Ma et al. (2025) Impacts of Triple La Niña Events on Forest Gross Primary Productivity in China from 2020 to 2022

Identification

• Journal: ˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences
• Year: 2025
• Date: 2025-11-26
• Authors: Wensi Ma, Qiaoli Wu, Wei He, Jie Jiang
• DOI: 10.5194/isprs-archives-xlviii-4-w14-2025-227-2025

Research Groups

• School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing, China
• Zhejiang Institute of Carbon Neutrality Innovation, Zhejiang University of Technology, Hangzhou, Zhejiang, China
• International Institute of Earth System Science, Nanjing University, Nanjing, Jiangsu, China

Short Summary

This study investigated the impact of the 2020-2022 triple La Niña event on China's forest Gross Primary Productivity (GPP), revealing an initial inhibition followed by gradual recovery, with distinct regional and seasonal variations driven by climatic factors.

Objective

• To systematically explore the impact mechanism of the 2020-2022 triple La Niña event on Gross Primary Productivity (GPP) of forest ecosystems in China and its underlying physiological and ecological driving processes.

Study Configuration

• Spatial Scale: National scale for China's forest ecosystems, with detailed analysis across five climatic regions (tropical monsoon, subtropical monsoon, temperate monsoon, temperate continental, and plateau mountain climate forest areas). Data resolutions were processed to 0.5° for analysis.
• Temporal Scale: The primary study period for La Niña impact is 2020-2022, with a baseline period of 2017-2018. Detrending analysis was performed over 2015-2022. Data sources cover periods from 1901 to 2024.

Methodology and Data

• Models used:
  • Linear regression for detrending annual GPP to account for CO₂ fertilization effect.
  • STL (Seasonal-Trend decomposition using Loess) for seasonal and monthly anomaly analysis.
  • Anomaly calculation (deviation from mean).
  • Z-Score standardization for cross-regional comparisons.
  • SHAP (SHapley Additive exPlanations) analysis to attribute feature contributions.
• Data sources:
  • GOSIF GPP (Global, OCO-2-based SIF-derived Gross Primary Productivity) dataset (0.05° spatial resolution, 8-day/monthly time steps, processed to 0.5°).
  • MODIS MCD12Q1 land cover data (500 m resolution, processed to 0.5°).
  • ERA5-Land reanalysis data for precipitation, air temperature, and soil moisture at root zone (SMroot) (0.1° spatial resolution, monthly temporal resolution, resampled to 0.5°).
  • CRUJRA v2.4 dataset (0.5° spatial resolution, 6-hour temporal resolution) for calculating Vapor Pressure Deficit (VPD).

Main Results

• China's forest GPP exhibited an "initial inhibition-gradual recovery" dynamic during the 2020-2022 triple La Niña event.
• The national average annual GPP decreased from 1799.27 Tg C year⁻¹ (2017-2018 baseline) to 1783.89 Tg C year⁻¹ during 2020-2022 (a 0.86% decrease).
• GPP reached its lowest value of 1763.68 Tg C year⁻¹ in 2020, then steadily recovered to 1804.54 Tg C year⁻¹ in 2022.
• Spatially, the subtropical monsoon climate region (contributing 63.7% of total GPP) showed a "V" shaped recovery, while the temperate monsoon climate region (24.7% contribution) displayed a unique "anti-phase" response, being inhibited by soil moisture at root zone and phenological delay.
• Regional differences are primarily due to the latitudinal regulation of La Niña on the East Asian monsoon system: the temperate zone is influenced by mid-high latitude atmospheric circulation, while the subtropical zone directly responds to tropical sea surface temperature (SST) anomalies causing continuous drought.
• Seasonal GPP fluctuations were significantly larger in spring and summer than in autumn and winter. The temperate zone showed "early stage determines late stage" characteristics (e.g., May low temperature impacting July GPP peak), while the subtropical zone exhibited a "spring and autumn compensation" pattern.

Contributions

• Clarified the complex multi-scale response mechanism of China's forest ecosystems to the 2020-2022 triple La Niña event.
• Provided a scientific basis for improving ecosystem model parameterization and predicting carbon sink function evolution under global change.
• Offered new physiological insights into climate adaptation thresholds in forest ecosystems.
• Enhanced understanding of phenological parameterization in ecosystem models by revealing seasonal dynamics and adaptive strategies of different vegetation types.

Funding

• National Natural Science Foundation of China (Grant No. 42201381)
• Program of Beijing Scholar

Citation

@article{Ma2025Impacts,
  author = {Ma, Wensi and Wu, Qiaoli and He, Wei and Jiang, Jie},
  title = {Impacts of Triple La Niña Events on Forest Gross Primary Productivity in China from 2020 to 2022},
  journal = {˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences},
  year = {2025},
  doi = {10.5194/isprs-archives-xlviii-4-w14-2025-227-2025},
  url = {https://doi.org/10.5194/isprs-archives-xlviii-4-w14-2025-227-2025}
}