Assessing a model of Pacific Northwest harmful algal bloom transport as a decision-support tool

Stone, Hally B.; Banas, Neil S.; MacCready, Parker; Trainer, Vera L.; Ayres, Daniel L.; Hunter, Matthew

doi:10.1016/j.hal.2022.102334

Cited by 9 publications

(3 citation statements)

References 37 publications

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“…We applied the same pipeline to the Scripps Pier Chl-a prediction and assessed the models' performance in predicating Chl-a concentration. The performance in binary event forecasts was not evaluated because it is more traditional to use a Pseudo-nitzschia cell count as a bloom indicator in the Pacific Northwest [61]. The results showed a smaller RMSE using WA-LSTM than RFR and SVR, and an even smaller RMSE and larger R 2 using WA-BPNN with the enriched dataset.…”

Section: Resultsmentioning

confidence: 99%

Temporal Prediction of Coastal Water Quality Based on Environmental Factors with Machine Learning

Lin,

Liu,

Song

et al. 2023

JMSE

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The accurate forecast of algal blooms can provide helpful information for water resource management. However, the complex relationship between environmental variables and blooms makes the forecast challenging. In this study, we build a pipeline incorporating four commonly used machine learning models, Support Vector Regression (SVR), Random Forest Regression (RFR), Wavelet Analysis (WA)-Back Propagation Neural Network (BPNN) and WA-Long Short-Term Memory (LSTM), to predict chlorophyll-a in coastal waters. Two areas with distinct environmental features, the Neuse River Estuary, NC, USA—where machine learning models are applied for short-term algal bloom forecast at single stations for the first time—and the Scripps Pier, CA, USA, are selected. Applying the pipeline, we can easily switch from the NRE forecast to the Scripps Pier forecast with minimum model tuning. The pipeline successfully predicts the occurrence of algal blooms in both regions, with more robustness using WA-LSTM and WA-BPNN than SVR and RFR. The pipeline allows us to find the best results by trying different numbers of neuron hidden layers. The pipeline is easily adaptable to other coastal areas. Experience with the two study regions demonstrated that enrichment of the dataset by including dominant physical processes is necessary to improve chlorophyll prediction when applying it to other aquatic systems.

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Section: Resultsmentioning

confidence: 99%

Temporal Prediction of Coastal Water Quality Based on Environmental Factors with Machine Learning

Lin,

Liu,

Song

et al. 2023

JMSE

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“…Coupled or linked atmospheric, hydrologic, and oceanic models are capable of simulating the release, downstream transport, and evolution in the coastal ocean of tracers representing contaminants. These complex models can be used as predictive tools for when conditions may be hazardous as well as pinpointing when to take water quality samples (Feddersen et al., 2021; Stone et al., 2022). Additionally, they can be used to guide development of simplified models that are even more readily deployed for planning (Brasseale et al., 2023).…”

Section: Discussionmentioning

confidence: 99%

Modeled Coastal‐Ocean Pathways of Land‐Sourced Contaminants in the Aftermath of Hurricane Florence

Moulton,

Zambon,

Xue

et al. 2024

JGR Oceans

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Extreme precipitation during Hurricane Florence, which made landfall in North Carolina in September 2018, led to breaches of hog waste lagoons, coal ash pits, and wastewater facilities. In the weeks following the storm, freshwater discharge carried pollutants, sediment, organic matter, and debris to the coastal ocean, contributing to beach closures, algae blooms, hypoxia, and other ecosystem impacts. Here, the ocean pathways of land‐sourced contaminants following Hurricane Florence are investigated using the Regional Ocean Modeling System (ROMS) with a river point source with fixed water properties from a hydrologic model (WRF‐Hydro) of the Cape Fear River Basin, North Carolina's largest watershed. Patterns of contaminant transport in the coastal ocean are quantified with a finite duration tracer release based on observed flooding of agricultural and industrial facilities. A suite of synthetic events also was simulated to investigate the sensitivity of the river plume transport pathways to river discharge and wind direction. The simulated Hurricane Florence discharge event led to westward (downcoast) transport of contaminants in a coastal current, along with intermittent storage and release of material in an offshore (bulge) or eastward (upcoast) region near the river mouth, modulated by alternating upwelling and downwelling winds. The river plume patterns led to a delayed onset and long duration of contaminants affecting beaches 100 km to the west, days to weeks after the storm. Maps of the onset and duration of hypothetical water quality hazards for a range of weather conditions may provide guidance to managers on the timing of swimming/shellfishing advisories and water quality sampling.

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“…Details of model configuration and validation are given in MacCready et al (2021). LiveOcean has an offline particle tracking code written in python named "Tracker" (v1.1), which has been used to identify the source of estuarine inflow from continental shelves (Brasseale and MacCready, 2021) and track trajectories of the harmful species Pseudo-nitzschia in daily post-processing to assist resource managers to decide to open or close WA beaches for razor clam harvest in 50 combination with beach sampling (https://faculty.washington.edu/pmacc/LO/p5_Phab_full_salt_top.html, Stone et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Intercomparisons of five ocean particle tracking software packages

Xiong

MacCready

2023

Preprint

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Abstract. Particle tracking is widely utilized to study transport features in a range of physical, chemical, and biological processes in oceanography. In this study, a new offline particle tracking package, Tracker, is introduced and its performance is evaluated in comparison to an online Eulerian dye, one online and three offline particle tracking software packages in a small high-resolution (200 m) model domain and a large coarser (1000 m) model domain. It was found that both particle and dye approaches give similar results across different model resolutions and domains when they were tracking the same water mass, as indicated by similar mean advection pathways and spatial distributions of dye and particles. The flexibility of offline particle tracking and its similarity against online dye and particle tracking make it a useful tool to complement existing ocean circulation models. Lastly, the new Tracker was shown to be a reliable particle tracking package to complement the Regional Ocean Modeling System (ROMS), and tradeoffs of performance, modifiability, and ease of use that can influence the choice of which package to use are discussed.

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Assessing a model of Pacific Northwest harmful algal bloom transport as a decision-support tool

Cited by 9 publications

References 37 publications

Temporal Prediction of Coastal Water Quality Based on Environmental Factors with Machine Learning

Temporal Prediction of Coastal Water Quality Based on Environmental Factors with Machine Learning

Modeled Coastal‐Ocean Pathways of Land‐Sourced Contaminants in the Aftermath of Hurricane Florence

Intercomparisons of five ocean particle tracking software packages

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