Work

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

Year: 2025

Type: article

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.

Source: Ecological Indicators

DOI: 10.1016/j.ecolind.2025.113166

Authors: Danyao Zhu, Luhe Wan, Wei Gao

Institutions: Mudanjiang Normal University, Harbin Normal University, Harbin Finance University

Language: English

Topic: Remote Sensing in Agriculture

Subfield: Ecology

Field: Environmental Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $3,650