Predicting net primary productivity response to multiple extreme climate drivers in Inner Mongolia

作者：Rihan Su, Enliang Guo, Lan Wu, Yongfang Wang, Xiangqian Li, Peng Wang, Licong Dai, Zhongyi Sun · 发表于：Environmental Research Letters · 年份：2025 · DOI：10.1088/1748-9326/ae2d78 · 被引用次数：2 · 研究领域：Remote Sensing in Agriculture、Tree-ring climate responses、Plant Water Relations and Carbon Dynamics

Abstract Extreme climate events (ECEs) are increasingly frequent in Inner Mongolia (IM), threatening vegetation productivity. While previous studies have examined vegetation responses to individual historical ECEs, knowledge of future vegetation dynamics under combined extreme climate stressors remains limited. Here, we simulated historical net primary productivity (NPP) (1982–2020) using the Carnegie–Ames–Stanford model, identified key extreme climate factors through geographical detector method (GDM), and projected future NPP variations (2023–2100) under different climate scenarios using partial least squares regression. Results showed that 72.3% of IM experienced increasing NPP during 1982–2020, with 28.2% showing significant increase, mainly in northeastern and eastern regions. Consecutive wet days, heavy precipitation days, cold nights, and maximum temperature were identified as key contributors to NPP changes, with explanatory powers of 0.903, 0.781, 0.704 and 0.630, respectively. Projections under SSP2-4.5 and SSP5-8.5 scenarios indicated NPP increases in southern IM, while eastern and western regions showed significant decreases under combined extreme climate stressors. This study provides an assessment of future NPP responses to ECEs in IM. The findings highlight vulnerable regions requiring targeted management strategies and warrant integrating multiple extreme climate factors into vegetation prediction models.