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.     

Spatial-temporal variability of in situ cyanobacteria vertical structure in Western Lake Erie: Implications for remote sensing observations

Remote sensing has provided expanded temporal and spatial range to the study of harmful algal blooms (cyanoHABs) in western Lake Erie, allowing for a greater understanding of bloom dynamics than is possible through in situ sampling. However, satellites are limited in their ability to specifically target cyanobacteria and can only observe the water within the first optical depth. This limits the ability of remote sensing to make conclusions about full water column cyanoHAB biomass if cyanobacteria are vertically stratified. FluoroProbe data were collected at nine stations across western Lake Erie in 2015 and 2016 and analyzed to characterize spatio-temporal variability in cyanobacteria vertical structure. Cyanobacteria were generally homogenously distributed during the growing season except under certain conditions. As water depth increased and high surface layer concentrations were observed, cyanobacteria were found to be more vertically stratified and the assumption of homogeneity was less supported. Cyanobacteria vertical distribution was related to wind speed and wave height, with increased stratification at low wind speeds (<4.9 m/s) and wave heights (<0.27 m). Once wind speed and wave height exceeded these thresholds the assumption of vertically uniform cyanobacteria populations was justified. These findings suggest that remote sensing can provide adequate estimates of water column cyanoHAB biomass in most conditions; however, the incorporation of bathymetry and environmental conditions could lead to improved biomass estimates. Additionally, cyanobacteria contributions to total chlorophyll-a were shown to change throughout the season and across depth, suggesting the need for remote sensing algorithms to specifically identify cyanobacteria.     

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.     

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.     

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.     

The MERIS Maximum Chlorophyll Index; its merits and limitations for inland water algal bloom monitoring

Satellite remote sensing methods adopting wavelengths in the red and near infra-red have been shown to be superior to the standard blue to green ratio based approaches in the detection of algal blooms under turbid, eutrophic conditions. Here, the MERIS Maximum Chlorophyll Index (MCI) has been explored as a tool for monitoring algal blooms in North America's inland waters where waters range from optically complex, turbid, eutrophic conditions, to low chlorophyll and oligotrophic conditions. Assessment of the MERIS MCI product is made for intense blooms of cyanobacteria in Lake of the Woods, algal blooms in turbid waters of Lake Erie, and low chlorophyll conditions in Lake Ontario. The MCI product is shown to be a versatile tool in monitoring intense surficial algal blooms with chlorophyll concentrations in the 10–300 mg m^? 3 range, while limited in its application to low-biomass conditions as observed in Lake Ontario. Wavelength shifts in the position of the MCI peak for different chlorophyll concentration ranges, as well as variations in the inherent optical properties of water colouring constituents, are anticipated to account for regional variations in MCI–chlorophyll relationships and potentially hinder a universally applicable quantitative MCI product.     

A novel method for tracking western Lake Erie Microcystis blooms, 2002–2011

After a period of improvement from the late 1970s through the mid 1990s, western Lake Erie has returned to eutrophic conditions and harmful algal blooms now dominated by the cyanobacterium Microcystis aeruginosa. The detection of long-term trends in Microcystis blooms would benefit from a convenient method for quantifying Microcystis using archived plankton tows. From 2002 to 2011, summer Microcystis blooms in western Lake Erie were quantified using plankton tows (N = 649). A flotation separation method was devised to quantify Microcystis biovolume in the tows, and the method was tested against whole water cell counts. Floating Microcystis biovolume (mL) in preserved tows was highly correlated with total Microcystis cells (R² = 0.84) and biomass (R² = 0.95) in whole water samples. We found that Microcystis annual biovolume was highly variable among years; the 2011 bloom was 2.4 times greater than the second largest bloom (2008) and 29.0 times greater than the smallest bloom (2002). Advantages of the method include use of archived samples, high sampling volume, and low effort and expense. Limitations include specificity for cyanobacterial blooms dominated by large Microcystis colonies and the need for site-specific validation. This study indicates that the flotation method can be used to rapidly assess past and present Microcystis in western Lake Erie and that there was high variability in the timing, duration, and intensity of the annual Microcystis blooms over a 10-year period. The data made possible by this method will aid further investigations into the underlying causal factors of blooms.     

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。     

Spatial and temporal variability of inherent and apparent optical properties in western Lake Erie: Implications for water quality remote sensing

Lake Erie has experienced dramatic changes in water quality over the past several decades requiring extensive monitoring to assess effectiveness of adaptive management strategies. Remote sensing offers a unique potential to provide synoptic monitoring at daily time scales complementing in-situ sampling activities occurring in Lake Erie. Bio-optical remote sensing algorithms require knowledge about the inherent optical properties (IOPs) of the water for parameterization to produce robust water quality products. This study reports new IOP and apparent optical property (AOP) datasets for western Lake Erie that encapsulate the May–October period for 2015 and 2016 at weekly sampling intervals. Previously reported IOP and AOP observations have been temporally limited and have not assessed statistical differences between IOPs over spatial and temporal gradients. The objective of this study is to assess trends in IOPs over variable spatial and temporal scales. Large spatio-temporal variability in IOPs was observed between 2015 and 2016 likely due to the difference in the extent and duration of mid-summer cyanobacteria blooms. Differences in the seasonal trends of the specific phytoplankton absorption coefficient between 2015 and 2016 suggest differing algal assemblages between the years. Other IOP variables, including chromophoric, dissolved organic matter (CDOM) and beam attenuation spectral slopes, suggest variability is influenced by river discharge and sediment re-suspension. The datasets presented in this study show how these IOPs and AOPs change over a season and between years, and are useful in advancing the applicability and robustness of remote sensing methods to retrieve water quality information in western Lake Erie.     

Fungal community dynamics associated with harmful cyanobacterial blooms in two Great Lakes

Harmful algal blooms (HABs) impose major costs on aquatic ecosystems worldwide, including the Laurentian Great Lakes. Microbial consumers, including fungi, can have important interactions with bloom-forming algae and cyanobacteria, although relatively few studies have investigated the relationship between fungi and HABs. We examined changes in the aquatic fungal community coincident with the occurrence of large cyanobacterial blooms in two areas of the Great Lakes (western Lake Erie and Saginaw Bay, Lake Huron). We collected water samples over the course of bloom development, peak, and decline from 3 sites in western Lake Erie on 11 dates and 2 sites in Saginaw Bay on 4 dates. Single molecule sequencing (PacBio RS II) with two molecular markers (the internal transcribed spacer (ITS) of the rRNA locus using fungal-specific primers and the 18S rRNA with primers targeting early-diverging lineages of fungi) was used to estimate fungal community composition. Results indicate a diverse fungal community within the lakes, including several major fungal phyla. The Chytridiomycota were particularly well-represented (54.8% and 45.4% of ITS and 18S sequences, respectively), and we also found representation from both Cryptomycota and Aphelidiomycota, which are putatively obligate intracellular parasites. Further, we found associations between the fungal community (alpha diversity; community composition) and measures of bloom magnitude (chlorophyll a, phycocyanin, and microcystin concentrations) in western Lake Erie. Our results suggest potentially important spatial and temporal heterogeneity in the fungal community that motivates further research on functional importance of fungi in the Great Lakes and consequences for HABs and freshwater ecosystems more broadly.     

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.     

基于人工神经网络的河湖蓝藻水华状态评价研究

面对依旧严峻的水环境问题,水资源的高效管理成为解决该问题的关键途径。其中,水华状态评价成为重中之重,本文针对河湖蓝藻水华状态评价过程中存在的非线性和不精确性问题,构建了基于人工神经网络的蓝藻水华状态评价模型,实现了固定站点监测和遥感监测信息的有效融合。并将该模型用于太湖蓝藻水华状态评价,研究表明:评价结果与实际情况相符,验证了模型的有效性和可行性,为蓝藻水华问题深入研究提供了思路。     

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.     

Remote Estimation of Nutrients for a Drinking Water Source Through Adaptive Modeling

Morse Reservoir, a major water supply for the Indianapolis metropolitan area, IN, USA, experiences nuisance cyanobacterial blooms due to agricultural and point source nutrient loadings. Hyperspectral remote sensing data from both in situ and airborne AISA measurements were applied to an adaptive model based on Genetic Algorithms-Partial Least Squares (GA-PLS) by relating the spectral signal with total nitrogen (TN) and phosphorus (TP) concentrations. Results indicate that GA-PLS relating in situ spectral reflectance to the nutrients yielded high coefficients of determination (TN: R ²?=?0.88; TP: R ²?=?0.91) between measured and estimated TN (RMSE?=?0.07 mg/L; Range: 0.6–1.88 mg/L), and TP (RMSE?=?0.017; Range: 0.023–0.314 mg/L). The GA-PLS model also yielded high performance with AISA imaging data, showing close correlation between measured and estimated values (TN: RMSE?=?0.11 mg/L; TP: RMSE?=?0.02 mg/L). An analysis of in situ data indicated that TN and TP were highly correlated with chlorophyll-a and suspended matter in the water column, setting a basis for remotely sensed estimates of TN and TP. Spatial correlation of TN, TP with chlorophyll-a and suspended matters further confirmed that remote quantification of nutrients for inland waters is based on the strong association of optically active constituents with nutrients. Based on these results, in situ and airborne hyperspectral remote sensors can provide both quantitative and qualitative information on the distribution and concentration of nutrients in Morse Reservoir. Our modeling approach combined with hyperspectral remote sensing is applicable to other productive waters, where algal blooms are triggered by nutrients.     

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.     

An analysis of satellite-derived chlorophyll and algal bloom indices on Lake Winnipeg

Lake Winnipeg has experienced dramatic increases in nutrient loading and phytoplankton biomass over the last few decades, accompanied by a marked shift in community composition towards the dominance of cyanobacteria. Comprehensive lake-wide observations of algal blooms are critical to assessing the lake's health status, its response to nutrient management practices, and an improved understanding of the processes driving blooms. We present an analysis of the spatial and temporal variability of algal blooms on Lake Winnipeg using satellite-derived chlorophyll and indices for algal bloom intensity, spatial extent, severity, and duration over the period of ESA's MERIS mission (2002–2011). Imagery documented extensive blooms covering as much as 93% of the lake surface. Bloom conditions were analysed in the context of in-lake and watershed processes to gain further insight on the drivers of bloom events. Day to day bloom variability was driven primarily by intermittent wind mixing events, with quiescent periods leading to the formation of dense surface blooms. Seasonal bloom distribution was consistent with light limitation in the south basin and lake circulation transporting bloom material towards the north-east shore. Inter-annual variability in average bloom severity was related to both total phosphorus (TP) loadings and summer lake surface temperatures. Results provide a valuable historical time series of bloom conditions to which ongoing observations from Sentinel-3's OLCI sensor can be added for longer term monitoring and change detection.     

HJ-1B和Landsat卫星蓝藻水华监测能力评估——以洱海为例

蓝藻水华是富营养化湖泊共同面临的问题。遥感技术为快速、大范围水华监测提供了可能,选取遥感数据应首先明确不同卫星的水华监测能力。以洱海为例,对比分析HJ-1B和Landsat卫星在内陆中小湖泊水华监测的时间和空间监测能力,评价两者在水华监测中的适用性及优势。结果表明:两者均能有效识别水华,提取水华分布细节信息,相比MODIS更适合用于中小湖泊水华监测;进一步分析表明,综合两者数据监测蓝藻水华,可以更加客观统计水华时间特征,描述水华空间分布发展规律,对于其它中小湖泊利用遥感手段辅助水华监测具有参考意义。     

A Semi-Lagrangian Study of Circulation and Transport in Lake Ontario

A particle tracking model (PTM) is linked with a hydrodynamic model to evaluate mean seasonal circulation patterns in Lake Ontario, and also to provide a basis for predicting movement of algal blooms. The PTM is based on a random walk algorithm that combines a deterministic advective component with a stochastic component associated with the turbulent diffusivity field to calculate trajectories of neutrally buoyant particles, where both the advective and diffusive velocities are obtained from the hydrodynamic model. Mean circulation is calculated using 30-year average meteorological forcing data collected from five stations around the lake. Seasonal variations in lake circulation are demonstrated, and a clockwise flow in the eastern basin during summer and early fall is identified, contrary to some previous observations that suggest counterclockwise flow. The impacts of Niagara and St. Lawrence river flows on general lake circulation are found to be small, except within approximately 10 km of the river mouth. Development and application of the PTM demonstrate its potential to provide calculations of (Lagrangian) movements as determined from the hydrodynamic output, and to serve as a first step toward development of an algal transport model. Particle tracking helps to visualize flow patterns and provides a means of evaluating the probability a bloom will reach a specified area, given an initial position and the predicted velocity and diffusivity fields. This capability, when set up for real-time applications, can provide an important tool to support management decisions that may be needed when a bloom is observed, for example in predicting potential impacts of the bloom on a beach or a water intake.