Impacts of extreme weather events on daily vegetation phenological development in the Lesser Khingan Mountains of China

• We present a model for phenological development under extreme weather stress. • The proposed phenological model improves the temporal resolution to the daily scale. • Excessive dryness, wetness, and coldness all inhibit vegetation growth. • Spring frost events exert the most profound influence on vegetation growth dynamics. Phenological development is intricately linked to climate change, and existing evidence suggests that the frequent occurrence of extreme weather events (EWEs) exerts a more profound influence on phenology. However, most prior studies have primarily focused on revealing the impact of overall extreme weather conditions over a period on specific phenological transition dates. There is an urgent need to obtain short-term, continuous development processes to deeply understand the underlying response mechanisms. In this study, we developed and assessed the Daily Phenological Development Model (DPDM) to explore the continuous influence relationship between remotely sensed vegetation phenology and EWEs in the Lesser Khingan Mountains of northeastern China from 2000 to 2022. The DPDM effectively captures daily vegetation phenological development during both spring and autumn, with R 2 values of 0.84 ± 0.06 and 0.78 ± 0.08, respectively. Additionally, our findings indicate that increased precipitation and more frequent hot events accelerate leaf greening and slow down leaf browning. In contrast, heavy rainfall, frost events and droughts negatively impact vegetation growth. Among these factors, frost events have the most significant inhibitory effect on spring vegetation growth. Furthermore, evergreen needleleaf forests demonstrate the strongest resistance to EWEs in all vegetation types. These insights provide valuable contributions to the accurate assessment and forecasting of vegetation phenology under climate change.

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Publication Details

Journal
Ecological Indicators
Published
2025-02-01
DOI
https://doi.org/10.1016/j.ecolind.2025.113166
Citations
1
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
1.72

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article

Impacts of extreme weather events on daily vegetation phenological development in the Lesser Khingan Mountains of China

Wei Gao, Luhe Wan, Danyao Zhu
1 citations
Ecological Indicators
Remote Sensing in Agriculture
1.72
article

Impacts of extreme weather events on daily vegetation phenological development in the Lesser Khingan Mountains of China

Wei Gao, Luhe Wan, Danyao Zhu
article en
1 citations

Abstract

• We present a model for phenological development under extreme weather stress. • The proposed phenological model improves the temporal resolution to the daily scale. • Excessive dryness, wetness, and coldness all inhibit vegetation growth. • Spring frost events exert the most profound influence on vegetation growth dynamics. Phenological development is intricately linked to climate change, and existing evidence suggests that the frequent occurrence of extreme weather events (EWEs) exerts a more profound influence on phenology. However, most prior studies have primarily focused on revealing the impact of overall extreme weather conditions over a period on specific phenological transition dates. There is an urgent need to obtain short-term, continuous development processes to deeply understand the underlying response mechanisms. In this study, we developed and assessed the Daily Phenological Development Model (DPDM) to explore the continuous influence relationship between remotely sensed vegetation phenology and EWEs in the Lesser Khingan Mountains of northeastern China from 2000 to 2022. The DPDM effectively captures daily vegetation phenological development during both spring and autumn, with R 2 values of 0.84 ± 0.06 and 0.78 ± 0.08, respectively. Additionally, our findings indicate that increased precipitation and more frequent hot events accelerate leaf greening and slow down leaf browning. In contrast, heavy rainfall, frost events and droughts negatively impact vegetation growth. Among these factors, frost events have the most significant inhibitory effect on spring vegetation growth. Furthermore, evergreen needleleaf forests demonstrate the strongest resistance to EWEs in all vegetation types. These insights provide valuable contributions to the accurate assessment and forecasting of vegetation phenology under climate change.

Ecological IndicatorsVol. 171
Harbin University (CN), Harbin Normal University (CN), Mudanjiang Normal University (CN), Institute of Wetland Research (CN)
National Natural Science Foundation of China, Department of Education, Heilongjiang Province, Natural Science Foundation of Heilongjiang Province, Mudanjiang Normal University
Climate action
Openalex Percentile: Top 19%
Remote Sensing in Agriculture
1.72
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