Assessing causal drivers of model-based cyanobacterial blooms along the South-East coast of India
(2025) In Journal of Operational Oceanography 18(3). p.229-243- Abstract
Cyanobacteria are bioactive compounds that produce toxins known as harmful algal blooms, posing serious threats to humans and marine life. Consequently, developing a robust monitoring framework to track and forecast the growth of such blooms is critical. The present work aims to understand the key environmental drivers influencing cyanobacterial bloom dynamics along the Southeast coast of India using model-based outputs from the NASA Ocean Biogeochemical model (NOBM) during 2004–2014. A Granger causality network analysis was employed to identify statistically significant causal relationships between environmental variables and cyanobacteria bloom concentrations. The analysis reveals unidirectional causal links from Sea surface... (More)
Cyanobacteria are bioactive compounds that produce toxins known as harmful algal blooms, posing serious threats to humans and marine life. Consequently, developing a robust monitoring framework to track and forecast the growth of such blooms is critical. The present work aims to understand the key environmental drivers influencing cyanobacterial bloom dynamics along the Southeast coast of India using model-based outputs from the NASA Ocean Biogeochemical model (NOBM) during 2004–2014. A Granger causality network analysis was employed to identify statistically significant causal relationships between environmental variables and cyanobacteria bloom concentrations. The analysis reveals unidirectional causal links from Sea surface temperature (SST) and precipitation to the cyanobacteria blooms. Bidirectional links exist between Nitrate (NO3) and Mixed layer depth (MLD) with the cyanobacterial blooms, which is statistically significant at a 95% confidence interval. SST, NO3 and MLD are the dominant causal drivers that promote the growth of cyanobacterial blooms across the South-East coast of India, evident from the higher values of outdegree for NO3, SST and MLD and higher values of indegree for Cyanobacteria during 2004–2014. Therefore, the present study provides a mitigation measure to monitor and forecast the growth of harmful algal blooms across the coastal areas.
(Less)
- author
- Budakoti, Sachin LU
- organization
- publishing date
- 2025
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Cyanobacteria, Granger causality network, harmful algal blooms, nitrate, NOBM
- in
- Journal of Operational Oceanography
- volume
- 18
- issue
- 3
- pages
- 15 pages
- publisher
- Taylor & Francis
- external identifiers
-
- scopus:105021422430
- ISSN
- 1755-876X
- DOI
- 10.1080/1755876X.2025.2580153
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
- id
- 10e9ec71-8aaa-4ea6-8a88-3860fa7dc8e2
- date added to LUP
- 2025-12-19 13:37:34
- date last changed
- 2025-12-19 13:38:22
@article{10e9ec71-8aaa-4ea6-8a88-3860fa7dc8e2,
abstract = {{<p>Cyanobacteria are bioactive compounds that produce toxins known as harmful algal blooms, posing serious threats to humans and marine life. Consequently, developing a robust monitoring framework to track and forecast the growth of such blooms is critical. The present work aims to understand the key environmental drivers influencing cyanobacterial bloom dynamics along the Southeast coast of India using model-based outputs from the NASA Ocean Biogeochemical model (NOBM) during 2004–2014. A Granger causality network analysis was employed to identify statistically significant causal relationships between environmental variables and cyanobacteria bloom concentrations. The analysis reveals unidirectional causal links from Sea surface temperature (SST) and precipitation to the cyanobacteria blooms. Bidirectional links exist between Nitrate (NO<sub>3</sub>) and Mixed layer depth (MLD) with the cyanobacterial blooms, which is statistically significant at a 95% confidence interval. SST, NO<sub>3</sub> and MLD are the dominant causal drivers that promote the growth of cyanobacterial blooms across the South-East coast of India, evident from the higher values of outdegree for NO<sub>3</sub>, SST and MLD and higher values of indegree for Cyanobacteria during 2004–2014. Therefore, the present study provides a mitigation measure to monitor and forecast the growth of harmful algal blooms across the coastal areas.</p>}},
author = {{Budakoti, Sachin}},
issn = {{1755-876X}},
keywords = {{Cyanobacteria; Granger causality network; harmful algal blooms; nitrate; NOBM}},
language = {{eng}},
number = {{3}},
pages = {{229--243}},
publisher = {{Taylor & Francis}},
series = {{Journal of Operational Oceanography}},
title = {{Assessing causal drivers of model-based cyanobacterial blooms along the South-East coast of India}},
url = {{http://dx.doi.org/10.1080/1755876X.2025.2580153}},
doi = {{10.1080/1755876X.2025.2580153}},
volume = {{18}},
year = {{2025}},
}