Reporting on the status,trends, and drivers of algal blooms on Lake of the Woods using satellite-derived bloom indices (2002–2021)

Despite significant declines in external phosphorus loads, Lake of the Woods continues to experience severe recurring cyanobacterial harmful algal blooms (cHABs) covering as much as 80% of the lake surface area. Satellite-derived bloom indices were used to assess the status, trends, and drivers of cHAB conditions for the period 2002 to 2021 in support of developing ecosystem objectives and response indicators for the lake. Areas of greatest potential concern, with the most prolonged bloom occurrences, were in the southeast of the lake. Significant decreases in bloom indices suggest the lake may now be responding to historical nutrient reductions. The greatest rates of decrease were within the main water flow paths, with little change in the more isolated embayments, suggesting flushing plays a key role in regulating regional bloom severity. Significant inter-annual variability in bloom phenology was observed, with blooms peaking later in recent years, which may be in response to climate-induced changes in the lake and watershed. The absence of a direct relationship between external phosphorus loads and annual bloom severity reflects the complexity of the lake’s response to eutrophication and the potential roles of other drivers including climate and a strong legacy effect of sedimentary nutrients. A case study of the 2017 bloom season captures the compounding interaction of meteorological variability and seasonal nutrient delivery in regulating the bloom response. Results highlight the need for greater understanding of seasonal and regional variability of bloom drivers to aid in forecasting the lake’s recovery under both nutrient management and climate change scenarios.     

The impact of phytoplankton community composition on optical properties and satellite observations of the 2017 western Lake Erie algal bloom

Since the early 2000s Lake Erie has seen a dramatic increase in phytoplankton biomass, manifested in particular by the rise in the severity of cyanobacteria blooms and the prevalence of potentially toxic taxa such as Microcystis. Satellite remote sensing has provided a unique capacity for the synoptic detection of these blooms, enabling spatial and temporal trends in their extent and severity to be documented. Algorithms for satellite detection of Lake Erie algal blooms often rely on a single consistent relationship between algal or cyanobacterial biomass and spectral indices such as the Maximum Chlorophyll Index (MCI) or Cyanobacteria Index (CI). Blooms, however, are known to vary significantly in community composition over space and time. A suite of phytoplankton and optical property measurements during the western Lake Erie algal bloom of 2017 showed highly diverse bloom composition with variable absorption and backscatter properties. Elevated backscattering coefficients were observed in the Maumee Bay, likely due to phytoplankton cell morphology and buoyancy regulating gas vacuoles, compared with typically Planktothrix dominated blooms in Sandusky Bay. MCI and CI calibrated to historical chlorophyll observations and applied to Sentinel 3's OLCI sensor accurately captured the 2017 bloom in Maumee Bay but underestimated the Sandusky Bay bloom by nearly 80%. The phycoerythrin-rich picocyanobacteria Aphanothece and Synechococcus were found in abundance throughout the western and central basins, resulting in substantial biomass underestimations using blue to green ratio-based algorithms. Potential misrepresentation of bloom severity resulting from phytoplankton optical properties should be considered in assessments of bloom conditions on Lake Erie.     

Algal bloom transport in Lake Erie using remote sensing and hydrodynamic modelling: Sensitivity to buoyancy velocity and initial vertical distribution

In this study, we simulate three-dimensional transport of algal blooms in Lake Erie using a combination of remote sensing and hydrodynamic modelling. The remote sensing algorithms use data from the Sentinel-3 OLCI satellite sensor to derive chlorophyll-a concentration from cyanobacteria blooms in Lake Erie. The derived chlorophyll-a concentration initializes an algal bloom transport model driven by the lake component of the Water Cycle Prediction System for the Great Lakes, a system of coupled atmosphere-lake-hydrological models operated out of Environment and Climate Change Canada. The bloom is modelled as Microcystis aeruginosa, a buoyant species that is often dominant in harmful algal blooms in western Lake Erie. Short-term (a few days) predictions of algal bloom transport from July 27 to October 8, 2017 are modelled in both Eulerian and Lagrangian frameworks. The Eulerian framework is used to evaluate the sensitivity of model results to the initial vertical distribution of the bloom. In this work, the Lagrangian framework is limited to two-dimensional surface confined particles. We use several error metrics to evaluate model predictions. We find that results are sensitive to the buoyancy velocity for cases where the bloom was initially distributed over a large portion of the water column. An initial vertical distribution selected from modelled chlorophyll-a half depth shows the highest accuracy for the entire range of buoyancy velocities tested. We also find that the Pierce skill score is difficult to interpret, particularly in cases where bloom intensity is greatly overpredicted by the model.     

Carbon fixation by the phytoplankton community across Lake Winnipeg

Lakes are important sites for carbon fixation and carbon dioxide (CO₂) exchange with the atmosphere. Carbon fixation rates have not previously been published for Lake Winnipeg but are important for quantifying the lake’s role in the regional greenhouse gas budget and the lake’s trophic structure and fish habitat. This study measured net ecosystem production (NEP), gross primary production (GPP), and gross respiration (GR) across the lake using a custom-built automated incubator connected to a ship’s water intake during a research cruise between July 31 and August 17, 2018 on Lake Winnipeg. The incubator estimated NEP, GR, and GPP every 60 min while moving along the ship’s track and at anchor, providing high-resolution data that are not obtainable through conventional incubations. The mean NEP for Lake Winnipeg during our survey was ?8.4 ± 5.6 g C m^?2 d^?1, suggesting that the lake was net heterotrophic and thus a net CO₂ source to the atmosphere during the 2018 summer cruise. The high-resolution data revealed significant spatiotemporal variability, including short-lived, highly net productive events that preceded remotely sensed chlorophyll a blooms by several days. Conversely, in regions with high chlorophyll a concentrations, we observed strong net heterotrophy and low nutrients, suggesting respiration was fueled by the degradation of mature, nutrient-limited phytoplankton blooms. The incubator system used in this study demonstrated its utility for monitoring rapid changes in NEP over short spatial scales in a lake which shows heightened regional variability in its physical, biogeochemical, and biological make-up.     

Spatial and temporal variations in algal blooms in the coastal waters of the western South China Sea

Algal blooms occur frequently in the coastal waters of the western South China Sea (SCS). This paper reports spatial and temporal variations of algal bloom events in these waters from 1993 to 2007. Twenty-five algal bloom events occurred in summer in the coastal waters of South and Central Vietnam where they were associated with wind-induced, coastal, nutrient upwelling and river discharges; a further eight events occurred in the coastal waters of North Vietnam. A greater number of algal bloom events were observed in 1999 and 2002, and were accompanied by several previously unobserved species for the study period. These events may be related to the El Niño events of 1998 and 2002. Furthermore, the bloom-causative species Trichodesmium erythraeum (Cyanophyta) entirely dominated the phytoplankton community of algal blooms during 1993–1999 whereas the species Phaeocystis globosa (Haptophyta) dominated blooms after 2002. This study establishes a basis for further long-term research of algal bloom event variations, and provides a compiled scientific reference that may be used for later prediction of Harmful algal blooms (HABs).     

基于SMDPSO算法的呼伦湖藻华遥感监测

水体富营养化所引起的藻华爆发现象是我国面临的重大环境问题之一。以内蒙古呼伦湖为研究区,采用基于离散粒子群优化的光谱匹配（SMDPSO）算法提取藻华,以浮游藻类指数（FAI）的分类结果作为验证数据进行精度检验。然后分析2009-2018年藻华的时空变化特征,并将此算法应用于黄海。结果表明：SMDPSO算法可以有效地识别呼伦湖藻华,与FAI分类结果之间的R²为0.97,RMSE为0.22 km²;呼伦湖藻华爆发于7-8月,且主要出现在湖泊边缘;SMDPSO算法既可以较好地识别以蓝藻为优势门的呼伦湖藻华,也可以提取黄海的浒苔（绿藻）;SMDPSO算法不仅保留了光谱指数法精度高的特点,而且它还具有成本低、参数少、无需人工干预的优势。该研究为藻华遥感监测提供了新的工具,有助于控制湖泊水体富营养化和改善水生态环境。     

Agricultural conservation practices could help offset climate change impacts on cyanobacterial harmful algal blooms in Lake Erie

Harmful algal blooms (HABs) are a recurring problem in many temperate large lake and coastal marine ecosystems, caused mainly by anthropogenic eutrophication. Implementation of agricultural conservation practices (ACPs) offers a means to reduce non-point source nutrient runoff and mitigate HABs. However, the effectiveness of ACPs in a changing climate remains uncertain. We used an integrated biophysical modeling approach to predict how Lake Erie cyanobacterial HAB severity (bloom biomass) may change under several climate and ACP implementation scenarios, using western Lake Erie and its largely agricultural watershed as our study system. An ensemble of general circulation model projections was used to drive spatially explicit land use and hydrology models of the Maumee River watershed, the output of which informed a predictive model of Lake Erie HAB severity. Results show that, in the absence of changes in ACPs, the frequency of severe HABs is projected to increase during coming decades, owing to increased inputs of nutrients from the watershed. These anticipated increases are due to increased total precipitation and more frequent higher-magnitude rainfall events. While further implementation of ACPs appears capable of reducing severe HAB events, widespread implementation would be necessary to reduce HAB severity below current management targets. This study highlights how continued climate change will only exacerbate the need for land management practices that can reduce nutrient runoff in agriculturally dominated ecosystems, such as Lake Erie. It also shows how interdisciplinary, biophysical modeling approaches can help identify strategies to mitigate HABs in the face of anthropogenic stressors.     

Determining remote sensing spatial resolution requirements for the monitoring of harmful algal blooms in the Great Lakes

Harmful algal blooms (HABs) have become a major health and environmental concern in the Great Lakes. In 2014, severe HABs prompted the State of Ohio to request NASA Glenn Research Center (GRC) to assist with monitoring algal blooms in Lake Erie. The most notable species of HAB is Microcystis aeruginosa, a hepatotoxin producing cyanobacteria that is responsible for liver complications for humans and other fauna that come in contact with these blooms. NASA GRC conducts semiweekly flights in order to gather up-to-date imagery regarding the blooms' spatial extents and concentrations. Airborne hyperspectral imagery is collected using two hyperspectral imagers, HSI-2 and HSI-3. Hyperspectral imagery is necessary in order to conduct experiments on differentiation of algal bloom types based on their spectral reflectance. In this analysis, imagery from September 19, 2016 was utilized to study the subpixel variability within the footprint of arbitrary sized pixels using several analysis techniques. This particular data set is utilized because it represents a worst case scenario where there is significant potential for public health concern due to high concentrations of microcystin toxin found in the water on this day and the concurrent observational challenges to accurately measure the algal bloom concentration variability with a remote sensing system due to the blooms high spatial variability. It has been determined that the optimal spatial resolution to monitor algal blooms in the Great Lakes is at most 50 m, and for much lower error 25 m, thus allowing for greater ease in identifying high concentration blooms near the surface. This resolution provides the best sensitivity to high concentration areas that are of significant importance in regard to human health and ecological damage.     

Application of a three-dimensional ecological model to develop nutrient management plans for Lake of the Woods

This paper presents the development of a three-dimensional hydrodynamic-ecological model for Lake of the Woods (LoW) to assess the impact of nutrient inputs on the lake’s ecological processes. LoW is a large bi-national water body with complex geometry and topography and receives significant nutrients mainly from the Rainy River, with additional inputs from a few other smaller tributaries, and suffers from degraded water quality with seasonal cyanobacterial and harmful algal blooms. A high-resolution model developed here has a horizontal grid resolution of 250 m with a variable vertical grid resolution and can simulate hydrodynamics, in-lake nutrients dynamics, and phytoplankton biomass. Our model reproduced observed temporal and spatial distribution of nutrient and chlorophyll-a concentrations reasonably well. The calibrated model is used to explore simulating the spatial and temporal variability of ecological conditions of the lake and its response to nutrient load reductions. Based on a range of potential nutrient loadings, the model results suggest that different areas within LoW may respond differently to decreased phosphorus loadings. The model simulations predict that as nutrient loads into LoW decrease, water quality conditions will improve in most of the segments. In addition to naturally reducing internal load, external load reductions of 160 MTA from the baseline conditions (877 MTA) are necessary to reduce late summer average TP concentrations to 0.03 mg/L and total chlorophyll-a concentrations in the range of 7–12 μg/L.     

Patterns in the crustacean zooplankton community in Lake Winnipeg,Manitoba: Response to long-term environmental change

Changes in the crustacean zooplankton community composition and abundance in Lake Winnipeg (1969–2006) provide a rare opportunity to examine their response to environmental changes in the largest naturally eutrophic lake on the Canadian prairies. Since 1929, zooplankton species composition in Lake Winnipeg has changed little except for the addition of the invasive cladoceran, Eubosmina coregoni in 1994. The dominant taxa in the lake in summer include: Leptodiaptomus ashlandi, Acanthocyclops vernalis, Diacyclops thomasi, Daphnia retrocurva, Daphnia mendotae, Diaphanosoma birgei, Eubosmina coregoni, and Bosmina longirostris. Climate-accelerated nutrient loading to southern Lake Winnipeg over the last two decades has led to increased phytoplankton abundance and higher frequency of cyanobacterial blooms especially in its northern basin. Crustacean zooplankton have likewise increased especially in the North Basin, but less so in the more nutrient rich South Basin, possibly as a consequence of higher densities of pelagic planktivorous fish and light-limited primary production compared with the more transparent North basin (Brunskill et al., 1979, 1980). Calanoid copepods play a larger role in the South basin food web in contrast to cyclopoid copepods and Cladocera in the North basin. The study begins to fill the recognized gap in understanding of Lake Winnipeg's food web structure and provides a baseline for evaluating ongoing changes in the zooplankton community with the arrival of new non-indigenous taxa, e.g. Bythotrephes longimanus and Dreissena polymorpha. It reinforces previous work demonstrating that zooplankton provide valuable indices toward evaluating the health of an ecosystem.     

Dolichospermum blooms in Lake Superior: DNA-based approach provides insight to the past,present and future of blooms

Cyanobacterial blooms are increasing in frequency, duration, and severity globally in freshwater ecosystems. The Laurentian Great Lakes are prone to toxin-producing cyanobacterial blooms and have experienced annually recurring blooms. Because of its oligotrophic nature, Lake Superior has been relatively free of bloom occurrences. However, in recent years, Dolichospermum blooms have occurred with increasing frequency, especially in the western arm. During a Dolichospermum bloom in 2018, opportunistic samples were collected from the offshore bloom and investigated with shotgun metagenomics. We identified a near-complete Dolichospermum genome that is highly similar to genomes from cultures recovered in Lakes Erie and Ontario. The genomes from the Laurentian Great Lakes are typified by their putative ability to produce a suite of secondary metabolites like anabaenopeptin, but not toxins like microcystin. Additionally, we recovered a Dolichospermum lemmermannii 16S rRNA gene from the bloom and using datasets collected from the epilimnion and sediments in Lake Superior show this organism is ubiquitous and that several strains may exist. While there is much to learn about Lake Superior cyanobacterial bloom development and triggers, understanding this organism is endemic to the region, what its genome is capable of and that specific strains may have provenance within the lake provides a distinct ecological basis for understanding and working towards a predictive framework for future blooms.     

SeaPRISM observations in the western basin of Lake Erie in the summer of 2016

In the summer of 2016, a robotic sun photometer called the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) Photometer Revision for Incident Surface Measurements (SeaPRISM), was deployed at a Coast Guard channel marker in western Lake Erie, measuring atmospheric properties and spectral water-leaving radiance. The instrument was deployed by the National Oceanic and Atmospheric Administration (NOAA) to support remote sensing validation and harmful algal bloom (HAB) satellite products. The Lake Erie SeaPRISM is also part of the international federated AERONET program maintained by the National Aeronautics and Space Administration (NASA), and more specifically is part of the AERONET Ocean Color (AERNOET-OC) network. The main purpose of this component of AERONET is specific to calibration/validation efforts for ocean color. The AERONET-OC network currently consists of 23 field radiometers at aquatic sites around the world. The Lake Erie site is the second freshwater lake location world-wide after the Palgrunden site in Sweden. During its operating period from mid-July to early September 2016, various environmental conditions were observed including a cyanobacteria bloom. Water-leaving radiance observations were generated on 43 out of 51 days, and varied by a factor of five. The variability in the above-water radiometry tracked that of in-water measurements made by a nearby buoy. During this brief operating window, satellite matchups were generated for several satellites. We highlight the first year's observations in relation to remote sensing validation and report on observations of cyanobacteria blooms from hourly to weekly time scales.     

Status,causes and controls of cyanobacterial blooms in Lake Erie

The Laurentian Great Lakes are among the most prominent sources of fresh water in the world. Lake Erie's infamous cyanobacterial blooms have, however, threatened the health of this valuable freshwater resource for decades. Toxic blooms dominated by the cyanobacterium Microcystis aeruginosa have most recently been one of primary ecological concerns for the lake. These toxic blooms impact the availability of potable water, as well as public health and revenues from the tourism and fishery industries. The socioeconomic effects of these blooms have spurred research efforts to pinpoint factors that drive bloom events. Despite decades of research and mitigation efforts, these blooms have expanded both in size and duration in recent years. However, through continued joint efforts between the Canadian and United States governments, scientists, and environmental managers, identification of the factors that drive bloom events is within reach. This review provides a summary of historical and contemporary research efforts in the realm of Lake Erie's harmful cyanobacterial blooms, both in terms of experimental and management achievements and insufficiencies, as well as future directions on the horizon for the lake's research community.     

Influence of episodic wind events on thermal stratification and bottom water hypoxia in a Great Lakes estuary

Hypoxia formation and breakdown were tracked during 2015 in Muskegon Lake estuary at multiple locations, and five years (2011–2015) of time-series buoy observatory data were evaluated for the effect of episodic wind-events on lake mixing. Bi-weekly water temperature and dissolved oxygen (DO) profiles at four locations revealed that hypoxia occurred at all sites and persisted for 2–3 months during summer 2015. On one date in late-summer, up to 24% of the lake’s volume was estimated to be mildly hypoxic (DO < 4 mg L^?1) as defined by lake sturgeon requirements. Patterns of wind speed and water column stability in late spring indicated that high winds and low stability delayed the onset of hypoxia while in late summer low winds and high stability delayed degradation of hypoxia. Wind speeds appear to play a great role in the interannual variability of stratification and subsequent hypoxia. Water temperature and DO profiles taken before and after one mid-summer mixing event (wind speed >7.7 m s^?1 for 10 h), indicated that while the wind was unable to completely mix the entire water column, it deepened the epilimnion by ～1.5 m and sheared a thin layer from the upper hypolimnion. By entraining internally loaded nutrients, such episodic wind-events may initiate and sustain algal blooms in nutrient limited surface waters. Quantifying the variable role of wind and mixing events will be key to integrating limnological processes into climate models of the future.     

Hydrodynamics of a large lake with complex geometry and topography: Lake of the Woods

We developed a high-resolution (250 m) three-dimensional hydrodynamic model of Lake of the Woods (LoW) to study surface seiches, lake circulation, and water temperature. LoW spans over two different geological regions, experiences wind variability, encompasses >14,500 islands and includes twelve hydraulically connected sub-basins. The complex geometry and topography are unique for this large lake that suffers from seasonal algal blooms. The model uses a spatially and temporally variable wind field, heat fluxes, riverine inflows obtained from a watershed model, outflows at Kenora and Norman dams, and was run for the spring-summer of 2017 and 2018. The predominant physical processes in LoW are surface seiches, and wind and rivers drive mean lake-wide circulations and mixing. The observed periods of seiches in the lake are 9.5, 3.4, 2.3, and 1.3 h, attributed to horizontal modes 1, 3, 6, and 12, respectively. Amphidromic structures show that sub-basin seiche's responses to wind may be limited to one sub-basin; however, lake-wide responses were also observed, which suggests the seiche-induced oscillatory velocities can impact transport paths. The mean water temperature is variable spatially, ranging from <15 °C in early June to ～25 °C in July-August. Sabaskong Bay experienced warmer summer water temperatures with unidirectional mean flows to the lake. Main water transport is towards the northeast. Modelled conservative tracers, consistent with observations, show that the Rainy River plume extent covers Big Traverse, Morson, and Little Traverse Bays. The modelled seasonal hydraulic retention times in southern bays are <150 days and are much longer in northern bays.     

Foreword to the second Lake Winnipeg special issue

Lake Winnipeg is the 10th largest freshwater lake in the world and, like many of the world’s great lakes, it is increasingly being affected by anthropogenic pressures, such as high nutrient loads and invasive species. The consequences of these on the hydrology and ecology of the lake are the focus of continuing research, funded by the renewed investment of Federal and Provincial agencies. Complicating this, Lake Winnipeg is still very much a wilderness lake and despite two decades of research there is much we don’t know. This second special issue on Lake Winnipeg is a collection of 21 research articles that describe some of the most recent and emerging research on the lake.     

洪泽湖浮游藻类变化动态及影响因素

针对2011年7月至2013年10月在洪泽湖11个采样点20次采样获得的湖水表层浮游藻类和水质理化指标数据,采用Shannon-Wiener指数H和优势度指数Y进行数据统计处理,分析洪泽湖浮游藻类时空分布动态及其影响要素。结果表明:洪泽湖浮游藻类共有7门60属144种,主要包括绿藻门66种、硅藻门36种、蓝藻门23种、裸藻门13种。浮游藻类群落具有明显的时空异质性,物种丰富度夏季最高,秋季中等,冬春季最低;西北部(成子湖区)和河流入湖口(溧河洼)高,湖心和河流出湖口(蒋坝)低;浮游藻类细胞密度全湖平均值变幅为157万~604万个/L,夏秋高,冬春--低;成子湖区等静水区高,入湖和出湖口低。浮游藻类组成和细胞密度的时间动态与温度、水位及营养盐(TN、TP等)的季节差异有关,而其空间动态与水动力因素和营养盐(NH3-N)的空间差异有关。建议限制洪泽湖营养盐陆地输入,合理调控洪泽湖水动力,以遏制洪泽湖蓝藻水华的发生。     

Simulation of harmful algal blooms using a deterministic Lagrangian particle separation-based method

Algal blooms (red tide) are often observed in Hong Kong's coastal waters. These algal blooms can cause discoloration of the marine water, and may result in severe dissolved oxygen depletion and fish kills; most harmful algal blooms (HAB) are caused by diatoms and dinoflagellates. Diatoms are non-motile algae relying on water turbulence for suspension and nutrient supply. Dinoflagellates, on the other hand, can undergo diel vertical migration. At night, the algal cells swim down the water column to uptake nutrient and store it as internal nutrient reserve (cell quota). During daytime, they ascend to the water surface to carry out photosynthesis using the nutrient reserve. Diel vertical migration is an important adaptive strategy of dinoflagellates to form blooms in stratified waters.In this paper, the vertical migration behaviour of dinoflagellates is modelled using a simple deterministic Lagrangian model based on a NEighbourhood Separation Technique (NEST). The method is based on relative diffusion concepts, and simulates the diffusion process via an equivalent macroscopic motion; it uses far less number of particles than that required in random walk methods. The Lagrangian cell quota based algal dynamics is incorporated in a one-dimensional model to predict the vertical structure of water quality. Dinoflagellates are represented by a number of particles, with algal growth dependent on its nutrient reserve and the available light intensity. Swimming behaviour is simulated by the corresponding advective translocation of the particle. The model is applied to study species competition, resulting in a simple bloom prediction criterion based on nutrient availability and vertical diffusivity. In addition, the changes in water quality during an observed dinoflagellate bloom in Hong Kong coastal waters are well supported by field data; the role of stratification and diel vertical migration on the bloom formation and the signature of dissolved oxygen are discussed.     

Extending the forecast model: Predicting Western Lake Erie harmful algal blooms at multiple spatial scales

Lake Erie is a classic case of development, recovery from, and return to eutrophication, hypoxia, and harmful algal blooms. Forecast models are used annually to predict bloom intensity for the whole Western Lake Erie Basin, but do not necessarily reflect nearshore conditions or regional variations, which are important for local stakeholders. In this study we: 1) developed relationships between observed whole basin and nearshore bloom sizes, and 2) updated and extended a Bayesian seasonal bloom forecast model to provide new regional predictions. The western basin was subdivided into 5 km near-shore regions, and bloom start date, size, and intensity were quantified with MODIS-derived images of chlorophyll concentrations for July–October 2002–2016 for each subdivision and for the entire basin. While bloom severity within each subdivision is temporally and spatially unique, it increased over the study period in each subdivision. The models for the 5 km subdivisions explained between 83 and 95% of variability between regional sizes and whole bloom size for US subdivisions and 51% for the Canadian subdivision. By linking predictive basin-wide models to regional regression estimates, we are now able to better predict potential bloom impacts at scales and in specific areas that are vital to the economic well-being of the region and allow for better management responses.