Recent changes in cyanobacteria algal bloom magnitude in large lakes across the contiguous United States.

Cyanobacterial blooms in inland lakes produce large quantities of biomass that impact drinking water systems, recreation, and tourism and may produce toxins that can adversely affect public health. This study analyzed nine years of satellite-derived bloom records and compared how the bloom magnitude has changed from 2008-2011 to 2016-2020 in 1881 of the largest lakes across the contiguous United States (CONUS). We determined bloom magnitude each year as the spatio-temporal mean cyanobacteria biomass from May to October and in concentrations of chlorophyll-a. We found that bloom magnitude decreased in 465 (25 %) lakes in the 2016-2020 period. Conversely, there was an increase in bloom magnitude in only 81 lakes (4 %). Bloom magnitude either didn't change, or the observed change was in the uncertainty range in the majority of the lakes (n = 1335, 71 %). Above-normal wetness and normal or below-normal maximum temperature over the warm season may have caused the decrease in bloom magnitude in the eastern part of the CONUS in recent years. On the other hand, a hotter and dryer warm season in the western CONUS may have created an environment for increased algal biomass. While more lakes saw a decrease in bloom magnitude, the pattern was not monotonic over the CONUS. The variations in temporal changes in bloom magnitude within and across climatic regions depend on the interactions between land use land cover (LULC) and physical factors such as temperature and precipitation. Despite expectations suggested by recent global studies, bloom magnitude has not increased in larger US lakes over this time period.

Confronting turbidity, the major challenge for satellite-derived coastal bathymetry.

Monitoring the complex seafloor morphology that drives the functioning of shallow coastal ecosystems is vital for assessing marine activities. Satellite-derived bathymetry (SDB) can provide a crucial dataset for creating the bathymetry maps needed to understand hazards and impacts produced by climate change in vulnerable coastal zones. SDB is effective in clear water, but still has limitations in application to areas with some turbidity. Here, using the twin satellites Sentinel-2A/B, we integrate water quality information from the satellite with a multi-temporal compositing method to demonstrate a potential for comprehensively operational bathymetric mapping over a range of environments. The automated compositing method diminishes the turbidity impact in addition to inferring the maximum detectable depth and removing optically deep-water areas. Examining a wide range of conditions along the Caribbean and eastern coast of the U.S. shows detailed bathymetry as deep as 30 m at 10 m spatial resolution with median errors <1 m when compared to high-resolution lidar surveys. These results demonstrate that the model adopted can provide useful bathymetry in areas that do not have consistently clear water and can be extended across multiple geographic regions and optical conditions at local, regional, and national scales.

Satellites quantify the spatial extent of cyanobacterial blooms across the United States at multiple scales.

Previous studies indicate that cyanobacterial harmful algal bloom (cyanoHAB) frequency, extent, and magnitude have increased globally over the past few decades. However, little quantitative capability is available to assess these metrics of cyanoHABs across broad geographic scales and at regular intervals. Here, the spatial extent was quantified from a cyanobacteria algorithm applied to two European Space Agency satellite platforms-the MEdium Resolution Imaging Spectrometer (MERIS) onboard Envisat and the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3. CyanoHAB spatial extent was defined for each geographic area as the percentage of valid satellite pixels that exhibited cyanobacteria above the detection limit of the satellite sensor. This study quantified cyanoHAB spatial extent for over 2,000 large lakes and reservoirs across the contiguous United States (CONUS) during two time periods: 2008-2011 via MERIS and 2017-2020 via OLCI when cloud-, ice-, and snow-free imagery was available. Approximately 56% of resolvable lakes were glaciated, 13% were headwater, isolated, or terminal lakes, and the rest were primarily drainage lakes. Results were summarized at national-, regional-, state-, and lake-scales, where regions were defined as nine climate regions which represent climatically consistent states. As measured by satellite, changes in national cyanoHAB extent did have a strong increase of 6.9% from 2017 to 2020 (|Kendall's tau (τ)| = 0.56; gamma (γ) = 2.87 years), but had negligible change (|τ| = 0.03) from 2008 to 2011. Two of the nine regions had moderate (0.3 ≤ |τ| < 0.5) increases in spatial extent from 2017 to 2020, and eight of nine regions had negligible (|τ| < 0.2) change from 2008 to 2011. Twelve states had a strong or moderate increase from 2017 to 2020 (|τ| ≥ 0.3), while only one state had a moderate increase and two states had a moderate decrease from 2008 to 2011. A decrease, or no change, in cyanoHAB spatial extent did not indicate a lack of issues related to cyanoHABs. Sensitivity results of randomly omitted daily CONUS scenes confirm that even with reduced data availability during a short four-year temporal assessment, the direction and strength of the changes in spatial extent remained consistent. We present the first set of national maps of lake cyanoHAB spatial extent across CONUS and demonstrate an approach for quantifying past and future changes at multiple spatial scales. Results presented here provide water quality managers information regarding current cyanoHAB spatial extent and quantify rates of change.

Quantifying Karenia brevis bloom severity and respiratory irritation impact along the shoreline of Southwest Florida.

Nearly all annual blooms of the toxic dinoflagellate Karenia brevis (K. brevis) pose a serious threat to coastal Southwest Florida. These blooms discolor water, kill fish and marine mammals, contaminate shellfish, cause mild to severe respiratory irritation, and discourage tourism and recreational activities, leading to significant health and economic impacts in affected communities. Despite these issues, we still lack standard measures suitable for assessing bloom severity or for evaluating the efficacy of modeling efforts simulating bloom initiation and intensity. In this study, historical cell count observations along the southwest Florida shoreline from 1953 to 2019 were used to develop monthly and annual bloom severity indices (BSI). Similarly, respiratory irritation observations routinely reported in Sarasota and Manatee Counties from 2006 to 2019 were used to construct a respiratory irritation index (RI). Both BSI and RI consider spatial extent and temporal evolution of the bloom, and can be updated routinely and used as objective criteria to aid future socioeconomic and scientific studies of K. brevis. These indices can also be used to help managers and decision makers both evaluate the risks along the coast during events and design systems to better respond to and mitigate bloom impacts. Before 1995, sampling was done largely in response to reports of discolored water, fish kills, or respiratory irritation. During this timeframe, lack of sampling during the fall, when blooms typically occur, generally coincided with periods of more frequent-than-usual offshore winds. Consequently, some blooms may have been undetected or under-sampled. As a result, the BSIs before 1995 were likely underestimated and cannot be viewed as accurately as those after 1995. Anomalies in the frequency of onshore wind can also largely account for the discrepancies between BSI and RI during the period from 2006 to 2019. These findings highlighted the importance of onshore wind anomalies when predicting respiratory irritation impacts along beaches.

The Lake Erie HABs Grab: A binational collaboration to characterize the western basin cyanobacterial harmful algal blooms at an unprecedented high-resolution spatial scale.

Monitoring of cyanobacterial bloom biomass in large lakes at high resolution is made possible by remote sensing. However, monitoring cyanobacterial toxins is only feasible with grab samples, which, with only sporadic sampling, results in uncertainties in the spatial distribution of toxins. To address this issue, we conducted two intensive "HABs Grabs" of microcystin (MC)-producing Microcystis blooms in the western basin of Lake Erie. These were one-day sampling events during August of 2018 and 2019 in which 100 and 172 grab samples were collected, respectively, within a six-hour window covering up to 2,270 km2 and analyzed using consistent methods to estimate the total mass of MC. The samples were analyzed for 57 parameters, including toxins, nutrients, chlorophyll, and genomics. There were an estimated 11,513 kg and 30,691 kg of MCs in the western basin during the 2018 and 2019 HABs Grabs, respectively. The bloom boundary poses substantial issues for spatial assessments because MC concentration varied by nearly two orders of magnitude over very short distances. The MC to chlorophyll ratio (MC:chl) varied by a factor up to 5.3 throughout the basin, which creates challenges for using MC:chl to predict MC concentrations. Many of the biomass metrics strongly correlated (r > 0.70) with each other except chlorophyll fluorescence and phycocyanin concentration. While MC and chlorophyll correlated well with total phosphorus and nitrogen concentrations, MC:chl correlated with dissolved inorganic nitrogen. More frequent MC data collection can overcome these issues, and models need to account for the MC:chl spatial heterogeneity when forecasting MCs.

Assessing cyanobacterial frequency and abundance at surface waters near drinking water intakes across the United States.

This study presents the first large-scale assessment of cyanobacterial frequency and abundance of surface water near drinking water intakes across the United States. Public water systems serve drinking water to nearly 90% of the United States population. Cyanobacteria and their toxins may degrade the quality of finished drinking water and can lead to negative health consequences. Satellite imagery can serve as a cost-effective and consistent monitoring technique for surface cyanobacterial blooms in source waters and can provide drinking water treatment operators information for managing their systems. This study uses satellite imagery from the European Space Agency's Ocean and Land Colour Instrument (OLCI) spanning June 2016 through April 2020. At 300-m spatial resolution, OLCI imagery can be used to monitor cyanobacteria in 685 drinking water sources across 285 lakes in 44 states, referred to here as resolvable drinking water sources. First, a subset of satellite data was compared to a subset of responses (n = 84) submitted as part of the U.S. Environmental Protection Agency's fourth Unregulated Contaminant Monitoring Rule (UCMR 4). These UCMR 4 qualitative responses included visual observations of algal bloom presence and absence near drinking water intakes from March 2018 through November 2019. Overall agreement between satellite imagery and UCMR 4 qualitative responses was 94% with a Kappa coefficient of 0.70. Next, temporal frequency of cyanobacterial blooms at all resolvable drinking water sources was assessed. In 2019, bloom frequency averaged 2% and peaked at 100%, where 100% indicated a bloom was always present at the source waters when satellite imagery was available. Monthly cyanobacterial abundances were used to assess short-term trends across all resolvable drinking water sources and effect size was computed to provide insight on the number of years of data that must be obtained to increase confidence in an observed change. Generally, 2016 through 2020 was an insufficient time period for confidently observing changes at these source waters; on average, a decade of satellite imagery would be required for observed environmental trends to outweigh variability in the data. However, five source waters did demonstrate a sustained short-term trend, with one increasing in cyanobacterial abundance from June 2016 to April 2020 and four decreasing.

Cyanobacterial bloom phenology in Saginaw Bay from MODIS and a comparative look with western Lake Erie.

Saginaw Bay and western Lake Erie basin (WLEB) are eutrophic catchments in the Laurentian Great Lakes that experience annual, summer-time cyanobacterial blooms. Both basins share many features including similar size, shallow depths, and equivalent-sized watersheds. They are geographically close and both basins derive a preponderance of their nutrient supply from a single river. Despite these similarities, the bloom phenology in each basin is quite different. The blooms in Saginaw Bay occur at the same time and place and at the same moderate severity level each year. The WLEB, in contrast, exhibits far greater interannual variability in the timing, location, and severity of the bloom than Saginaw Bay, consistent with greater and more variable phosphorus inputs. Saginaw Bay has bloom biomass that corresponds to relatively mild blooms in WLEB, and also has equivalent phosphorus loads. This result suggests that if inputs of P into the WLEB were reduced to similarly sized loads as Saginaw Bay the most severe blooms would be abated. Above 500 t P input, which occur in WLEB, blooms increase non-linearly indicating any reduction in P-input at the highest inputs levels currently occurring in the WLEB, would yield disproportionately large reductions in cyanobacterial bloom intensity. As the maximum phosphorus loads in Saginaw Bay lie just below this inflection point, shifts in the Saginaw Bay watershed toward greater agriculture uses and less wetlands may substantially increase the risk of more intense cyanobacterial blooms than presently occur.

Evaluation of a satellite-based cyanobacteria bloom detection algorithm using field-measured microcystin data.

Widespread occurrence of cyanobacterial harmful algal blooms (CyanoHABs) and the associated health effects from potential cyanotoxin exposure has led to a need for systematic and frequent screening and monitoring of lakes that are used as recreational and drinking water sources. Remote sensing-based methods are often used for synoptic and frequent monitoring of CyanoHABs. In this study, one such algorithm - a sub-component of the Cyanobacteria Index called the CIcyano, was validated for effectiveness in identifying lakes with toxin-producing blooms in 11 states across the contiguous United States over 11 bloom seasons (2005-2011, 2016-2019). A matchup data set was created using satellite data from MEdium Resolution Imaging Spectrometer (MERIS) and Ocean Land Colour Imager (OLCI), and nearshore, field-measured Microcystins (MCs) data as a proxy of CyanoHAB presence. While the satellite sensors cannot detect toxins, MCs are used as the indicator of health risk, and as a confirmation of cyanoHAB presence. MCs are also the most common laboratory measurement made by managers during CyanoHABs. Algorithm performance was evaluated by its ability to detect CyanoHAB 'Presence' or 'Absence', where the bloom is confirmed by the presence of the MCs. With same-day matchups, the overall accuracy of CyanoHAB detection was found to be 84% with precision and recall of 87 and 90% for bloom detection. Overall accuracy was expected to be between 77% and 87% (95% confidence) based on a bootstrapping simulation. These findings demonstrate that CIcyano has utility for synoptic and routine monitoring of potentially toxic cyanoHABs in lakes across the United States.

Satellite remote sensing to assess cyanobacterial bloom frequency across the United States at multiple spatial scales.

Cyanobacterial blooms can have negative effects on human health and local ecosystems. Field monitoring of cyanobacterial blooms can be costly, but satellite remote sensing has shown utility for more efficient spatial and temporal monitoring across the United States. Here, satellite imagery was used to assess the annual frequency of surface cyanobacterial blooms, defined for each satellite pixel as the percentage of images for that pixel throughout the year exhibiting detectable cyanobacteria. Cyanobacterial frequency was assessed across 2,196 large lakes in 46 states across the continental United States (CONUS) using imagery from the European Space Agency's Ocean and Land Colour Instrument for the years 2017 through 2019. In 2019, across all satellite pixels considered, annual bloom frequency had a median value of 4% and a maximum value of 100%, the latter indicating that for those satellite pixels, a cyanobacterial bloom was detected by the satellite sensor for every satellite image considered. In addition to annual pixel-scale cyanobacterial frequency, results were summarized at the lake- and state-scales by averaging annual pixel-scale results across each lake and state. For 2019, average annual lake-scale frequencies also had a maximum value of 100%, and Oregon and Ohio had the highest average annual state-scale frequencies at 65% and 52%. Pixel-scale frequency results can assist in identifying portions of a lake that are more prone to cyanobacterial blooms, while lake- and state-scale frequency results can assist in the prioritization of sampling resources and mitigation efforts. Satellite imagery is limited by the presence of snow and ice, as imagery collected in these conditions are quality flagged and discarded. Thus, annual bloom frequencies within nine climate regions were investigated to determine whether missing data biased results in climate regions more prone to snow and ice, given that their annual summaries would be weighted toward the summer months when cyanobacterial blooms tend to occur. Results were unbiased by the time period selected in most climate regions, but a large bias was observed for the Northwest Rockies and Plains climate region. Moderate biases were observed for the Ohio Valley and the Southeast climate regions. Finally, a clustering analysis was used to identify areas of high and low cyanobacterial frequency across CONUS based on average annual lake-scale cyanobacterial frequencies for 2019. Several clusters were identified that transcended state, watershed, and eco-regional boundaries. Combined with additional data, results from the clustering analysis may offer insight regarding large-scale drivers of cyanobacterial blooms.

Satellites for long-term monitoring of inland U.S. lakes: The MERIS time series and application for chlorophyll-a.

Lakes and other surface fresh waterbodies provide drinking water, recreational and economic opportunities, food, and other critical support for humans, aquatic life, and ecosystem health. Lakes are also productive ecosystems that provide habitats and influence global cycles. Chlorophyll concentration provides a common metric of water quality, and is frequently used as a proxy for lake trophic state. Here, we document the generation and distribution of the complete MEdium Resolution Imaging Spectrometer (MERIS; Appendix A provides a complete list of abbreviations) radiometric time series for over 2300 satellite resolvable inland bodies of water across the contiguous United States (CONUS) and more than 5,000 in Alaska. This contribution greatly increases the ease of use of satellite remote sensing data for inland water quality monitoring, as well as highlights new horizons in inland water remote sensing algorithm development. We evaluate the performance of satellite remote sensing Cyanobacteria Index (CI)-based chlorophyll algorithms, the retrievals for which provide surrogate estimates of phytoplankton concentrations in cyanobacteria dominated lakes. Our analysis quantifies the algorithms' abilities to assess lake trophic state across the CONUS. As a case study, we apply a bootstrapping approach to derive a new CI-to-chlorophyll relationship, ChlBS, which performs relatively well with a multiplicative bias of 1.11 (11%) and mean absolute error of 1.60 (60%). While the primary contribution of this work is the distribution of the MERIS radiometric timeseries, we provide this case study as a roadmap for future stakeholders' algorithm development activities, as well as a tool to assess the strengths and weaknesses of applying a single algorithm across CONUS.

Less Agricultural Phosphorus Applied in 2019 Led to Less Dissolved Phosphorus Transported to Lake Erie.

Extreme precipitation events affect water quantity and quality in various regions of the world. Heavy precipitation in 2019 resulted in a record high area of unplanted agricultural fields in the U.S. and especially in the Maumee River Watershed (MRW). March-July phosphorus (P) loads from the MRW drive harmful algal bloom (HAB) severity in Lake Erie; hence changes in management that influence P export can ultimately affect HAB severity. In this study, we found that the 2019 dissolved reactive P (DRP) load from March-July was 29% lower than predicted, while the particulate P (PP) load was similar to the predicted value. Furthermore, the reduced DRP load resulted in a less severe HAB than predicted based on discharge volume. The 29% reduction in DRP loss in the MRW occurred with a 62% reduction in applied P, emphasizing the strong influence of recently applied P and subsequent incidental P losses on watershed P loading. Other possible contributing factors to this reduced load include lower precipitation intensity, altered tillage practices, and effects of fallow soils, but more data is needed to assess their importance. We recommend conservation practices focusing on P application techniques and timing and improving resiliency against extreme precipitation events.

Dynamics of an intense Alexandrium catenella red tide in the Gulf of Maine: satellite observations and numerical modeling.

In July 2009, an unusually intense bloom of the toxic dinoflagellate Alexandrium catenella occurred in the Gulf of Maine. The bloom reached high concentrations (from hundreds of thousands to one million cells L-1) that discolored the water and exceeded normal bloom concentrations by a factor of 1000. Using Medium Resolution Imaging Spectrometer (MERIS) imagery processed to target chlorophyll concentrations (>2 µg L-1), patches of intense A. catenella concentration were identified that were consistent with the highly localized cell concentrations observed from ship surveys. The bloom patches were generally aligned with the edge of coastal waters with high-absorption. Dense bloom patches moved onshore in response to a downwelling event, persisted for approximately one week, then dispersed rapidly over a few days and did not reappear. Coupled physical-biological model simulations showed that wind forcing was an important factor in transporting cells onshore. Upward swimming behavior facilitated the horizontal cell aggregation, increasing the simulated maximum depth-integrated cell concentration by up to a factor of 40. Vertical convergence of cells, due to active swimming of A. catenella from the subsurface to the top layer, could explain the additional 25-fold intensification (25 × 40=1000-fold) needed to reach the bloom concentrations that discolored the water. A model simulation that considered upward swimming overestimated cell concentrations downstream of the intense aggregation. This discrepancy between model and observed concentrations suggested a loss of cells from the water column at a time that corresponded to the start of encystment. These results indicated that the joint effect of upward swimming, horizontal convergence, and wind-driven flow contributed to the red water event, which might have promoted the sexual reproduction event that preceded the encystment process.

Atmospheric correction for satellite-derived bathymetry in the Caribbean waters: from a single image to multi-temporal approaches using Sentinel-2A/B.

Different atmospheric correction (AC) procedures for Sentinel-2 satellites are evaluated for their effectiveness in retrieving consistent satellite-derived bathymetry (SDB) over two islands in the Caribbean (Buck and Culebra). The log-ratio method for SDB, which allows use of minimal calibration information from lidar surveys (25 points in this study), is applied to several Sentinel-2A/B scenes at 10 m spatial resolution. The overall performance during a one-year study period depends on the image quality and AC. Three AC processors were evaluated: ACOLITE Exponential model (EXP), ACOLITE Dark Spectrum Fitting model (DSF), and C2RCC model. ACOLITE EXP and ACOLITE DSF produce greater consistency and repeatability with accurate results in a scene-by-scene analysis (mean errors ∼1.1 m) for depths up to 23 m (limit of lidar surveys). In contrast, C2RCC produces lower accuracy and noisier results with generally higher (>50%) errors (mean errors ∼2.2 m), but it is able to retrieve depth for scenes in Buck Island that have moderately severe sunglint. Furthermore, we demonstrate that a multi-temporal compositing model for SDB mapping, using ACOLITE for the input scenes, could achieve overall median errors <1 m for depths ranging 0-23 m. The simple and effective compositing model can considerably enhance coastal SDB estimates with high reliability and no missing data, outperforming the traditional single image approaches and thus eliminating the need to evaluate individual scenes. The consistency in the output from the AC correction indicates the potential for automated application of the multi-scene compositing technique, which can apply the open and free Sentinel-2 data set for the benefit of operational and scientific investigations.

Physical drivers facilitating a toxigenic cyanobacterial bloom in a major Great Lakes tributary.

The Maumee River is the primary source for nutrients fueling seasonal Microcystis-dominated blooms in western Lake Erie's open waters though such blooms in the river are infrequent. The river also serves as source water for multiple public water systems and a large food services facility in northwest Ohio, USA. On 20 September 2017, an unprecedented bloom was reported in the Maumee River estuary within the Toledo metropolitan area, which triggered a recreational water advisory. Here we (1) explore physical drivers likely contributing to the bloom's occurrence, and (2) describe the toxin concentration and bacterioplankton taxonomic composition. A historical analysis using ten-years of seasonal river discharge, water level, and local wind data identified two instances when high-retention conditions occurred over ≥10 days in the Maumee River estuary: in 2016 and during the 2017 bloom. Observation by remote sensing imagery supported the advection of cyanobacterial cells into the estuary from the lake during 2017 and the lack of an estuary bloom in 2016 due to a weak cyanobacterial bloom in the lake. A rapid-response survey during the 2017 bloom determined levels of the cyanotoxins, specifically microcystins, in excess of recreational contact limits at sites within the lower 20 km of the river while amplicon sequencing found these sites were dominated by Microcystis. These results highlight the need to broaden our understanding of physical drivers of cyanobacterial blooms within the interface between riverine and lacustrine systems, particularly as such blooms are expected to become more prominent in response to a changing climate.

Measurement of Cyanobacterial Bloom Magnitude using Satellite Remote Sensing.

Cyanobacterial harmful algal blooms (cyanoHABs) are a serious environmental, water quality and public health issue worldwide because of their ability to form dense biomass and produce toxins. Models and algorithms have been developed to detect and quantify cyanoHABs biomass using remotely sensed data but not for quantifying bloom magnitude, information that would guide water quality management decisions. We propose a method to quantify seasonal and annual cyanoHAB magnitude in lakes and reservoirs. The magnitude is the spatiotemporal mean of weekly or biweekly maximum cyanobacteria biomass for the season or year. CyanoHAB biomass is quantified using a standard reflectance spectral shape-based algorithm that uses data from Medium Resolution Imaging Spectrometer (MERIS). We demonstrate the method to quantify annual and seasonal cyanoHAB magnitude in Florida and Ohio (USA) respectively during 2003-2011 and rank the lakes based on median magnitude over the study period. The new method can be applied to Sentinel-3 Ocean Land Color Imager (OLCI) data for assessment of cyanoHABs and the change over time, even with issues such as variable data acquisition frequency or sensor calibration uncertainties between satellites. CyanoHAB magnitude can support monitoring and management decision-making for recreational and drinking water sources.

Performance metrics for the assessment of satellite data products: an ocean color case study.

Performance assessment of ocean color satellite data has generally relied on statistical metrics chosen for their common usage and the rationale for selecting certain metrics is infrequently explained. Commonly reported statistics based on mean squared errors, such as the coefficient of determination (r2), root mean square error, and regression slopes, are most appropriate for Gaussian distributions without outliers and, therefore, are often not ideal for ocean color algorithm performance assessment, which is often limited by sample availability. In contrast, metrics based on simple deviations, such as bias and mean absolute error, as well as pair-wise comparisons, often provide more robust and straightforward quantities for evaluating ocean color algorithms with non-Gaussian distributions and outliers. This study uses a SeaWiFS chlorophyll-a validation data set to demonstrate a framework for satellite data product assessment and recommends a multi-metric and user-dependent approach that can be applied within science, modeling, and resource management communities.

Mobile device application for monitoring cyanobacteria harmful algal blooms using Sentinel-3 satellite Ocean and Land Colour Instruments.

Cyanobacterial harmful algal blooms (cyanoHAB) cause human and ecological health problems in lakes worldwide. The timely distribution of satellite-derived cyanoHAB data is necessary for adaptive water quality management and for targeted deployment of water quality monitoring resources. Software platforms that permit timely, useful, and cost-effective delivery of information from satellites are required to help managers respond to cyanoHABs. The Cyanobacteria Assessment Network (CyAN) mobile device application (app) uses data from the European Space Agency Copernicus Sentinel-3 satellite Ocean and Land Colour Instrument (OLCI) in near realtime to make initial water quality assessments and quickly alert managers to potential problems and emerging threats related to cyanobacteria. App functionality and satellite data were validated with 25 state health advisories issued in 2017. The CyAN app provides water quality managers with a user-friendly platform that reduces the complexities associated with accessing satellite data to allow fast, efficient, initial assessments across lakes.

A method for examining temporal changes in cyanobacterial harmful algal bloom spatial extent using satellite remote sensing.

Cyanobacterial harmful algal blooms (CyanoHAB) are thought to be increasing globally over the past few decades, but relatively little quantitative information is available about the spatial extent of blooms. Satellite remote sensing provides a potential technology for identifying cyanoHABs in multiple water bodies and across geo-political boundaries. An assessment method was developed using MEdium Resolution Imaging Spectrometer (MERIS) imagery to quantify cyanoHAB surface area extent, transferable to different spatial areas, in Florida, Ohio, and California for the test period of 2008 to 2012. Temporal assessment was used to evaluate changes in satellite resolvable inland waterbodies for each state of interest. To further assess cyanoHAB risk within the states, the World Health Organization's (WHO) recreational guidance level thresholds were used to categorize surface area of cyanoHABs into three risk categories: low, moderate, and high-risk bloom area. Results showed that in Florida, the area of cyanoHABs increased largely due to observed increases in high-risk bloom area. California exhibited a slight decrease in cyanoHAB extent, primarily attributed to decreases in Northern California. In Ohio (excluding Lake Erie), little change in cyanoHAB surface area was observed. This study uses satellite remote sensing to quantify changes in inland cyanoHAB surface area across numerous water bodies within an entire state. The temporal assessment method developed here will be relevant into the future as it is transferable to the Ocean Land Colour Instrument (OLCI) on Sentinel-3A/3B missions.

Ecophysiological Examination of the Lake Erie Microcystis Bloom in 2014: Linkages between Biology and the Water Supply Shutdown of Toledo, OH.

Annual cyanobacterial blooms dominated by Microcystis have occurred in western Lake Erie (U.S./Canada) during summer months since 1995. The production of toxins by bloom-forming cyanobacteria can lead to drinking water crises, such as the one experienced by the city of Toledo in August of 2014, when the city was rendered without drinking water for >2 days. It is important to understand the conditions and environmental cues that were driving this specific bloom to provide a scientific framework for management of future bloom events. To this end, samples were collected and metatranscriptomes generated coincident with the collection of environmental metrics for eight sites located in the western basin of Lake Erie, including a station proximal to the water intake for the city of Toledo. These data were used to generate a basin-wide ecophysiological fingerprint of Lake Erie Microcystis populations in August 2014 for comparison to previous bloom communities. Our observations and analyses indicate that, at the time of sample collection, Microcystis populations were under dual nitrogen (N) and phosphorus (P) stress, as genes involved in scavenging of these nutrients were being actively transcribed. Targeted analysis of urea transport and hydrolysis suggests a potentially important role for exogenous urea as a nitrogen source during the 2014 event. Finally, simulation data suggest a wind event caused microcystin-rich water from Maumee Bay to be transported east along the southern shoreline past the Toledo water intake. Coupled with a significant cyanophage infection, these results reveal that a combination of biological and environmental factors led to the disruption of the Toledo water supply. This scenario was not atypical of reoccurring Lake Erie blooms and thus may reoccur in the future.

Satellite monitoring of cyanobacterial harmful algal bloom frequency in recreational waters and drinking source waters.

Cyanobacterial harmful algal blooms (cyanoHAB) cause extensive problems in lakes worldwide, including human and ecological health risks, anoxia and fish kills, and taste and odor problems. CyanoHABs are a particular concern in both recreational waters and drinking source waters because of their dense biomass and the risk of exposure to toxins. Successful cyanoHAB assessment using satellites may provide an indicator for human and ecological health protection, In this study, methods were developed to assess the utility of satellite technology for detecting cyanoHAB frequency of occurrence at locations of potential management interest. The European Space Agency's MEdium Resolution Imaging Spectrometer (MERIS) was evaluated to prepare for the equivalent series of Sentine1-3 Ocean and Land Colour Imagers (OLCI) launched in 2016 as part of the Copernicus program. Based on the 2012 National Lakes Assessment site evaluation guidelines and National Hydrography Dataset, the continental United States contains 275,897 lakes and reservoirs >1 hectare in area. Results from this study show that 5.6 % of waterbodies were resolvable by satellites with 300 m single-pixel resolution and 0.7 % of waterbodies were resolvable when a three by three pixel (3×3-pixel) array was applied based on minimum Euclidian distance from shore. Satellite data were spatially joined to U.S. public water surface intake (PWSI) locations, where single-pixel resolution resolved 57% of the PWSI locations and a 3×3-pixel array resolved 33% of the PWSI locations. Recreational and drinking water sources in Florida and Ohio were ranked from 2008 through 2011 by cyanoHAB frequency above the World Health Organization's (WHO) high threshold for risk of 100,000 cells mL-1. The ranking identified waterbodies with values above the WHO high threshold, where Lake Apopka, FL (99.1 %) and Grand Lake St. Marys, OH (83 %) had the highest observed bloom frequencies per region. The method presented here may indicate locations with high exposure to cyanoHABs and therefore can be used to assist in prioritizing management resources and actions for recreational and drinking water sources.