Analysis of Temporal Cumulative, Lagging Effects and Driving Mechanisms of Environmental Factors on Green Tide Outbreaks: A Case Study of the Ulva Prolifera Disaster in the South Yellow Sea, China

The Ulva prolifera green tide in the South Yellow Sea has erupted annually for many years, posing significant threats to coastal ecology, the economy, and society. While environmental factors are widely acknowledged as prerequisites for these outbreaks, the asynchrony and complex coupling between their variations and disaster events have challenged traditional studies that rely on instantaneous correlations to uncover the underlying dynamic mechanisms. This study focuses on the Ulva prolifera disaster in the South Yellow Sea, systematically analyzing its spatiotemporal distribution patterns, the temporal accumulation and lag effects of environmental factors, and the coupled driving mechanisms using the Floating Algae Index (FAI). The results indicate that: (1) The disaster shows significant interannual variability, with 2019 experiencing the most severe outbreak. Monthly, the disaster begins offshore of Jiangsu in May, moves northward and peaks in June, expands northward with reduced scale in July, and largely dissipates in August. Years with large-scale outbreaks exhibit higher distribution frequency and broader spatial extent. (2) Environmental factors demonstrate significant accumulation and lag effects on Ulva prolifera disasters, with a mixed temporal mode of both accumulation and lag effects being dominant. Temporal parameters vary across different factors—nutrients generally have longer lag times, while light and temperature factors show longer accumulation times. These parameters change dynamically across disaster stages and display a clear inshore–offshore gradient, with shorter effects in coastal areas and longer durations in offshore waters, revealing significant spatiotemporal heterogeneity in temporal response patterns. (3) The driving mechanism of Ulva prolifera disasters follows a “nutrient-dominated, temporally relayed” pattern. Nutrient accumulation (PO4, NO3, SI) from the previous autumn and winter serves as the decisive factor, explaining 86.8% of interannual variation in disaster scale and 56.1% of the variation in first outbreak timing. Light and heat conditions play a secondary modulating role. A clear temporal relay occurs through three distinct stages: the initial outbreak triggered by nutrients, the peak outbreak governed by light–temperature–nutrient synergy, and the system decline characterized by the dissipation of all driving forces. These findings provide a mechanistic basis for developing predictive models and targeted control strategies.

Authors

Institutions

Publication Details

Journal
Remote Sensing
Published
2026-01-06
DOI
https://doi.org/10.3390/rs18020194
Primary Topic
Marine and coastal plant biology
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Analysis of Temporal Cumulative, Lagging Effects and Driving Mechanisms of Environmental Factors on Green Tide Outbreaks: A Case Study of the Ulva Prolifera Disaster in the South Yellow Sea, China

Xinyi Yu, Bangping Deng, Lijia Liu, Huimin Zou et al.
Remote Sensing
Marine and coastal plant biology
article

Analysis of Temporal Cumulative, Lagging Effects and Driving Mechanisms of Environmental Factors on Green Tide Outbreaks: A Case Study of the Ulva Prolifera Disaster in the South Yellow Sea, China

Xinyi Yu, Bangping Deng, Lijia Liu, Huimin Zou, Jian-Hua Zhu, Zhen Tian, Weiwei Li, Zeen Lu, Yating Zhan
article en

Abstract

The Ulva prolifera green tide in the South Yellow Sea has erupted annually for many years, posing significant threats to coastal ecology, the economy, and society. While environmental factors are widely acknowledged as prerequisites for these outbreaks, the asynchrony and complex coupling between their variations and disaster events have challenged traditional studies that rely on instantaneous correlations to uncover the underlying dynamic mechanisms. This study focuses on the Ulva prolifera disaster in the South Yellow Sea, systematically analyzing its spatiotemporal distribution patterns, the temporal accumulation and lag effects of environmental factors, and the coupled driving mechanisms using the Floating Algae Index (FAI). The results indicate that: (1) The disaster shows significant interannual variability, with 2019 experiencing the most severe outbreak. Monthly, the disaster begins offshore of Jiangsu in May, moves northward and peaks in June, expands northward with reduced scale in July, and largely dissipates in August. Years with large-scale outbreaks exhibit higher distribution frequency and broader spatial extent. (2) Environmental factors demonstrate significant accumulation and lag effects on Ulva prolifera disasters, with a mixed temporal mode of both accumulation and lag effects being dominant. Temporal parameters vary across different factors—nutrients generally have longer lag times, while light and temperature factors show longer accumulation times. These parameters change dynamically across disaster stages and display a clear inshore–offshore gradient, with shorter effects in coastal areas and longer durations in offshore waters, revealing significant spatiotemporal heterogeneity in temporal response patterns. (3) The driving mechanism of Ulva prolifera disasters follows a “nutrient-dominated, temporally relayed” pattern. Nutrient accumulation (PO4, NO3, SI) from the previous autumn and winter serves as the decisive factor, explaining 86.8% of interannual variation in disaster scale and 56.1% of the variation in first outbreak timing. Light and heat conditions play a secondary modulating role. A clear temporal relay occurs through three distinct stages: the initial outbreak triggered by nutrients, the peak outbreak governed by light–temperature–nutrient synergy, and the system decline characterized by the dissipation of all driving forces. These findings provide a mechanistic basis for developing predictive models and targeted control strategies.

Remote SensingVol. 18(2)
Tianjin University of Science and Technology (CN), Ministry of Natural Resources (CN), China Centre for Resources Satellite Data and Application (CN), Ministry of Natural Resources (RW), Jiangsu Provincial Institute of Geological Survey (CN), National Center of Ocean Standards and Metrology (CN), State Key Laboratory of Remote Sensing Science (CN)
National Aeronautics and Space Administration, European Centre for Medium-Range Weather Forecasts, Goddard Space Flight Center
Life below water
Openalex Percentile: Top 96%
Marine and coastal plant biology
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.