Shah et al. (2025) Integrating time–space dynamics for meteorological drought monitoring and trend analysis

Identification

• Journal: Acta Geophysica
• Year: 2025
• Date: 2025-12-10
• Authors: Zahoor A. Shah, Rizwan Niaz, Mohammed M. A. Almazah, Hefa Cheng, Fathia Moh. Al Samman, Shreefa O. Hilali
• DOI: 10.1007/s11600-025-01738-8

Research Groups

• Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan
• School of Energy and Environmental Science, Yunnan Normal University, Kunming, China
• Department of Mathematics, College of Sciences and Arts (Muhyil), King Khalid University, Muhyil, Saudi Arabia
• College of Urban and Environmental Sciences, Peking University, Beijing, China
• Department of Mathematics, College of Science, Northern Border University, Arar, Saudi Arabia
• Department of Mathematics, College of Sciences and Arts (Majardah), King Khalid University, Magardah, Saudi Arabia

Short Summary

This study developed a Composite Integrated Meteorological Drought Index (CIMDI) by integrating SPI, SPEI, and SPTI using a hybrid weighting scheme (steady-state probabilities and mean squared correlation) to provide a more robust and spatially/temporally adaptive meteorological drought assessment in Punjab, Pakistan. CIMDI demonstrated superior statistical performance and identified significant increasing drought trends in several stations.

Objective

• To develop a Composite Integrated Meteorological Drought Index (CIMDI) by combining Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Standardized Precipitation Temperature Index (SPTI) using a hybrid weighting scheme (steady-state probabilities and mean squared correlation) to provide a robust, balanced, and integrated measure for meteorological drought assessment, accounting for time-space dynamics.

Study Configuration

• Spatial Scale: Punjab region of Pakistan, covering 21 meteorological stations.
• Temporal Scale: 41 years (January 1981–December 2021) of monthly climatic data.

Methodology and Data

• Models used:
  • Composite Integrated Meteorological Drought Index (CIMDI)
  • Standardized Precipitation Index (SPI)
  • Standardized Precipitation Evapotranspiration Index (SPEI)
  • Standardized Precipitation Temperature Index (SPTI)
  • Hybrid weighting scheme combining Steady-state (SS) probabilities (for temporal weights) and Mean Squared Correlation (MSC) (for spatial weights).
  • Statistical tests: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Bias Error (MBE), Standard Error of Estimate (SEE), Efficiency (EF), Correlation coefficient (r, R), Concordance (D), Confidence level (C), and Mann–Kendall trend test.
  • R package Propagate for probability distribution selection (based on Bayesian Information Criterion).
• Data sources: Monthly meteorological data (precipitation, minimum and maximum temperature) from 21 stations in Punjab, Pakistan, obtained from the POWER Data Access Viewer of NASA (satellite-based reanalysis data).

Main Results

• CIMDI consistently achieved lower error values compared to individual indices, with an RMSE of 0.34 (compared to SPEI 0.98 and SPTI 0.41 at Gujrat station), MAE of 0.41 (compared to SPEI 1.44 and SPTI 0.47 at Jhang station), and SEE of 0.34 (compared to SPTI 0.41 and SPEI 0.98 at Gujrat station).
• CIMDI demonstrated substantially higher efficiency (EF = 0.39 at Faisalabad station) compared to negative values obtained from SPEI (-0.77) and SPTI (-0.76).
• The confidence level for CIMDI reached 0.38 (and 0.84 at Faisalabad in conclusion), indicating higher reliability in capturing real drought conditions.
• CIMDI showed strong correlations with SPI (0.80–0.94) and SPTI (0.89–0.97), and significant correlations with SPEI (0.39–0.77), reflecting its integrative strength.
• Mann–Kendall trend analysis of CIMDI revealed statistically significant increasing drought trends at Sargodha (p = 0.001), Rawalpindi (p = 0.0022), Jhang (p = 0.0126), and Bhakkar (p = 0.0311), indicating increasing drought severity in these areas.
• CIMDI provided smoother transitions between drought classes, less noise in classification, and no abrupt shifts compared to individual indices, proving to be spatially adaptive across arid, semi-arid, and humid zones.

Contributions

• Development of CIMDI, a novel composite meteorological drought index that effectively integrates SPI, SPEI, and SPTI using a hybrid weighting scheme based on steady-state probabilities (temporal dynamics) and mean squared correlation (spatial dynamics).
• Addresses the limitations of conventional single-parameter drought indices by providing a more robust, balanced, and spatially/temporally adaptive assessment of meteorological drought.
• Demonstrates superior statistical performance (lower error metrics, higher efficiency, and confidence) compared to existing individual indices.
• Offers a more realistic and comprehensive representation of drought conditions, enhancing drought monitoring, early warning systems, and climate-resilient planning.
• Identifies specific regions in Punjab, Pakistan, experiencing statistically significant increasing trends in drought occurrence, highlighting areas of growing vulnerability.

Funding

• Deanship of Research and Graduate Studies at King Khalid University (major research project grant number RGP.2/145/46).
• Deanship of Scientific Research at Northern Border University, Arar, Saudi Arabia (project number NBU-FFR-2025-1324-08).

Citation

@article{Shah2025Integrating,
  author = {Shah, Zahoor A. and Niaz, Rizwan and Almazah, Mohammed M. A. and Cheng, Hefa and Samman, Fathia Moh. Al and Hilali, Shreefa O.},
  title = {Integrating time–space dynamics for meteorological drought monitoring and trend analysis},
  journal = {Acta Geophysica},
  year = {2025},
  doi = {10.1007/s11600-025-01738-8},
  url = {https://doi.org/10.1007/s11600-025-01738-8}
}