Rabbia et al. (2026) Framework for spatiotemporal soil moisture assessment: an application to the integration of model-based clustering with non-parametric change points detection

Identification

• Journal: Scientific Reports
• Year: 2026
• Date: 2026-08-01
• Authors: Waleeja Tur Rabbia, Asad Ellahi, Mohamed A. E. Khalefa, Walaa A. A. Ismael, Ijaz Hussain, Paulo Canas Rodrigues
• DOI: 10.1038/s41598-026-61805-y

Research Groups

• Department of Statistics, Quaid-I-Azam University, Pakistan
• Department of Community Medicine and JWMC Office, Wah Medical College, National University of Medical Sciences, Pakistan
• College of Business, Imam Mohammad Ibn Saud Islamic University (IMSIU), Saudi Arabia
• Department of Statistics, Federal University of Bahia, Brazil
• Department of Business Management, University of Pretoria, South Africa

Short Summary

The study proposes a three-phase statistical framework combining model-based clustering and non-parametric change point detection to analyze spatiotemporal soil moisture variability in Punjab, Pakistan.

Objective

• To develop an integrated framework for multi-regional soil moisture assessment capable of identifying homogeneous spatial groups and detecting significant temporal shifts within those groups.

Study Configuration

• Spatial Scale: 36 districts of Punjab, Pakistan.
• Temporal Scale: 42 years (January 1981 to November 2022; 503 months).

Methodology and Data

• Models used: Model-Based Clustering (MBC) with Bayesian Information Criterion (BIC), Bootstrapping (for time series construction), Non-Parametric Change Point (NPCP) detection, and the Kolmogorov–Smirnov (KS) test.
• Data sources: Monthly-averaged soil moisture observations.

Main Results

• Spatial Clustering: The MBC identified five homogeneous soil moisture clusters, with the optimal number of clusters determined by a BIC value of -10995.2.
• Temporal Shifts: NPCP analysis revealed significant temporal variability; Cluster 2 exhibited the highest instability with 18 change points, while the remaining clusters showed between 4 and 6 shifts.
• Distributional Analysis: The KS test confirmed substantial distributional differences between adjacent time segments following the detected change points.

Contributions

• Provides a robust, integrated statistical framework for soil moisture assessment that is particularly valuable for data-scarce and climate-sensitive regions.
• Enhances the ability to support agricultural planning, irrigation management, and climate adaptation strategies through precise spatiotemporal monitoring.

Funding

• Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU), grant number IMSIU-DDRSP2602.

Citation

@article{Rabbia2026Framework,
  author = {Rabbia, Waleeja Tur and Ellahi, Asad and Khalefa, Mohamed A. E. and Ismael, Walaa A. A. and Hussain, Ijaz and Rodrigues, Paulo Canas},
  title = {Framework for spatiotemporal soil moisture assessment: an application to the integration of model-based clustering with non-parametric change points detection},
  journal = {Scientific Reports},
  year = {2026},
  doi = {10.1038/s41598-026-61805-y},
  url = {https://doi.org/10.1038/s41598-026-61805-y}
}