Gao et al. (2025) 1 km HILDA + based land cover/use map time series of China under 1.5 °C climate of this century

Identification

• Journal: Scientific Data
• Year: 2025
• Date: 2025-12-11
• Authors: Yifan Gao, Xian Zhang Feng, Changqing Song, Yuanhui Wang, Sijing Ye, Min Zhao, Delin Fang, Peichao Gao
• DOI: 10.1038/s41597-025-06411-9

Research Groups

• State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Faculty of Geographical Science, Beijing Normal University, Beijing, China

Short Summary

This study generated a 1 km resolution land cover/use map time series for China from 2015 to 2100, under a 1.5 °C climate scenario incorporating Nationally Determined Contributions (NDCs), by integrating the GCAM and Land-N2N models with the accurate HILDA+ baseline map. The resulting dataset provides crucial scientific guidance for land management in addressing climate crises.

Objective

• To forecast high-resolution (1 km) land cover/use map time series for China from 2015 to 2100 under a 1.5 °C climate scenario that accounts for Nationally Determined Contributions (NDCs), providing scientific guidance for land management and climate pledge adjustments.

Study Configuration

• Spatial Scale: China, divided into 24 water basins, with land cover/use maps at 1 km spatial resolution.
• Temporal Scale: Forecasts from 2015 to 2100 at 10-year intervals (except for the 2015 to 2020 period).

Methodology and Data

• Models used:
  • Global Change Analysis Model (GCAM): Used to quantify land demands under future scenarios.
  • Land-N2N model: Used to simulate spatially explicit land cover/use changes based on demands and driving factors, employing a combined coarser-grained and finer-grained iteration mechanism.
  • Random Forest models: Used within Land-N2N to calculate local suitability based on land cover/use change and driving factors.
• Data sources:
  • HIstoric Land Dynamics Assessment+ (HILDA+) land cover/use maps: Baseline land cover/use maps for 2005 and 2015 at 1 km resolution, harmonized from multiple global, regional, and national maps and calibrated with FAOSTAT national land statistics.
  • Driving factors: Seven categories (soil, socio-economic, accessibility, agriculture/vegetation, terrain, climate, and livestock) resampled to 1 km resolution, centered around 2015.
  • GCAM model outputs: Land areas (demands) from 1990 to 2100 at five-year intervals for each water basin, specifically using the 1.5 °C climate scenario (Iyer, et al. 2, scenario 7) which assumes adherence to NDCs.

Main Results

• Ten 1 km resolution land cover/use maps of China were generated for the period 2015-2100 under the 1.5 °C climate scenario considering NDCs.
• The framework's performance, evaluated by simulating land cover/use changes from 2005 to 2015, showed an average Kappa coefficient of 94.71% and an average Figure of Merit (FoM) of 14.43% across 24 water basins.
• The lowest Kappa coefficient (89.85%) in the study exceeded all Kappa coefficients reported for other similar land cover/use maps in comparative literature.
• A comparison of the 2100 land cover/use map with another similar map (Lv, et al. 18) under the same scenario showed a 70.15% agreement in spatial patterns.
• The largest discrepancies were observed in pasture/rangeland and unmanaged grass/shrubland types (1.46 × 10^6 km^2 difference, 15.42%) and forest (0.55 × 10^6 km^2 difference, 5.81%).

Contributions

• Provides a novel 1 km resolution land cover/use map time series for China, offering higher spatial detail than previous studies (e.g., 5 km).
• Enhances the reliability of land cover/use forecasts by utilizing the highly accurate HILDA+ dataset as a baseline.
• Captures detailed time series information of land cover/use changes at 10-year intervals, which is crucial for understanding non-linear land dynamics under climate pledges.
• Integrates NDCs into future land cover/use projections, making the forecasts more relevant and actionable for decision-makers in adjusting climate pledges compared to scenarios based solely on SSPs or RCPs.
• Offers essential data support and valuable insights for land management and the adjustment of NDCs to address climate crises.

Funding

• National Natural Science Foundation of China (Grant Nos. 42271418 and 42230106)
• Fundamental Research Funds for the Central Universities

Citation

@article{Gao20251,
  author = {Gao, Yifan and Feng, Xian Zhang and Song, Changqing and Wang, Yuanhui and Ye, Sijing and Zhao, Min and Fang, Delin and Gao, Peichao},
  title = {1 km HILDA + based land cover/use map time series of China under 1.5 °C climate of this century},
  journal = {Scientific Data},
  year = {2025},
  doi = {10.1038/s41597-025-06411-9},
  url = {https://doi.org/10.1038/s41597-025-06411-9}
}