Zarei (2026) A dynamic eco-hydrological drought framework integrating soil moisture memory, human pressure, and vegetation–atmosphere coupling under a non-stationary climate

Identification

• Journal: The Science of The Total Environment
• Year: 2026
• Date: 2026-08-18
• Authors: Abdol Rassoul Zarei
• DOI: 10.1016/j.scitotenv.2026.182207

Research Groups

• Department of Range and Watershed Management (Nature engineering), College of Agriculture, Fasa University, Iran.

Short Summary

The study introduces the Dynamic Eco-hydrological Drought Index (DEHDI), a process-based framework that integrates soil moisture memory, human pressure, and $\text{CO}_2$ effects to detect terrestrial water stress more accurately than traditional meteorological indices.

Objective

• To develop and validate a dynamic eco-hydrological drought framework (DEHDI) that incorporates vegetation–atmosphere coupling, recursive soil moisture memory, anthropogenic pressure, and $\text{CO}_2$-mediated physiological regulation to overcome the limitations of meteorological-only indices (e.g., SPI and SPEI).

Study Configuration

• Spatial Scale: 25 climatically diverse stations across Iran.
• Temporal Scale: 1982–2024.

Methodology and Data

• Models used: Dynamic Eco-hydrological Drought Index (DEHDI) optimized via Elastic Net regularization; performance evaluated using 10-fold cross-validation, Area Under the ROC Curve (AUC), Matthews Correlation Coefficient (MCC), and Heidke Skill Score (HSS).
• Data sources: TerraClimate-derived soil moisture reference dataset, Normalized Difference Vegetation Index (NDVI), vapor pressure deficit, and a Human Activity Proxy (HAP).

Main Results

• DEHDI significantly outperformed conventional indices in detecting terrestrial stress, achieving a mean AUC of 0.84, compared to 0.65 for SPEI and 0.61 for SPI.
• The highest improvements in detection skill were observed in arid and semi-arid regions.
• Ablation studies confirmed that recursive soil moisture memory and anthropogenic pressure are critical diagnostic drivers of the index.
• The integration of physiological $\text{CO}_2$ adjustment successfully eliminated thermal noise in the drought indexing process.

Contributions

• Provides a robust early warning tool for human-impacted landscapes by transitioning from simple meteorological anomaly detection to a process-based approach that accounts for eco-hydrological feedbacks and non-stationary climate factors.

Funding

• Not specified in the provided text.

Citation

@article{Zarei2026dynamic,
  author = {Zarei, Abdol Rassoul},
  title = {A dynamic eco-hydrological drought framework integrating soil moisture memory, human pressure, and vegetation–atmosphere coupling under a non-stationary climate},
  journal = {The Science of The Total Environment},
  year = {2026},
  doi = {10.1016/j.scitotenv.2026.182207},
  url = {https://doi.org/10.1016/j.scitotenv.2026.182207}
}