Yasin et al. (2025) Spatially integrated standardized relative humidity index: A principal component analysis-based approach for regional drought assessment

Identification

• Journal: Theoretical and Applied Climatology
• Year: 2025
• Date: 2025-11-11
• Authors: Ahad Yasin, Sadia Qamar, Samina Satti, Naim Ahmad, Zulfiqar Ali, Amna Nazeer
• DOI: 10.1007/s00704-025-05885-2

Research Groups

• Department of Statistics, University of Sargodha, Sargodha, Pakistan.
• Department of Statistics, University of Wah, Rawalpindi, Pakistan.
• College of Computer Science, King Khalid University, Abha, Saudi Arabia.
• College of Statistical Sciences, University of the Punjab, Lahore, Pakistan.
• Department of Mathematics, COMSATS University Islamabad, Lahore Campus, Pakistan.

Short Summary

This study introduces the Multivariate Standardized Relative Humidity Index (MSRHI), a novel drought assessment tool that integrates relative humidity data from multiple stations using Principal Component Analysis (PCA). The index provides a more stable and spatially coherent representation of regional drought conditions across Pakistan's diverse climatic zones compared to traditional station-based univariate indices.

Objective

• To develop a robust, regionally adaptive drought monitoring framework that emphasizes the role of relative humidity (RH) in atmospheric moisture and evapotranspiration dynamics.
• To address the limitations of station-based indices by using PCA to extract dominant regional drought signals and minimize localized noise.

Study Configuration

• Spatial Scale: Regional (Pakistan), encompassing 25 meteorological stations categorized into five climatic zones: Coastal, Arid, Semi-Arid, Humid, and Semi-Humid.
• Temporal Scale: 43 years (1981–2023) of monthly relative humidity records (516 months).

Methodology and Data

• Models used: Principal Component Analysis (PCA) for dimensionality reduction and spatial integration; 32 univariate probability distributions (including Gamma, Weibull, Normal, Johnson SB, and Triangular) for statistical standardization.
• Data sources: Monthly relative humidity (RH) time series from 25 meteorological stations across Pakistan.
• Statistical Approach:
  1. Fitting 32 candidate distributions to station-level RH data and selecting the optimal model via the Bayesian Information Criterion (BIC).
  2. Generating the Standardized Relative Humidity Index (SRHI) for individual stations.
  3. Applying PCA to SRHI series within each climatic zone to extract the first principal component (PC1).
  4. Standardizing PC1 to construct the final MSRHI.

Main Results

• Variance Explanation: The first principal component (PC1) captured the dominant regional signal, explaining 88.19% of the variance in the Humid zone, 66.09% in the Coastal zone, and 47.94% in the Arid zone.
• Distributional Fit: Optimal probability distributions varied by region: Johnson SB for Coastal, Triangular for Arid and Humid, Johnson SU for Semi-Arid, and Normal for Semi-Humid zones.
• Index Performance: MSRHI demonstrated higher stability and lower volatility than individual station-based indices (SRHIs), effectively filtering out local anomalies to provide a clearer regional drought signal.
• Spatial Coherence: Strong inter-station correlations were observed in Humid and Coastal zones, while Arid and Semi-Arid zones showed higher localized heterogeneity, justifying the use of PCA to extract shared patterns.

Contributions

• Novel Variable Integration: Shifts the focus of drought assessment toward relative humidity, a critical but often overlooked driver of atmospheric dryness and soil moisture retention.
• Methodological Innovation: Introduces a PCA-based multivariate framework that integrates multiple station records into a single regional index, improving the reliability of early warning systems in data-scarce or climatically diverse regions.
• Statistical Robustness: Employs an extensive distribution-fitting process (32 models) rather than assuming a fixed distribution (e.g., Gamma), ensuring the index is statistically tailored to specific regional climatic behaviors.

Funding

• This research was funded by the Deanship of Research and Graduate Studies at King Khalid University through the Large Research Project under grant number RGP2/417/46.

Citation

@article{Yasin2025Spatially,
  author = {Yasin, Ahad and Qamar, Sadia and Satti, Samina and Ahmad, Naim and Ali, Zulfiqar and Nazeer, Amna},
  title = {Spatially integrated standardized relative humidity index: A principal component analysis-based approach for regional drought assessment},
  journal = {Theoretical and Applied Climatology},
  year = {2025},
  doi = {10.1007/s00704-025-05885-2},
  url = {https://doi.org/10.1007/s00704-025-05885-2}
}

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