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Anderson, CR, Kudela RM, Kahru M, Chao Y, Rosenfeld LK, Bahr FL, Anderson DM, Norris TA.  2016.  Initial skill assessment of the California Harmful Algae Risk Mapping (C-HARM) system. Harmful Algae. 59:1-18.   10.1016/j.hal.2016.08.006   AbstractWebsite

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 (SPATI') 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. (C) 2016 Elsevier B.V. All rights reserved.

Anderson, CR, Berdalet E, Kudela RM, Cusack CK, Silke J, O'Rourke E, Dugan D, McCammon M, Newton JA, Moore SK, Paige K, Ruberg S, Morrison JR, Kirkpatrick B, Hubbard K, Morell J.  2019.  Scaling up from regional case studies to a global Harmful Algal Bloom observing system. Frontiers in Marine Science. 6   10.3389/fmars.2019.00250   AbstractWebsite

Harmful algal blooms (HABs) produce local impacts in nearly all freshwater and marine systems. They are a problem that occurs globally requiring an integrated and coordinated scientific understanding, leading to regional responses and solutions. Given that these natural phenomena will never be completely eliminated, an improved scientific understanding of HAB dynamics coupled with monitoring and ocean observations, facilitates new prediction and prevention strategies. Regional efforts are underway worldwide to create state-of-the-art HAB monitoring and forecasting tools, vulnerability assessments, and observing networks. In the United States, these include Alaska, Pacific Northwest, California, Gulf of Mexico, Gulf of Maine, Great Lakes, and the United States Caribbean islands. This paper examines several regional programs in the United States, European Union, and Asia and concludes that there is no one-size-fits-all approach. At the same time, successful programs require strong coordination with stakeholders and institutional sustainability to maintain and reinforce them with new automating technologies, wherever possible, ensuring integration of modeling efforts with multiple regional to national programs. Recommendations for scaling up to a global observing system for HABs can be summarized as follows: (1) advance and improve cost-effective and sustainable HAB forecast systems that address the HAB-risk warning requirements of key end-users at global and regional levels; (2) design programs that leverage and expand regional HAB observing systems to evaluate emerging technologies for Essential Ocean Variables (EOVs) and Essential Biodiversity Variables (EBVs) in order to support interregional technology comparisons and regional networks of observing capabilities; (3) fill the essential need for sustained, preferably automated, near real-time information from nearshore and offshore sites situated in HAB transport pathways to provide improved, advanced HAB warnings; (4) merge ecological knowledge and models with existing Earth System Modeling Frameworks to enhance end-to-end capabilities in forecasting and scenario-building; (5) provide seasonal to decadal forecasts to allow governments to plan, adapt to a changing marine environment, and ensure coastal industries are supported and sustained in the years ahead; and (6) support implementation of the recent calls for action by the United Nations Decade 2010 Sustainable Development Goals (SDGs) to develop indicators that are relevant to an effective and global HAB early warning system.