Seifian et al. (2025) Investigating meteorological drought propagation to soil moisture drought: insights from Iran’s diverse climate regions

Identification

• Journal: Theoretical and Applied Climatology
• Year: 2025
• Date: 2025-12-13
• Authors: Zahra Seifian, Farhad Hooshyaripor, Bahram Saghafian, Rasoul Mirabbasi
• DOI: 10.1007/s00704-025-05924-y

Research Groups

• Department of Civil Engineering, SR.C, Islamic Azad University, Tehran, Iran
• Department of Water Engineering, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran
• Water Sciences and Hydroinformatics Research Center, Khazar University, Mahsati str. 41, Baku, AZ 1096, Azerbaijan

Short Summary

This study investigates the propagation of meteorological drought to soil moisture drought across Iran's diverse climate regions using the Standardized Precipitation Index (SPI) and Soil Moisture Deficit Index (SMDI) with copula functions, revealing a 28–40% probability of their co-occurrence and regional variations in their relationship.

Objective

• To examine meteorological and soil moisture droughts across Iran, assess their severity, spatial distribution, and temporal dynamics, determine the lag time between their onset, and model the dependency between SPI and SMDI using copula functions to estimate joint probabilities of co-occurring droughts.

Study Configuration

• Spatial Scale: Iran (1,648,195 km²), divided into 8 homogeneous precipitation regions, with soil moisture data from 8 meteorological stations used for bias correction.
• Temporal Scale: 2000–2023 (24 years) for satellite precipitation and soil moisture data; drought indices calculated at 3-month and 6-month scales.

Methodology and Data

• Models used:
  • Standardized Precipitation Index (SPI) for meteorological drought.
  • Soil Moisture Deficit Index (SMDI) for soil moisture drought.
  • Copula functions (Archimedean and Gaussian families, including Joe, Frank, Normal, Clayton, Gumbel) for multivariate dependency modeling.
  • Statistical analyses: Pearson's correlation coefficient, Kendall's tau, Maximum Likelihood Estimation (MLE), Inference Function for Margins (IFM).
  • Goodness-of-fit tests: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), correlation coefficient (r), Mean Absolute Error (MAE), Nash-Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE).
  • Quantile mapping for bias correction of satellite data.
• Data sources:
  • Precipitation: Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) satellite dataset (0.05 degree spatial resolution).
  • Soil Moisture: AVHRR-3 sensor on NOAA-15 satellite (1.1 km spatial resolution) and SMAP satellite data (aggregated to monthly averages).
  • Ground-based observations: Soil moisture data from 8 meteorological stations in Iran for validation and bias correction.
  • Platform: Google Earth Engine for data processing.

Main Results

• A 28–39% probability of simultaneous meteorological and soil moisture droughts was found over 3-month periods, and 29–40% over 6-month periods.
• The probability of co-occurrence decreased as drought severity increased.
• The highest probability of simultaneous drought occurrence was in region G2 (38% for 3-month, 40% for 6-month scales), while the lowest was in region G6 (25% for 3-month, 26% for 6-month scales).
• Meteorological drought (SPI) was moderately correlated with soil moisture drought (SMDI), with statistically significant relationships (p-values < 0.05) observed across most lags, peaking at 1- to 3-month lags.
• The relationship between meteorological and soil moisture drought varied across Iran's diverse climate regions, being weak or non-existent in central desert regions (e.g., G1) and in heavily forested regions (e.g., G6).
• Normal distribution was generally the best fit for SPI, while normal or logistic distributions were best for SMDI, depending on the region.
• Different copula functions (Joe, Frank, Normal, Clayton, Gumbel) were found to be the best fit for different regions, reflecting diverse precipitation characteristics and drought responses.

Contributions

• Pioneering assessment of the risk of co-occurrence of meteorological and soil moisture droughts in Iran using satellite-based products and copula functions.
• Quantification of the joint probability of these two drought types using a rigorous statistical framework, enhancing understanding of their interdependence.
• Provides a valuable framework for drought planning, management, and early warning systems by identifying high-risk regions and informing irrigation scheduling.
• Highlights the importance of probabilistic drought assessments for enhancing preparedness and adaptive strategies in water resource management and agriculture.
• Offers a methodological foundation for future research linking other types of extreme events through probabilistic analysis.

Funding

• The authors received no specific funding for this work.

Citation

@article{Seifian2025Investigating,
  author = {Seifian, Zahra and Hooshyaripor, Farhad and Saghafian, Bahram and Mirabbasi, Rasoul},
  title = {Investigating meteorological drought propagation to soil moisture drought: insights from Iran’s diverse climate regions},
  journal = {Theoretical and Applied Climatology},
  year = {2025},
  doi = {10.1007/s00704-025-05924-y},
  url = {https://doi.org/10.1007/s00704-025-05924-y}
}