Li et al. (2025) Assessing the impacts of dry-heat extremes on grassland gross primary productivity across mainland China using multi-source remote sensing data

Identification

• Journal: Information Geography
• Year: 2025
• Date: 2025-12-17
• Authors: Xinxin Li, Lin Zhao, Tianning Zhang, Zhijiang Zhang, Shenglei Fu
• DOI: 10.1016/j.infgeo.2025.100038

Research Groups

• College of Geographical Science, Faculty of Geographical Science and Engineering, Henan University, Zhengzhou, Henan, China
• Henan Dabieshan National Field Observation and Research Station of Forest Ecosystem, Henan University, Zhengzhou, Henan, China
• Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Zhengzhou, Henan, China
• School of Resource and Environmental Sciences, Wuhan University, Wuhan, Hubei, China
• School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou, Jiangsu, China

Short Summary

This study investigates how background aridity influences grassland gross primary productivity (GPP) responses to dry-heat extremes across mainland China, and assesses the trade-off between GPP loss rate (G lr) and loss intensity (G li). It finds that compound drought-heatwave events cause greater GPP losses in semi-arid/arid zones but less in humid/semi-humid zones, with soil moisture as the dominant regulator, and that dry-heat extremes significantly decouple the G lr-G li trade-off, exacerbated by increasing aridity.

Objective

• To quantify how GPP loss rate (G lr) and loss intensity (G li) vary under different types of dry-heat extremes (drought events, heatwave events, and compound drought-heatwave events).
• To identify the dominant factors regulating GPP during dry-heat extreme events.
• To determine the spatial trade-off patterns between G lr and G li.
• To elucidate how background aridity modulates grassland GPP responses to dry-heat extremes.

Study Configuration

• Spatial Scale: Grasslands across mainland China.
• Temporal Scale: Daily data from June to August each year, spanning 1985–2018.

Methodology and Data

• Models used: Random forest attribution model, Gamma distribution, Gaussian normal distribution, 7-day moving average, Pearson correlation analysis.
• Data sources: Multi-source daily remote sensing data, including:
  • China Meteorological Forcing Dataset (CMFD) for precipitation, air temperature, downward shortwave radiation, wind speed, and specific humidity (10 km spatial resolution).
  • BESSv2 product for Gross Primary Productivity (GPP) (5 km spatial resolution).
  • Global 30m Fine Classification Land Cover Product (GLC_FCS30) for grassland cover (30 m spatial resolution).
  • Aridity Index (AI) dataset (1 km spatial resolution).
  • Soil Moisture (SMCI1.0) data (10 km spatial resolution, 10 depth layers from 10 cm to 100 cm).
  • SRTM3 for elevation data (90 m spatial resolution).
  • Standardized Precipitation Index (SPI) and Standardized Temperature Index (STI) for identifying dry-heat extremes.

Main Results

• The frequency of compound drought-heatwave events (CDHEs) was significantly lower than that of drought events (DEs) and heatwave events (HEs) across all climate zones.
• CDHEs were more likely to cause grassland GPP deviations from the normal range, resulting in lower GPP normal rates compared to DEs and HEs across all zones.
• GPP loss rate (G lr) and loss intensity (G li) induced by CDHEs increased with increasing background aridity, being lower than DEs and HEs in humid/semi-humid zones but higher in semi-arid/arid zones.
• Increased background aridity significantly amplified G lr under dry-heat extremes; G lr in semi-arid/arid zones increased by 54.67% (DEs), 80.50% (HEs), and 288.11% (CDHEs) compared to humid/semi-humid zones.
• Soil moisture (SM) was the dominant factor regulating GPP under dry-heat extremes, with its regulatory capacity strengthening with increasing background aridity and being most pronounced under CDHEs.
• Dry-heat extremes led to a significant breakdown of the G lr-G li trade-off, and this decoupling was exacerbated by increased background aridity.
• The spatial extent without a G lr-G li trade-off in semi-arid/arid zones increased by 12.18% (DEs), 22.99% (HEs), and 14.10% (CDHEs) compared to humid/semi-humid zones.
• Except in arid zones, CDHEs caused more severe G lr-G li trade-off decoupling compared to DEs and HEs.
• A spatial pattern of grassland vulnerability emerged, transitioning from low G lr and G li in the southwest to high G lr and G li in the northeast, highlighting high-risk areas in the semi-arid zones of North China and transitional zones.

Contributions

• Provides the first systematic investigation into how the spatial heterogeneity of background aridity influences grassland GPP responses to dry-heat extremes across mainland China.
• Reveals the actual state and spatial patterns of the trade-off between GPP loss rate and loss intensity in grasslands under dry-heat extremes at a national scale.
• Demonstrates that, contrary to common assumptions, a widespread absence of a GPP loss rate-loss intensity trade-off exists in Chinese grasslands, particularly in semi-arid North China.
• Offers critical implications for risk management and ecological risk zoning strategies in China, emphasizing the need to consider trade-offs between different resistance metrics influenced by background aridity to avoid underestimating GPP loss risks.
• Establishes a robust theoretical and data foundation for future models exploring the mechanisms behind changes in grassland carbon sink function under intensifying dry-heat extremes.

Funding

• Third Xinjiang Scientific Expedition Program (2022xjkk0601)
• National Natural Science Foundation of China (42471085, 42207449)
• Natural Science Foundation of Hubei Province (2023KZ01323)
• Postdoctoral Fellowship Program of CPSF (GZB20230192)

Citation

@article{Li2025Assessing,
  author = {Li, Xinxin and Zhao, Lin and Zhang, Tianning and Zhang, Zhijiang and Fu, Shenglei},
  title = {Assessing the impacts of dry-heat extremes on grassland gross primary productivity across mainland China using multi-source remote sensing data},
  journal = {Information Geography},
  year = {2025},
  doi = {10.1016/j.infgeo.2025.100038},
  url = {https://doi.org/10.1016/j.infgeo.2025.100038}
}