Quantifying national and regional cyanobacterial occurrence in US lakes using satellite remote sensing.

Cyanobacterial harmful algal blooms are the most common form of harmful algal blooms in freshwater systems throughout the world. However, in situ sampling of cyanobacteria in inland lakes is limited both spatially and temporally. Satellite data has proven to be an effective tool to monitor cyanobacteria in freshwater lakes across the United States. This study uses data from the European Space Agency Envisat MEdium Resolution Imaging Spectrometer and the Sentinel-3 Ocean and Land Color Instrument to provide a national overview of the percentage of lakes experiencing a cyanobacterial bloom on a weekly basis for 2008-2011, 2017, and 2018. A total of 2321 lakes across the contiguous United States were included in the analysis. We examined four different thresholds to define when a waterbody is classified as experiencing a bloom. Across these four thresholds, we explored variability in bloom percentage with changes in seasonality and lake size. As a validation of algorithm performance, we analyzed the agreement between satellite observations and previously established ecological patterns, although data availability in the wintertime limited these comparisons on a year-round basis. Changes in cyanobacterial bloom percentage at the national scale followed the well-known temporal pattern of freshwater blooms. The percentage of lakes experiencing a bloom increased throughout the year, reached a maximum in fall, and decreased through the winter. Wintertime data, particularly in northern regions, were consistently limited due to snow and ice cover. With the exception of the Southeast and South, regional patterns mimicked patterns found at the national scale. The Southeast and South exhibited an unexpected pattern as cyanobacterial bloom percentage reached a maximum in the winter rather than the summer. Lake Jesup in Florida was used as a case study to validate this observed pattern against field observations of chlorophyll a. Results from this research establish a baseline of annual occurrence of cyanobacterial blooms in inland lakes across the United States. In addition, methods presented in this study can be tailored to fit the specific requirements of an individual system or region.

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.

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.

Envisat MERIS and Sentinel-3 OLCI satellite lake biophysical water quality flag dataset for the contiguous United States.

Monitoring lake biophysical water quality is a global challenge. Satellite remote sensing offers a technology for continuous water quality information in data poor regions throughout the United States. Quality assurance flag data are provided for the presence of snow/ice, land-adjacency, and unresolvable waterbodies supporting water quality derived measures from Envisat MEdium Resolution Imaging Spectrometer and Sentinel-3 Ocean and Land Colour Instrument for the continental United States. In addition, an updated Waterbody Data mask that contains valid waterbody and coastal ocean delineation is provided. The quality assurance flag datasets can benefit the scientific community in processing lake water quality throughout the contiguous United States by addressing errors from snow/ice, land adjacency, and land masking. The dataset presented here will be used in the development of national scale metrics for derived biophysical water quality in the US.

Forecasting Seasonal Vibrio parahaemolyticus Concentrations in New England Shellfish.

Seafood-borne Vibrio parahaemolyticus illness is a global public health issue facing resource managers and the seafood industry. The recent increase in shellfish-borne illnesses in the Northeast United States has resulted in the application of intensive management practices based on a limited understanding of when and where risks are present. We aim to determine the contribution of factors that affect V. parahaemolyticus concentrations in oysters (Crassostrea virginica) using ten years of surveillance data for environmental and climate conditions in the Great Bay Estuary of New Hampshire from 2007 to 2016. A time series analysis was applied to analyze V. parahaemolyticus concentrations and local environmental predictors and develop predictive models. Whereas many environmental variables correlated with V. parahaemolyticus concentrations, only a few retained significance in capturing trends, seasonality and data variability. The optimal predictive model contained water temperature and pH, photoperiod, and the calendar day of study. The model enabled relatively accurate seasonality-based prediction of V. parahaemolyticus concentrations for 2014-2016 based on the 2007-2013 dataset and captured the increasing trend in extreme values of V. parahaemolyticus concentrations. The developed method enables the informative tracking of V. parahaemolyticus concentrations in coastal ecosystems and presents a useful platform for developing area-specific risk forecasting models.

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.

Environmental Conditions Associated with Elevated Vibrio parahaemolyticus Concentrations in Great Bay Estuary, New Hampshire.

Reports from state health departments and the Centers for Disease Control and Prevention indicate that the annual number of reported human vibriosis cases in New England has increased in the past decade. Concurrently, there has been a shift in both the spatial distribution and seasonal detection of Vibrio spp. throughout the region based on limited monitoring data. To determine environmental factors that may underlie these emerging conditions, this study focuses on a long-term database of Vibrio parahaemolyticus concentrations in oyster samples generated from data collected from the Great Bay Estuary, New Hampshire over a period of seven consecutive years. Oyster samples from two distinct sites were analyzed for V. parahaemolyticus abundance, noting significant relationships with various biotic and abiotic factors measured during the same period of study. We developed a predictive modeling tool capable of estimating the likelihood of V. parahaemolyticus presence in coastal New Hampshire oysters. Results show that the inclusion of chlorophyll a concentration to an empirical model otherwise employing only temperature and salinity variables, offers improved predictive capability for modeling the likelihood of V. parahaemolyticus in the Great Bay Estuary.

Uncertainty in model predictions of Vibrio vulnificus response to climate variability and change: a Chesapeake Bay case study.

The effect that climate change and variability will have on waterborne bacteria is a topic of increasing concern for coastal ecosystems, including the Chesapeake Bay. Surface water temperature trends in the Bay indicate a warming pattern of roughly 0.3-0.4°C per decade over the past 30 years. It is unclear what impact future warming will have on pathogens currently found in the Bay, including Vibrio spp. Using historical environmental data, combined with three different statistical models of Vibrio vulnificus probability, we explore the relationship between environmental change and predicted Vibrio vulnificus presence in the upper Chesapeake Bay. We find that the predicted response of V. vulnificus probability to high temperatures in the Bay differs systematically between models of differing structure. As existing publicly available datasets are inadequate to determine which model structure is most appropriate, the impact of climatic change on the probability of V. vulnificus presence in the Chesapeake Bay remains uncertain. This result points to the challenge of characterizing climate sensitivity of ecological systems in which data are sparse and only statistical models of ecological sensitivity exist.