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Improved seasonal prediction of harmful algal blooms in Lake Erie using large-scale climate indices

Environmental Studies and Forestry

Improved seasonal prediction of harmful algal blooms in Lake Erie using large-scale climate indices

M. Tewari, C. M. Kishtawal, et al.

Harmful Algal Blooms (HABs) in Lake Erie are wreaking havoc, leading to significant economic damage. This innovative research by Mukul Tewari, Chandra M. Kishtawal, Vincent W. Moriarty, Pallav Ray, Tarkeshwar Singh, Lei Zhang, Lloyd Treinish, and Kushagra Tewari showcases a groundbreaking machine learning method that enhances prediction accuracy by combining nutrient loading data with large-scale climate indices. Seasonal predictions can be made by early June, paving the way for effective mitigation strategies.

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Playback language: English
Abstract
Harmful Algal Blooms (HABs) in Lake Erie cause significant economic losses. Conventional prediction methods using nutrient loading are inaccurate during extreme bloom years. This study demonstrates that a machine learning approach, incorporating observed nutrient loading and large-scale climate indices, improves HAB prediction in Lake Erie. Seasonal prediction can be completed by early June, enabling timely mitigation strategies.
Publisher
Communications Earth & Environment
Published On
Aug 31, 2022
Authors
Mukul Tewari, Chandra M. Kishtawal, Vincent W. Moriarty, Pallav Ray, Tarkeshwar Singh, Lei Zhang, Lloyd Treinish, Kushagra Tewari
Tags
Harmful Algal Blooms
Lake Erie
machine learning
climate indices
nutrient loading
economic losses
seasonal prediction

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