Impacts of extreme weather events on daily vegetation phenological development in the Lesser Khingan Mountains of China
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...
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Elsevier
2025-02-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X25000950 |
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| author | Danyao Zhu Luhe Wan Wei Gao |
| author_facet | Danyao Zhu Luhe Wan Wei Gao |
| author_sort | Danyao Zhu |
| collection | DOAJ |
| description | 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 R2 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. |
| format | Article |
| id | doaj-art-1834b7e830da42779cf0a92bb311224e |
| institution | DOAJ |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-1834b7e830da42779cf0a92bb311224e2025-08-20T02:43:32ZengElsevierEcological Indicators1470-160X2025-02-0117111316610.1016/j.ecolind.2025.113166Impacts of extreme weather events on daily vegetation phenological development in the Lesser Khingan Mountains of ChinaDanyao Zhu0Luhe Wan1Wei Gao2Department of Geography, College of History and Culture, Mudanjiang Normal University, Mudanjiang 157012, ChinaHeilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Harbin Normal University, Harbin 150025, China; Heilongjiang Wuyiling Wetland Ecosystem National Observation and Research Station, Yichun 153000, China; Corresponding author at: No. 1, Shida Road, Limin Economic Development Zone, Harbin 157012, China.Department of Computer, Harbin Finance University, Harbin 150030, ChinaPhenological 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 R2 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.http://www.sciencedirect.com/science/article/pii/S1470160X25000950Vegetation phenologyExtreme weather eventsBayesian hierarchical modelEVI2 |
| spellingShingle | Danyao Zhu Luhe Wan Wei Gao Impacts of extreme weather events on daily vegetation phenological development in the Lesser Khingan Mountains of China Ecological Indicators Vegetation phenology Extreme weather events Bayesian hierarchical model EVI2 |
| title | Impacts of extreme weather events on daily vegetation phenological development in the Lesser Khingan Mountains of China |
| title_full | Impacts of extreme weather events on daily vegetation phenological development in the Lesser Khingan Mountains of China |
| title_fullStr | Impacts of extreme weather events on daily vegetation phenological development in the Lesser Khingan Mountains of China |
| title_full_unstemmed | Impacts of extreme weather events on daily vegetation phenological development in the Lesser Khingan Mountains of China |
| title_short | Impacts of extreme weather events on daily vegetation phenological development in the Lesser Khingan Mountains of China |
| title_sort | impacts of extreme weather events on daily vegetation phenological development in the lesser khingan mountains of china |
| topic | Vegetation phenology Extreme weather events Bayesian hierarchical model EVI2 |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X25000950 |
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