Initial skill assessment of the California Harmful Algae Risk Mapping (C-HARM) system.

Toxic algal events are an annual burden on aquaculture and coastal ecosystems of California. The threat of domoic acid (DA) toxicity to human and wildlife health is the dominant harmful algal bloom (HAB) concern for the region, leading to a strong focus on prediction and mitigation of these blooms and their toxic effects. This paper describes the initial development of the California Harmful Algae Risk Mapping (C-HARM) system that predicts the spatial likelihood of blooms and dangerous levels of DA using a unique blend of numerical models, ecological forecast models of the target group, Pseudo-nitzschia, and satellite ocean color imagery. Data interpolating empirical orthogonal functions (DINEOF) are applied to ocean color imagery to fill in missing data and then used in a multivariate mode with other modeled variables to forecast biogeochemical parameters. Daily predictions (nowcast and forecast maps) are run routinely at the Central and Northern California Ocean Observing System (CeNCOOS) and posted on its public website. Skill assessment of model output for the nowcast data is restricted to nearshore pixels that overlap with routine pier monitoring of HABs in California from 2014 to 2015. Model lead times are best correlated with DA measured with solid phase adsorption toxin tracking (SPATT) and marine mammal strandings from DA toxicosis, suggesting long-term benefits of the HAB predictions to decision-making. Over the next three years, the C-HARM application system will be incorporated into the NOAA operational HAB forecasting system and HAB Bulletin.

[1]  Alexander Barth,et al.  Enhancing temporal correlations in EOF expansions for the reconstruction of missing data using DINEOF , 2009 .

[2]  David A. Siegel,et al.  Detecting toxic diatom blooms from ocean color and a regional ocean model , 2011 .

[3]  G. Egbert,et al.  Efficient Inverse Modeling of Barotropic Ocean Tides , 2002 .

[4]  Categorizing the severity of paralytic shellfish poisoning outbreaks in the Gulf of Maine for forecasting and management. , 2014, Deep-sea research. Part II, Topical studies in oceanography.

[5]  M. Busman,et al.  Domoic acid production near California coastal upwelling zones, June 1998 , 2000 .

[6]  R. P. Stumpfa,et al.  Monitoring Karenia brevis blooms in the Gulf of Mexico using satellite ocean color imagery and other data , 2003 .

[7]  Vera L. Trainer,et al.  Harmful algal blooms along the North American west coast region: History, trends, causes, and impacts , 2012 .

[8]  J. Beckers,et al.  Reconstruction of incomplete oceanographic data sets using empirical orthogonal functions: application to the Adriatic Sea surface temperature , 2005 .

[9]  Kevin Ruddick,et al.  Reconstruction of MODIS total suspended matter time series maps by DINEOF and validation with autonomous platform data , 2011 .

[10]  Raphael M. Kudela,et al.  Development of a logistic regression model for the prediction of toxigenic Pseudo-nitzschia blooms in Monterey Bay, California. , 2009 .

[11]  Raphael M. Kudela,et al.  Application of Solid Phase Adsorption Toxin Tracking (SPATT) for field detection of the hydrophilic phycotoxins domoic acid and saxitoxin in coastal California , 2010 .

[12]  John L. Largier,et al.  New insights into the controls and mechanisms of plankton productivity in coastal upwelling waters of the Northern California Current System , 2008 .

[13]  Yi Chao,et al.  High-resolution real-time modeling of the marine atmospheric boundary layer in support of the AOSN-II field campaign , 2009 .

[14]  D. Haidvogel,et al.  A semi-implicit ocean circulation model using a generalized topography-following coordinate system , 1994 .

[15]  P. Minnett,et al.  Long-term evaluation of three satellite ocean color algorithms for identifying harmful algal blooms (Karenia brevis) along the west coast of Florida: A matchup assessment. , 2011, Remote sensing of environment.

[16]  C. Anderson,et al.  Pseudo-nitzschia and domoic acid fluxes in Santa Barbara Basin (CA) from 1993 to 2008 , 2011 .

[17]  Chunyan Li,et al.  Pseudo‐nitzschia blooms, domoic acid, and related California sea lion strandings in Monterey Bay, California , 2012 .

[18]  Pierre-Marie Poulain,et al.  MODIS chlorophyll variability in the northern Adriatic Sea and relationship with forcing parameters , 2007 .

[19]  David A. Siegel,et al.  Rapid downward transport of the neurotoxin domoic acid in coastal waters. , 2009 .

[20]  A. H. Murphy,et al.  What Is a Good Forecast? An Essay on the Nature of Goodness in Weather Forecasting , 1993 .

[21]  Libe Washburn,et al.  Circulation and environmental conditions during a toxigenic Pseudo-nitzschia australis bloom in the Santa Barbara Channel, California , 2006 .

[22]  G. Smith,et al.  Domoic acid contamination within eight representative species from the benthic food web of Monterey Bay, California, USA , 2008 .

[23]  R. Tjeerdema,et al.  Detection of domoic acid in northern anchovies and California sea lions associated with an unusual mortality event. , 1999, Natural toxins.

[24]  V. Trainer,et al.  Monitoring Approaches for Early Warning of Domoic Acid Events in Washington State , 2005 .

[25]  M. Brzezinski,et al.  Empirical models of toxigenic Pseudo-nitzschia blooms: Potential use as a remote detection tool in the Santa Barbara Channel , 2009 .

[26]  Kayo Ide,et al.  A three‐dimensional variational data assimilation scheme for the Regional Ocean Modeling System: Implementation and basic experiments , 2008 .

[27]  David J. Schwab,et al.  Evolution of a cyanobacterial bloom forecast system in western Lake Erie: Development and initial evaluation , 2013 .

[28]  L. MacKenzie,et al.  In situ passive solid-phase adsorption of micro-algal biotoxins as a monitoring tool. , 2010, Current opinion in biotechnology.

[29]  D. Jin,et al.  Economic impact of the 2005 red tide event on commercial shellfish fisheries in New England , 2008 .

[30]  J. Beckers,et al.  EOF Calculations and Data Filling from Incomplete Oceanographic Datasets , 2003 .

[31]  Alexander Barth,et al.  Conclusions References , 2004 .

[32]  Wen Long,et al.  Ecological forecasting in Chesapeake Bay: Using a mechanistic-empirical modeling approach , 2013 .

[33]  Roman Marin,et al.  Mortality of sea lions along the central California coast linked to a toxic diatom bloom , 2000, Nature.

[34]  M. Quilliam,et al.  AN OUTBREAK OF DOMOIC ACID POISONING ATTRIBUTED TO THE PENNATE DIATOM PSEUDONITZSCHIA AUSTRALIS 1 , 1992 .

[35]  Alexander F. Shchepetkin,et al.  The regional oceanic modeling system (ROMS): a split-explicit, free-surface, topography-following-coordinate oceanic model , 2005 .

[36]  E. Lehmann Testing Statistical Hypotheses , 1960 .

[37]  Tom Fawcett,et al.  ROC Graphs: Notes and Practical Considerations for Researchers , 2007 .

[38]  I. Jolliffe,et al.  Forecast verification : a practitioner's guide in atmospheric science , 2011 .

[39]  Aïda Alvera Azcarate,et al.  Reconstruction of missing satellite total suspended matter data over the Southern North Sea and English Channel using empirical orthogonal function decomposition of satellite imagery and hydrodynamical modelling , 2008 .

[40]  James G. Bellingham,et al.  Monitoring of harmful algal blooms in the era of diminishing resources: A case study of the U.S. West Coast , 2013 .

[41]  K. Ide,et al.  A Three-Dimensional Variational Data Assimilation Scheme for the Regional Ocean Modeling System , 2008 .

[42]  R. Kudela,et al.  The Monitoring of Harmful Algal Blooms through Ocean Observing: The Development of the California Harmful Algal Bloom Monitoring and Alert Program , 2015 .

[43]  R. Kudela,et al.  An unprecedented coastwide toxic algal bloom linked to anomalous ocean conditions , 2016, Geophysical research letters.

[44]  S. Bates,et al.  Pseudo-nitzschia physiological ecology, phylogeny, toxicity, monitoring and impacts on ecosystem health , 2012 .

[45]  D. Anderson,et al.  Suppression of the 2010 Alexandrium fundyense bloom by changes in physical, biological, and chemical properties of the Gulf of Maine , 2011, Limnology and oceanography.

[46]  Mati Kahru,et al.  Evaluation of Satellite Retrievals of Ocean Chlorophyll-a in the California Current , 2014, Remote. Sens..

[47]  David M. Fratantoni,et al.  Development, implementation and evaluation of a data-assimilative ocean forecasting system off the central California coast , 2009 .

[48]  D. Anderson,et al.  Progress in understanding harmful algal blooms: paradigm shifts and new technologies for research, monitoring, and management. , 2012, Annual review of marine science.

[49]  Angelicque E. White,et al.  Satellite‐based detection and monitoring of phytoplankton blooms along the Oregon coast , 2012 .

[50]  J. Beckers,et al.  Multivariate reconstruction of missing data in sea surface temperature, chlorophyll, and wind satellite fields , 2007 .

[51]  James C. McWilliams,et al.  Modeling tides in Monterey Bay, California , 2009 .

[52]  Mark A. Moline,et al.  Synergistic applications of autonomous underwater vehicles and regional ocean modeling system in coastal ocean forecasting , 2008 .

[53]  S. Siedlecki,et al.  Hindcasts of potential harmful algal bloom transport pathways on the Pacific Northwest coast , 2014 .

[54]  Wen Long,et al.  Predicting potentially toxigenic Pseudo-nitzschia blooms in the Chesapeake Bay , 2010 .

[55]  Characterizing the South Atlantic Bight seasonal variability and cold‐water event in 2003 using a daily cloud‐free SST and chlorophyll analysis , 2009 .

[56]  Erica L. Seubert,et al.  Subsurface seeding of surface harmful algal blooms observed through the integration of autonomous gliders, moored environmental sample processors, and satellite remote sensing in southern California , 2015 .

[57]  Richard P Stumpf,et al.  Skill assessment for an operational algal bloom forecast system. , 2009, Journal of marine systems : journal of the European Association of Marine Sciences and Techniques.