Ma et al. (2026) Quantitative Assessment of Drought Impact on Grassland Productivity in Inner Mongolia Using SPI and Biome-BGC

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Diversity
• Year: 2026
• Date: 2026-01-09
• Authors: Yunjia Ma, Tianjie Lei, Jiabao Wang, Zhitao Lin, Hang Li, Baoyin Liu
• DOI: 10.3390/d18010036

Research Groups

Not specified in the provided text.

Short Summary

This study developed and validated a novel hybrid modeling framework to quantify the interactive effects of drought intensity and duration on net primary productivity (NPP) across Inner Mongolia's grasslands. The framework significantly outperforms conventional models, revealing that drought duration is a stronger driver of productivity decline than intensity, with desert grasslands being the most vulnerable.

Objective

• To develop and validate a novel hybrid modeling framework to quantify drought impacts on net primary productivity (NPP) across Inner Mongolia’s major grasslands, explicitly capturing the compounded, nonlinear influence of combined drought intensity and duration.

Study Configuration

• Spatial Scale: Inner Mongolia’s major grasslands (meadow, typical, desert grassland types).
• Temporal Scale: 1961–2012.

Methodology and Data

• Models used: Biome-BGC model (for ecosystem productivity simulation), Standardized Precipitation Index (SPI) (for drought characterization), and a novel hybrid modeling framework (integrating linear and nonlinear components to capture drought intensity and duration effects).
• Data sources: Specific data sources (e.g., satellite, observation, reanalysis) for model inputs are not detailed in the provided text.

Main Results

• The novel hybrid model substantially outperforms linear and nonlinear models alone, yielding highly significant regression equations for all grassland types (p < 0.001).
• Independent validation confirmed the hybrid model's robustness and high predictive skill (Nash-Sutcliffe Efficiency (NSE) ≈ 0.868, Root Mean Square Error (RMSE) = 20.09 gC/m²/yr).
• Drought duration is a stronger driver of productivity decline than instantaneous drought intensity.
• Desert grasslands are the most vulnerable to drought-induced productivity loss, followed by typical grasslands and then meadow grasslands.

Contributions

• Development of a novel hybrid modeling framework that explicitly captures the compounded, nonlinear influence of combined drought intensity and duration, representing a significant advance over conventional single-perspective approaches.
• Provides a practical tool for estimating site-specific productivity loss, directly informing grassland management priorities, adaptive grazing strategies, and early-warning system design.
• Offers a transferable methodology for assessing drought-induced vulnerability in biodiverse ecosystems, supporting conservation and climate-adaptive management.

Funding

Not specified in the provided text.

Citation

@article{Ma2026Quantitative,
  author = {Ma, Yunjia and Lei, Tianjie and Wang, Jiabao and Lin, Zhitao and Li, Hang and Liu, Baoyin},
  title = {Quantitative Assessment of Drought Impact on Grassland Productivity in Inner Mongolia Using SPI and Biome-BGC},
  journal = {Diversity},
  year = {2026},
  doi = {10.3390/d18010036},
  url = {https://doi.org/10.3390/d18010036}
}