraries. The developed WebGIS application provides user-friendly interfaces to prepare inputs, configure simulation settings, visualize, analyse, and validate simulation results within the same framework. The feasibility, capacity, and advantage of the proposed framework were tested and evaluated in a real-world application of red tide transport simulation in the Qinhuangdao coastal waters. The model results showed qualitative agreement with the red tide observed from remote sensing. Our experimental results demonstrated that the developed web-based modelling prototype would present a useful performance for study cases related to HABs transport simulation.

Individual-based modelling of cyanobacteria blooms: Physical and physiological processes

Lakes and reservoirs throughout the world are increasingly adversely affected by cyanobacterial harmful algal blooms (CyanoHABs). The development and spatiotemporal distributions of blooms are governed by complex physical mixing and transport processes that interact with physiological processes affecting the growth and loss of bloom-forming species. Individual-based models (IBMs) can provide a valuable tool for exploring and integrating some of these processes. Here we contend that the advantages of IBMs have not been fully exploited. The main reasons for the lack of progress in mainstreaming IBMs in numerical modelling are their complexity and high computational demand. In this review, we identify gaps and challenges in the use of IBMs for modelling CyanoHABs and provide an overview of the processes that should be considered for simulating the spatial and temporal distributions of cyanobacteria. Notably, important processes affecting cyanobacteria distributions, in particular their vertical passive movement, have not been considered in many existing lake ecosystem models. We identify the following research gaps that should be addressed in future studies that use IBMs: 1) effects of vertical movement and physiological processes relevant to cyanobacteria growth and accumulations, 2) effects and feedbacks of CyanoHABs on their environment; 3) inter and intra-specific competition of cyanobacteria species for nutrients and light; 4) use of high resolved temporal-spatial data for calibration and verification targets for IBMs; and 5) climate change impacts on the frequency, intensity and duration of CyanoHABs. IBMs are well adapted to incorporate these processes and should be considered as the next generation of models for simulating CyanoHABs.

Comparative analysis of Microcystis buoyancy in western Lake Erie and Saginaw Bay of Lake Huron

Microcystis is the predominant genus of harmful cyanobacterium in both Lake Erie and Saginaw Bay of Lake Huron and has the capacity to regulate the buoyancy of its colonies, sinking under certain conditions while floating towards the surface in others. Understanding the factors that control buoyancy is critical for interpretation of remote sensing data, modeling and forecasting harmful algal blooms within these two systems. To determine if Microcystis colony buoyancy in the two lakes responds similarly to diurnal light cycles, colony buoyant velocity (floating/sinking terminal velocity in a quiescent water column) and size were measured after manipulating the intensity of sunlight. Overall, there were more positively buoyant (floating) colonies in Lake Erie while most of the colonies in Saginaw Bay were negatively buoyant (sinking). In Lake Erie the colonies became less buoyant at increased light intensities and were less buoyant in the afternoon than in the morning. In both lakes, apparent colony density was more variable among small colonies (< 200 µm), whereas larger colonies showed a diminished response of density to light intensity and duration. These findings suggest that colony density becomes less plastic as colonies increase in size, leading to a weak relationship between size and velocity. These relationships may ultimately affect how the bloom is transported throughout each system and will help explain observed differences in vertical distribution and movement of Microcystis in the two lakes.

Quantifying uncertainty cascading from climate, watershed, and lake models in harmful algal bloom predictions

In response to increased harmful algal blooms (HABs), hypoxia, and nearshore algae growth in Lake Erie, the United States and Canada agreed to phosphorus load reduction targets. While the load targets were guided by an ensemble of models, none of them considered the effects of climate change. Some watershed models developed to guide load reduction strategies have simulated climate effects, but without extending the resulting loads or their uncertainties to HAB projections. In this study, we integrated an ensemble of four climate models, three watershed models, and four HAB models. Nutrient loads and HAB predictions were generated for historical (1985-1999), current (2002-2017), and mid-21st-century (2051-2065) periods. For the current and historical periods, modeled loads and HABs are comparable to observations but exhibit less interannual variability. Our results show that climate impacts on watershed processes are likely to lead to reductions in future loading, assuming land use and watershed management practices are unchanged. This reduction in load should help reduce the magnitude of future HABs, although increases in lake temperature could mitigate that decrease. Using Monte-Carlo analysis to attribute sources of uncertainty from this cascade of models, we show that the uncertainty associated with each model is significant, and that improvements in all three are needed to build confidence in future projections.

Assessing the origin of a massive cyanobacterial bloom in the Río de la Plata (2019): Towards an early warning system

The Río de la Plata estuary drains the second largest river basin of South America. The occurrence of frequent cyanobacterial blooms of the Microcystis and Dolichospermum complex in the Uruguayan coast are associated with high flows of Uruguay River due to rainy years. In summer 2019, a massive cyanobacterial bloom reached up to the Uruguayan Atlantic coast. This study seeks to unveil the origin and the environmental conditions that favored the occurrence of the last cyanobacterial bloom in the Río de la Plata, and to contribute with the development of an early warning system of cyanobacterial scum on Montevideo beaches. A complementary approach was applied with Sentinel-2 imagery, environmental data of monitoring programs of Salto Grande Reservoir and Montevideo beaches, hydro-meteorological information, and hydroelectric dam operation. Images were analyzed with the Normalized Difference Chlorophyll Index (NDCI), which allowed evaluating several water bodies within the same ranges. Positive anomalous rainfall increased river flows, particularly that of Uruguay and Negro rivers, which caused the opening of the dam spillways. NDCI maps showed that areas with high values (NDCI_>0.06) in Salto Grande reservoir kept a similar surface area before and after the prolonged overflow period (8.7-7.8 km², before and after). In the Río Negro reservoirs, however, NDCI_>0.06 coverage remarkably changed (62.5 km², Palmar reservoir), with a subsequent 56-fold reduction in the post-discharge of surface water. Twenty days after opening the spillways, Montevideo beaches were closed to swimming and the NDCI_>0.06 surface reached 51.7 km² in the Río de la Plata coast. The dynamics of NDCI areas, the downstream bloom discharge, and the predicted Río de la Plata residual currents, suggest that the cyanobacterial bloom originated in the Negro River (Palmar reservoir). This bloom event was one of the worst that occurred in the Río de la Plata in last 20 years, circulated along the Uruguayan sub-corridor to the Atlantic coast along 690 km from its origin, and lasted three months on Montevideo coast. This is the first study that addresses the impact of cyanobacterial blooms from the Negro River reservoirs on the Río de la Plata estuary. Therefore, the Negro River basin is where the main efforts should be directed to mitigate massive cyanobacterial blooms.

Modeling harmful algal blooms in a changing climate

This review assesses harmful algal bloom (HAB) modeling in the context of climate change, examining modeling methodologies that are currently being used, approaches for representing climate processes, and time scales of HAB model projections. Statistical models are most commonly used for near-term HAB forecasting and resource management, but statistical models are not well suited for longer-term projections as forcing conditions diverge from past observations. Process-based models are more complex, difficult to parameterize, and require extensive calibration, but can mechanistically project HAB response under changing forcing conditions. Nevertheless, process-based models remain prone to failure if key processes emerge with climate change that were not identified in model development based on historical observations. We review recent studies on modeling HABs and their response to climate change, and the various statistical and process-based approaches used to link global climate model projections and potential HAB response. We also make several recommendations for how the field can move forward: 1) use process-based models to explicitly represent key physical and biological factors in HAB development, including evaluating HAB response to climate change in the context of the broader ecosystem; 2) quantify and convey model uncertainty using ensemble approaches and scenario planning; 3) use robust approaches to downscale global climate model results to the coastal regions that are most impacted by HABs; and 4) evaluate HAB models with long-term observations, which are critical for assessing long-term trends associated with climate change and far too limited in extent.

A space-time geostatistical model for probabilistic estimation of harmful algal bloom biomass and areal extent

Harmful algal blooms (HABs) have been increasing in intensity worldwide, including the western basin of Lake Erie. Substantial efforts have been made to track these blooms using in situ sampling and remote sensing. However, such measurements do not fully capture HAB spatial and temporal dynamics due to the limitations of discrete shipboard sampling over large areas and the effects of clouds and winds on remote sensing estimates. To address these limitations, we develop a space-time geostatistical modeling framework for estimating HAB intensity and extent using chlorophyll a data sampled during the HAB season (June-October) from 2008 to 2017 by five independent monitoring programs. Based on the Bayesian information criterion for model selection, trend variables explain bloom northerly and easterly expansion from Maumee Bay, wind effects over depth, and variability among sampling methods. Cross validation results demonstrate that space-time kriging explains over half of the variability in daily, location-specific chlorophyll observations, on average. Conditional simulations provide, for the first time, comprehensive estimates of overall bloom biomass (based on depth-integrated concentrations) and surface areal extent with quantified uncertainties. These new estimates are contrasted with previous Lake Erie HAB monitoring studies, and deviations among estimates are explored and discussed. Overall, results highlight the importance of maintaining sufficient monitoring coverage to capture bloom dynamics, as well as the benefits of the proposed approach for synthesizing data from multiple monitoring programs to improve estimation accuracy while reducing uncertainty.

Tracking cyanobacteria blooms: Do different monitoring approaches tell the same story?

Cyanobacteria blooms are a major environmental issue worldwide. Our understanding of the biophysical processes driving cyanobacterial proliferation and the ability to develop predictive models that inform resource managers and policy makers rely upon the accurate characterization of bloom dynamics. Models quantifying relationships between bloom severity and environmental drivers are often calibrated to an individual set of bloom observations, and few studies have assessed whether differences among observing platforms could lead to contrasting results in terms of relevant bloom predictors and their estimated influence on bloom severity. The aim of this study was to assess the degree of coherence of different monitoring methods in (1) capturing short- and long-term cyanobacteria bloom dynamics and (2) identifying environmental drivers associated with bloom variability. Using western Lake Erie as a case study, we applied boosted regression tree (BRT) models to long-term time series of cyanobacteria bloom estimates from multiple in-situ and remote sensing approaches to quantify the relative influence of physico-chemical and meteorological drivers on bloom variability. Results of BRT models showed remarkable consistency with known ecological requirements of cyanobacteria (e.g., nutrient loading, water temperature, and tributary discharge). However, discrepancies in inter-annual and intra-seasonal bloom dynamics across monitoring approaches led to some inconsistencies in the relative importance, shape, and sign of the modeled relationships between select environmental drivers and bloom severity. This was especially true for variables characterized by high short-term variability, such as wind forcing. These discrepancies might have implications for our understanding of the role of different environmental drivers in regulating bloom dynamics, and subsequently for the development of models capable of informing management and decision making. Our results highlight the need to develop methods to integrate multiple data sources to better characterize bloom spatio-temporal variability and improve our ability to understand and predict cyanobacteria blooms.

Measuring spatial variation in secondary production and food quality using a common consumer approach in Lake Erie

Lake Erie is a large lake straddling the border of the USA and Canada that has become increasingly eutrophic in recent years. Eutrophication is particularly focused in the shallow western basin. The western basin of Lake Erie is hydrodynamically similar to a large estuary, with riverine inputs from the Detroit and Maumee Rivers mixing together and creating gradients in chemical and physical conditions. This study was driven by two questions: (1) How does secondary production and food quality for consumers vary across this large mixing zone? and (2) Are there correlations between cyanobacterial abundance and secondary production or food quality for consumers? Measuring spatial and temporal variation in secondary production and food quality is difficult for a variety of logistical reasons, so here a common consumer approach was used. In a common consumer approach, individuals of a single species are raised under similar conditions until placed in the field across environmental gradients of interest. After some period of exposure, the response of that common consumer is measured to provide an index of spatial variation in conditions. Here, a freshwater mussel (Lampsilis siliquoidea) was deployed at 32 locations that spanned habitat types and a gradient in cyanobacterial abundance in the western basin of Lake Erie to measure spatial variation in growth (an index of secondary production) and fatty acid (FA) content (an index of food quality). We found secondary production was highest within the Maumee river mouth and lowest in the open waters of the lake. Mussel tissues in the Maumee river mouth also included more eicosapentaenoic and docosapentaenoic fatty acids (EPA and DPA, respectively), but fewer bacterial FAs, suggesting more algae at the base of the food web in the Maumee river mouth compared to open lake sites. The satellite-derived estimate of cyanobacterial abundance was not correlated to secondary production, but was positively related to EPA and DPA content in the mussels, suggesting more of these important FAs in locations with more cyanobacteria. These results suggest that growth of secondary consumers and the availability of important fatty acids in the western basin are centered on the Maumee river mouth.

A HAB warning system for shellfish harvesting in Portugal

The development of sustainable shellfish aquaculture is highly dependent on the provision of reliable monitoring and predictive information on the occurrence of harmful algal blooms (HABs). The Portuguese HAB early warning system and shellfish closures presented here is a prototype, developed in the ASIMUTH project. It relies on weekly monitoring data composed of observations of HAB species and toxin concentrations within shellfish, and ocean circulation forecasts generated by an operational oceanographic model. The shellfish harvesting areas comprise coastal areas, estuaries+rías and coastal lagoons. The weekly bulletin characterizes the current shellfish closure situation and next week's forecasts for potentially impacted areas. The period analyzed ranged from 27 July 2013 to 17 March 2014, and describes the first skill assessment of the warning system. The forecast accuracy was evaluated, considering the number of forecasts that were verified to be correct the following week (85%) as well as the number of events not forecasted (false negatives, 12%) and those expected but did not occur (false positives, 3%). Variations were most visible in the first weeks of bulletin implementation and during autumn-winter months. The complementary use of field data, remote sensing and operational models led to more accurate predictions of blooms and range of the event.

Spatial and temporal patterns in the seasonal distribution of toxic cyanobacteria in Western Lake Erie from 2002-2014

Lake Erie, the world's tenth largest freshwater lake by area, has had recurring blooms of toxic cyanobacteria for the past two decades. These blooms pose potential health risks for recreation, and impact the treatment of drinking water. Understanding the timing and distribution of the blooms may aid in planning by local communities and resources managers. Satellite data provides a means of examining spatial patterns of the blooms. Data sets from MERIS (2002-2012) and MODIS (2012-2014) were analyzed to evaluate bloom patterns and frequencies. The blooms were identified using previously published algorithms to detect cyanobacteria (~25,000 cells mL-1), as well as a variation of these algorithms to account for the saturation of the MODIS ocean color bands. Images were binned into 10-day composites to reduce cloud and mixing artifacts. The 13 years of composites were used to determine frequency of presence of both detectable cyanobacteria and high risk (>100,000 cells mL-1) blooms. The bloom season according to the satellite observations falls within June 1 and October 31. Maps show the pattern of development and areas most commonly impacted during all years (with minor and severe blooms). Frequencies during years with just severe blooms (minor bloom years were not included in the analysis) were examined in the same fashion. With the annual forecasts of bloom severity, these frequency maps can provide public water suppliers and health departments with guidance on the timing of potential risk.

Long-term and seasonal trend decomposition of Maumee River nutrient inputs to western Lake Erie

Cyanobacterial blooms in western Lake Erie have recently garnered widespread attention. Current evidence indicates that a major source of the nutrients that fuel these blooms is the Maumee River. We applied a seasonal trend decomposition technique to examine long-term and seasonal changes in Maumee River discharge and nutrient concentrations and loads. Our results indicate similar long-term increases in both regional precipitation and Maumee River discharge (1975-2013), although changes in the seasonal cycles are less pronounced. Total and dissolved phosphorus concentrations declined from the 1970s into the 1990s; since then, total phosphorus concentrations have been relatively stable, while dissolved phosphorus concentrations have increased. However, both total and dissolved phosphorus loads have increased since the 1990s because of the Maumee River discharge increases. Total nitrogen and nitrate concentrations and loads exhibited patterns that were almost the reverse of those of phosphorus, with increases into the 1990s and decreases since then. Seasonal changes in concentrations and loads were also apparent with increases since approximately 1990 in March phosphorus concentrations and loads. These documented changes in phosphorus, nitrogen, and suspended solids likely reflect changing land-use practices. Knowledge of these patterns should facilitate efforts to better manage ongoing eutrophication problems in western Lake Erie.

Is exposure to cyanobacteria an environmental risk factor for amyotrophic lateral sclerosis and other neurodegenerative diseases?

There is a broad scientific consensus that amyotrophic lateral sclerosis (ALS) is caused by gene-environment interactions. Mutations in genes underlying familial ALS (fALS) have been discovered in only 5-10% of the total population of ALS patients. Relatively little attention has been paid to environmental and lifestyle factors that may trigger the cascade of motor neuron death leading to the syndrome of ALS, although exposure to chemicals including lead and pesticides, and to agricultural environments, smoking, certain sports, and trauma have all been identified with an increased risk of ALS. There is a need for research to quantify the relative roles of each of the identified risk factors for ALS. Recent evidence has strengthened the theory that chronic environmental exposure to the neurotoxic amino acid β-N-methylamino-L-alanine (BMAA) produced by cyanobacteria may be an environmental risk factor for ALS. Here we describe methods that may be used to assess exposure to cyanobacteria, and hence potentially to BMAA, namely an epidemiologic questionnaire and direct and indirect methods for estimating the cyanobacterial load in ecosystems. Rigorous epidemiologic studies could determine the risks associated with exposure to cyanobacteria, and if combined with genetic analysis of ALS cases and controls could reveal etiologically important gene-environment interactions in genetically vulnerable individuals.