Biophysical feedback from earlier leaf-out enhances nonerosive precipitation in China
Abstract Earlier leaf-out enhances spring moisture exchange between land and atmosphere. However, its impact on precipitation intensity remains unclear, hampering dynamic monitoring on water erosion. Here we combined remote sensing, reanalysis data, and coupled land-atmosphere model to address this knowledge gap. Results observed an increasing trend in spring precipitation accompanied by earlier leaf-out across temperate China. The simulations isolated the phenological impacts and attributed it to the enhancement of nonerosive precipitation. The mid-temperate semi-humid region exhibited the largest increase (3.03 ± 3.66 mm) in response to 14-days leaf-out advancement. Conversely, erosive precipitation declined in sparsely vegetated areas (<50% cover), sharpening the heterogeneity of moisture gradient. Elevated convective available potential energy promoted more frequent precipitation, inhibiting the deep moisture accumulation required for erosive downpours. Simulated erosive precipitation also declined in temperate summer and autumn. Findings highlight vegetation’s role in precipitation redistribution beyond surface interception, requiring conservation planning to consider bidirectional vegetation-climate feedback.
Authors
- Wenbo Li (ORCID: https://orcid.org/0000-0003-2498-0499)
- Lingxue Yu (ORCID: https://orcid.org/0000-0002-5565-535X)
- Jiuchun Yang (ORCID: https://orcid.org/0000-0001-6211-0289)
- Han Zhang (ORCID: https://orcid.org/0000-0003-2253-8605)
- Huanjun Liu (ORCID: https://orcid.org/0000-0003-1392-0407)
- Zhuoran Yan
- Yeqiao Wang
Institutions
- University of Rhode Island (US)
- Chinese Academy of Sciences (CN)
- Northeast Institute of Geography and Agroecology (CN)
- Louisiana Department of Natural Resources (US)
- Northeastern University (CN)
Publication Details
- Journal
- Communications Earth & Environment
- Published
- 2025-11-29
- DOI
- https://doi.org/10.1038/s43247-025-03054-x
- Citations
- 4
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 6.87
Funders
- Chinese Academy of Sciences
- People's Government of Jilin Province
- Youth Innovation Promotion Association of the Chinese Academy of Sciences
- Oak Ridge Institute for Science and Education
- Youth Innovation Promotion Association