Yan et al. (2025) Biophysical feedback from earlier leaf-out enhances nonerosive precipitation in China

Identification

• Journal: Communications Earth & Environment
• Year: 2025
• Date: 2025-11-29
• Authors: Zhuoran Yan, Jiuchun Yang, Han Zhang, Wenbo Li, Yeqiao Wang, Huanjun Liu, Lingxue Yu
• DOI: 10.1038/s43247-025-03054-x

Research Groups

• State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, Jilin 130102, China
• School of Humanities and Law, Northeastern University, Shenyang, 110169, China
• Department of Natural Resources Science, University of Rhode Island, Kingston, RI 02881 USA

Short Summary

This study investigates the impact of earlier vegetation leaf-out on precipitation intensity and water erosion in China using remote sensing, reanalysis data, and a coupled land-atmosphere model. It finds that advanced phenology enhances nonerosive precipitation, particularly in semi-humid temperate regions, while reducing erosive precipitation in sparsely vegetated areas and during summer/autumn, driven by biophysical feedbacks that redistribute atmospheric moisture and energy.

Objective

• To quantify the impact of earlier vegetation leaf-out on precipitation intensity (erosive vs. nonerosive) and its underlying physical mechanisms across China, particularly in temperate regions, to better understand water erosion dynamics.

Study Configuration

• Spatial Scale: China, focusing on seven climatic zones (R1-R7), with a primary emphasis on five temperate regions (R1-R5). Model simulations were performed at a spatial resolution of 25 km × 25 km.
• Temporal Scale: Long-term trends and simulations for the period 2001–2020. Seasonal analysis covered spring (March–May), summer (June–August), and autumn (September–November). Phenological shifts were simulated as a 14-day advancement in leaf-out timing.

Methodology and Data

• Models used:
  • Weather Research and Forecasting (WRF) model (version 4.2)
  • Noah land surface model (Noah-LSM)
  • Modified Mann-Kendall (M-K) test (for temporal trends)
  • Wavelet coherence analysis (for time-frequency relationships)
• Data sources:
  • Remote sensing: Global land surface satellite (GLASS) Leaf Area Index (LAI) product (500 m and 0.05° resolution), GLASS Fractional Vegetation Coverage (FVC) data (0.05° resolution), MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid (version 6.1), GLASS snow-free albedo data (0.05° resolution).
  • Observation/Reanalysis: 1-km monthly precipitation dataset for China (MPDC) (2001–2020), ERA5 6-hourly reanalysis dataset (for WRF boundary conditions).
  • Static geographical data: Soil, land use, and terrain (from WRF USERS PAGE).

Main Results

• Spring Leaf Area Index (LAI) showed an increasing trend across temperate China (37.86% of pixels with significant increase, p < 0.05), with Mid-Temperate Semi-Arid (R1) and Warm Temperate Semi-Humid (R4) regions exhibiting significant increases of 0.032 m²·m⁻²·decade⁻¹ and 0.055 m²·m⁻²·decade⁻¹, respectively. Spring precipitation also increased but with less spatial significance (2.24% of pixels significant).
• A 14-day earlier leaf-out significantly increased spring nonerosive precipitation (< 12 mm·d⁻¹) in temperate semi-arid and semi-humid regions (R1: 1.14 ± 2.78 mm, R2: 3.03 ± 3.66 mm, R4: 1.85 ± 3.36 mm).
• Erosive precipitation (≥ 12 mm·d⁻¹) showed no significant regional increase in spring but declined in sparsely vegetated areas (Fractional Vegetation Coverage < 50%), with a peak average decrease of -61.60 mm in 40-45% FVC regions.
• During summer and autumn, simulated erosive precipitation generally declined across temperate zones and other climate regions, indicating a suppression of extreme precipitation events.
• Physical mechanisms: Earlier leaf-out enhanced spring evapotranspiration (ET) in temperate forest ecosystems, increased vertically integrated moisture flux (VIMF) towards higher latitudes in coastal regions (R2, R4) by 0.56 and 0.46 kg·m⁻¹·s⁻¹ respectively, and elevated convective available potential energy (CAPE) (R1: 11.36 J·kg⁻¹, R2: 18.92 J·kg⁻¹, R4: 17.07 J·kg⁻¹) promoting light-to-moderate precipitation. Precipitable water vapor (PWV) increased in coastal regions (R4: 0.09 ± 0.04 mm).
• Time-lagged effects: Autumn ET generally reduced, and PWV decreased in mid-high latitudes, suggesting premature depletion of soil moisture.

Contributions

• This study provides a novel quantification of the differential impact of vegetation phenological advancement on erosive versus nonerosive precipitation intensity, which is crucial for accurate water erosion assessment.
• It isolates vegetation feedback from climatic forcing using a coupled land-atmosphere model, offering a process-based understanding of how earlier leaf-out redistributes precipitation patterns.
• The findings demonstrate that advanced phenology can mitigate potential increases in erosive precipitation, particularly in sparsely vegetated areas, challenging previous assumptions that greening universally intensifies precipitation.
• It decodes the physical pathways (ET, VIMF, CAPE, PWV) through which phenological shifts influence the hydrological cycle, including time-lagged effects on autumn precipitation and moisture availability.
• The research highlights the necessity for conservation planning to integrate bidirectional vegetation-climate feedback and the spatial heterogeneity of precipitation responses, moving beyond simple surface interception effects.

Funding

• National Key R&D Program of China (No. 2024YFD1501600)
• Strategic Priority Research Program (A) of the Chinese Academy of Sciences (XDA28080503)
• Youth Innovation Promotion Association of Chinese Academy of Sciences (2023240)
• Jilin Province Changbai Elite Talent Program (202441137)

Citation

@article{Yan2025Biophysical,
  author = {Yan, Zhuoran and Yang, Jiuchun and Zhang, Han and Li, Wenbo and Wang, Yeqiao and Liu, Huanjun and Yu, Lingxue},
  title = {Biophysical feedback from earlier leaf-out enhances nonerosive precipitation in China},
  journal = {Communications Earth & Environment},
  year = {2025},
  doi = {10.1038/s43247-025-03054-x},
  url = {https://doi.org/10.1038/s43247-025-03054-x}
}