Work

Response of Vegetation to Extreme Climate in the Yellow River Basin: Spatiotemporal Patterns, Lag Effects, and Scenario Differences

Year: 2025

Type: article

Abstract: Extreme climates pose increasing threats to ecosystems, particularly in ecologically fragile regions such as the Yellow River Basin (YRB). Leaf area index (LAI) reflects vegetation response to climatic stressors, yet spatiotemporal dynamics of such responses under future climate scenarios remain poorly understood. This study examined LAI responses to extreme climatic factors across the YRB from 2025 to 2065, utilizing Coupled Model Intercomparison Project Phase 6 (CMIP6) outputs under three Shared Socioeconomic Pathways (SSP) scenarios. Partial least squares regression was performed using historical consistency-validated and future scenario LAI data alongside 26 extreme climate indices to identify extreme climate impacts on vegetation dynamics. Time-lag and cumulative effect analyses using Pearson correlation further quantified the potential impacts of extreme climate on future vegetation dynamics. Results indicate that the regionally averaged LAI in the YRB exhibits a consistent increasing trend under all three SSP scenarios, with linear rates of 0.0016–0.0020 yr−1 and the highest values under SSP5-8.5, accompanied by clear scenario-dependent spatial differences in LAI distribution and vegetation response to extreme climates, particularly in the lag and cumulative effects that depend on local hydro-climatic conditions. Partial least squares regression results identified annual total wet-day precipitation, frost days, growing season length, summer days, and ice days as the dominant extreme climate indices regulating LAI variability. In the arid and semiarid Loess Plateau regions, relatively long lag and cumulative effects imply vegetation vulnerability to delayed or prolonged climatic stress, necessitating enhanced soil and water conservation practices. These findings support region-specific ecological conservation and climate mitigation strategies for the YRB and other ecologically vulnerable watersheds.

Source: Remote Sensing

DOI: 10.3390/rs17243967

Authors: Shilun Zhou, Feiyang Wang, Ruiting Lyu, Maosheng Liu, Ning Nie

Institutions: China University of Petroleum, East China, Lanzhou University

Language: English

Topic: Remote Sensing in Agriculture

Subfield: Ecology

Field: Environmental Science

Domain: Physical Sciences

SDGs: Climate action, Life on land

Open Access status: gold

Estimated APC paid: $3,258