Liu et al. (2026) Responses of forests, cultivated lands, and grasslands to climatic factors at a basin scale

Identification

• Journal: Theoretical and Applied Climatology
• Year: 2026
• Date: 2026-04-10
• Authors: Cancan Liu, Shiqi Yao, Yongqin David Chen, Md Lokman Hossain, Jianfeng Li
• DOI: 10.1007/s00704-026-06181-3

Research Groups

• Guangzhou College of Technology and Business, China
• Hunan Institute of Advanced Technology, China
• Department of Geography and Resource Management, The Chinese University of Hong Kong, China
• School of Humanities and Social Science, The Chinese University of Hong Kong, Shenzhen, China

Short Summary

This study investigated vegetation dynamics and their responses to key climatic factors (temperature, precipitation, wind, and solar radiation) across forests, cultivated lands, and grasslands in the humid East River basin from 2000 to 2018, revealing a significant basin-wide greening trend (0.006 per year) primarily driven by temperature, with differentiated sensitivities among vegetation types.

Objective

• To identify the spatial and temporal characteristics of vegetation change in the East River basin (ERb) region during the period 2000–2018.
• To explore the correlations between Normalized Difference Vegetation Index (NDVI) and key climate factors (temperature, precipitation, wind, and solar radiation) for each vegetation type (forest, cultivated land, and grassland) across wet, dry, and annual periods over nineteen years.

Study Configuration

• Spatial Scale: East River basin (ERb), China, with a drainage area of 27,040 square kilometers.
• Temporal Scale: 2000–2018 for vegetation dynamics and climate factor correlations; 1960–2018 for long-term temperature and precipitation trends.

Methodology and Data

• Models used: Pearson correlation analysis, Mann-Kendall (M-K) test statistics.
• Data sources:
  • Normalized Difference Vegetation Index (NDVI): Terra Moderate Resolution Imaging Spectroradiometer (MODIS MOD13A1, version 6) at 500 meters spatial resolution, 16-day L3 surface product (2000–2018).
  • Meteorological data (precipitation, temperature, radiation hours, wind, daily maximum temperature, daily minimum temperature): National Meteorological Information Center of China Meteorological Administration (CMA) and several meteorological stations in the ERb (2000–2018; 1960–1999 for long-term temperature and precipitation).
  • Land use datasets: Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC) at 1 kilometer spatial resolution (2000, 2005, 2010, 2015).

Main Results

• The East River basin (ERb) experienced a significant "greening effect" from 2000 to 2018, with the annual Normalized Difference Vegetation Index (NDVI) increasing at a rate of 0.006 per year. This greening was more pronounced in the dry season (0.0067 per year) than in the wet season (0.0054 per year).
• Forests and cultivated lands showed higher NDVI increasing rates (0.0053 per year and 0.0050 per year, respectively) compared to grasslands (0.0039 per year). Forests maintained the highest NDVI values in both wet (peak: 0.74 in 2017) and dry seasons (peak: 0.71 in 2017).
• Temperature was identified as the dominant climatic driver, exhibiting significant positive correlations with NDVI for all vegetation types (forests, cultivated lands, grasslands) across annual, wet, and dry periods. Cultivated lands showed the strongest sensitivity to temperature. The annual average temperature in the ERb increased significantly by 1.6% (0.023 °C per year after 1974) from 1960 to 2018.
• Precipitation showed an insignificant upward trend (1.1%) from 1960 to 2018. It significantly benefited only cultivated lands (positive correlation with annual NDVI, r = 0.306), but had non-significant relationships with forests and grasslands.
• Solar radiation exhibited significant positive effects on both seasonal and annual NDVI across all land use types, with stronger correlations during the wet season. It was more strongly associated with forests and grasslands than with cultivated land.
• Wind showed a slight positive correlation with cultivated land during the wet season (r = 0.234) but non-significant relationships for forests and grasslands.
• A sharp decrease in NDVI values was observed between 2003 and 2005 across all land use types, attributed to prolonged extreme dry events during that period.
• Spatially, higher NDVI values were observed in the northern ERb, while lower values were found in the southwestern and midwestern urban areas. The proportion of areas with high NDVI (≥ 0.8) increased significantly from 2010 (1.942%) to 2015 (13.721%), indicating a conversion from bare soil to forested land, particularly in the urban region of the southwestern ERb.

Contributions

• Addresses critical research gaps by providing a vegetation-type-specific analysis of climate-vegetation interactions, including seasonal dynamics (especially dry periods) and divergent sensitivities of co-located vegetation types in a humid, monsoon-driven basin.
• Strengthens the understanding of how different vegetation types respond to climate variability in humid regions, which are increasingly experiencing climatic extremes.
• Offers targeted insights for sustainable agricultural management and regional conservation and land-use planning under climate change.
• Highlights that in humid regions, hydrological factors like soil moisture deficits and evaporative demand can override the background benefits of CO₂ fertilization during periods of climatic stress.

Funding

• National Natural Science Foundation of China (42071055)
• Research Grants Council of the Hong Kong Special Administrative Region, China (CUHK12301220 and RFS2223-2H02)
• Hunan Provincial Natural Science Foundation of China (2025JJ60253)

Citation

@article{Liu2026Responses,
  author = {Liu, Cancan and Yao, Shiqi and Chen, Yongqin David and Hossain, Md Lokman and Li, Jianfeng},
  title = {Responses of forests, cultivated lands, and grasslands to climatic factors at a basin scale},
  journal = {Theoretical and Applied Climatology},
  year = {2026},
  doi = {10.1007/s00704-026-06181-3},
  url = {https://doi.org/10.1007/s00704-026-06181-3}
}