Harmful algal bloom forecast system for SW Ireland. Part II: Are operational oceanographic models useful in a HAB warning system

Cusack, Caroline; Dabrowski, Tomasz; Lyons, Kieran; Berry, Alan; Westbrook, Guy; Salas, Rafael; Duffy, Conor; Nolan, Glenn; Silke, Joe

doi:10.1016/j.hal.2015.11.013

Cited by 43 publications

(31 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The wind direction and speed can affect the distribution of algal blooms in three major ways: (1) prevailing wind directions create ocean currents and water exchanges that transport HAB cells [9,54] and biotoxins [9]; (2) wind and bathymetry guide the location of nutrient upwelling facilitating the concentration of the algae [13]; and (3) winds can transfer the aerosols [21] promoting the growth of toxic phytoplankton [55]. The wind direction and wind speed were calculated using a reflectance model based on the Cox-Munk wave-slope distribution [56].…”

Section: Wind Direction (Degrees) and Wind Speed (M/s)mentioning

confidence: 99%

Mapping and Forecasting Onsets of Harmful Algal Blooms Using MODIS Data over Coastal Waters Surrounding Charlotte County, Florida

et al. 2018

View full text Add to dashboard Cite

Over the past two decades, persistent occurrences of harmful algal blooms (HAB; Karenia brevis) have been reported in Charlotte County, southwestern Florida. We developed data-driven models that rely on spatiotemporal remote sensing and field data to identify factors controlling HAB propagation, provide a same-day distribution (nowcasting), and forecast their occurrences up to three days in advance. We constructed multivariate regression models using historical HAB occurrences (213 events reported from January 2010 to October 2017) compiled by the Florida Fish and Wildlife Conservation Commission and validated the models against a subset (20%) of the historical events. The models were designed to capture the onset of the HABs instead of those that developed days earlier and continued thereafter. A prototype of an early warning system was developed through a threefold exercise. The first step involved the automatic downloading and processing of daily Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua products using SeaDAS ocean color processing software to extract temporal and spatial variations of remote sensing-based variables over the study area. The second step involved the development of a multivariate regression model for same-day mapping of HABs and similar subsequent models for forecasting HAB occurrences one, two, and three days in advance. Eleven remote sensing variables and two non-remote sensing variables were used as inputs for the generated models. In the third and final step, model outputs (same-day and forecasted distribution of HABs) were posted automatically on a web map. Our findings include: (1) the variables most indicative of the timing of bloom propagation are bathymetry, euphotic depth, wind direction, sea surface temperature (SST), ocean chlorophyll three-band algorithm for MODIS [chlorophyll-a OC3M] and distance from the river mouth, and (2) the model predictions were 90% successful for same-day mapping and 65%, 72% and 71% for the one-, two- and three-day advance predictions, respectively. The adopted methodologies are reliable at a local scale, dependent on readily available remote sensing data, and cost-effective and thus could potentially be used to map and forecast algal bloom occurrences in data-scarce regions.

show abstract

Section: Wind Direction (Degrees) and Wind Speed (M/s)mentioning

confidence: 99%

Mapping and Forecasting Onsets of Harmful Algal Blooms Using MODIS Data over Coastal Waters Surrounding Charlotte County, Florida

et al. 2018

View full text Add to dashboard Cite

show abstract

“…The wind direction and speed can affect the distribution of algal blooms in three major ways: (1) prevailing wind directions create ocean currents and water exchanges that transport HAB cells [35,9] or their biotoxins [9], (2) wind combined with ocean bathymetry controls the locations where nutrient upwelling occurs; HABs feed on and concentrate around these nutrient upwelling sites [13], and (3) winds can also transfer the aerosols on the sea surface [21] that were shown as promoting the growth of toxic phytoplankton [36]. The wind direction and wind speed were calculated using a reflectance model based on the Cox-Mux wave-slope distribution [37].…”

Section: 12mentioning

confidence: 99%

“…Earlier efforts to map or forecast HAB occurrences examined the distribution of HABs in relation to a wide range of related and/or causal parameters, such as wind-driven water exchanges [9], temperature [10], the relative abundance of protozoans that feed on algae, e.g., Mesodinium species [11], cell distribution and oceanic currents [12], and hydrodynamic variables, e.g., current pathways, rate and volume of flow, upwelling and downwelling pulses [13]. Such parameters were subsequently used to conduct same-day mappings of bloom occurrences, to model onsets of blooms [14][15][16] and to forecast seasonal algal bloom occurrences [12].…”

Section: Introductionmentioning

confidence: 99%

Mapping and forecasting onsets of harmful algal blooms using MODIS data over coastal waters surrounding Charlotte County, Florida

Karki¹,

Sultan²,

Elkadiri³

et al. 2018

Preprint

View full text Add to dashboard Cite

Over the past two decades, persistent occurrences of harmful algal blooms (HAB; Karenia brevis) have been reported in Charlotte County, southwestern Florida. We developed data-driven models that rely on spatiotemporal remote sensing and field data to identify factors controlling HAB propagation, provide a same-day distribution (nowcasting), and forecast their occurrences up to three days in advance. We constructed multivariate regression models using historical HAB occurrences (213 events reported from January 2010 to October 2017) compiled by the Florida Fish and Wildlife Conservation Commission and validated the models against a subset (20%) of the reported historical events. The models were designed to specifically capture the onset of the HABs instead of those that developed days earlier and continued thereafter. A prototype of an early warning system was developed through a threefold exercise. The first step involved the automatic downloading and processing of daily Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua products using SeaDAS ocean color processing software to extract temporal and spatial variations of remote sensing-based variables over the study area. The second step involved the development of a multivariate regression model for same-day mapping of HABs and similar subsequent models for forecasting HAB occurrences one, two, and three days in advance. Eleven remote sensing variables and two non-remote sensing variables were used as inputs for the generated models. In the third and final step, model outputs (same-day and forecasted distribution of HABs) were posted automatically on a web-based GIS (http://www.esrs.wmich.edu/webmap/bloom/). Our findings include the following: (1) the variables most indicative of the timing of bloom propagation are bathymetry, euphotic depth, wind direction, SST, chlorophyll-a [OC3M] and distance from the river mouth, and (2) the model predictions were 90% successful for same-day mapping and 65%, 72% and 71% for the one-, two- and three-day advance predictions, respectively. The adopted methodologies are reliable, dependent on readily available remote sensing data sets, and cost-effective and thus could potentially be used to map and forecast algal bloom occurrences in data-scarce regions.

show abstract

“…satellite data: sea surface temperature and chlorophyll, data from the national harmful algal bloom and shellfish biotoxin monitoring programme) constitute part of a weekly HAB bulletin published online. Cusack et al (2016) show how this model is used in the HAB forecast and how all of the above products provide information on the likelihood of HAB outbreaks in the bay. Table 1 summarizes the products that are currently used by local expert to generate the bulletin.…”

Section: Hab Warningmentioning

confidence: 99%

“…It is therefore evident that a shelf ocean observing system is needed to provide the offshore HAB abundance data. More detailed discussion can be found in Cusack et al (2016).…”

Section: Bantry Bay Modelmentioning

confidence: 99%

Ocean modelling for aquaculture and fisheries in Irish waters

et al. 2016

View full text Add to dashboard Cite

Abstract. The Marine Institute, Ireland, runs a suite of operational regional and coastal ocean models. Recent developments include several tailored products that focus on the key needs of the Irish aquaculture sector. In this article, an overview of the products and services derived from the models are presented. The authors give an overview of a shellfish model developed in-house and that was designed to predict the growth, the physiological interactions with the ecosystem, and the level of coliform contamination of the blue mussel. As such, this model is applicable in studies on the carrying capacity of embayments, assessment of the impacts of pollution on aquaculture grounds, and the determination of shellfish water classes. Further services include the assimilation of the model-predicted shelf water movement into a new harmful algal bloom alert system used to inform end users of potential toxic shellfish events and high biomass blooms that include fish-killing species. Models are also used to identify potential sites for offshore aquaculture, to inform studies of potential cross-contamination in farms from the dispersal of planktonic sea lice larvae and other pathogens that can infect finfish, and to provide modelled products that underpin the assessment and advisory services on the sustainable exploitation of the resources of marine fisheries. This paper demonstrates that ocean models can provide an invaluable contribution to the sustainable blue growth of aquaculture and fisheries.

show abstract

Harmful algal bloom forecast system for SW Ireland. Part II: Are operational oceanographic models useful in a HAB warning system

Cited by 43 publications

References 32 publications

Mapping and Forecasting Onsets of Harmful Algal Blooms Using MODIS Data over Coastal Waters Surrounding Charlotte County, Florida

Mapping and Forecasting Onsets of Harmful Algal Blooms Using MODIS Data over Coastal Waters Surrounding Charlotte County, Florida

Mapping and forecasting onsets of harmful algal blooms using MODIS data over coastal waters surrounding Charlotte County, Florida

Ocean modelling for aquaculture and fisheries in Irish waters

Contact Info

Product

Resources

About