Niaz et al. (2025) BAMPP: A novel Bayesian network enhanced by average marginal posterior probabilities to identify critical ground truth meteorological stations for drought monitoring

Identification

• Journal: Physics and Chemistry of the Earth Parts A/B/C
• Year: 2025
• Date: 2025-11-27
• Authors: Rizwan Niaz, Sarfraz Munir, Ahmad Raza, Rıfat Tür, Sadegh Partani, Ali Danandeh Mehr
• DOI: 10.1016/j.pce.2025.104215

Research Groups

• School of Energy and Environmental Science, Yunnan Normal University, Kunming, Yunnan, China
• Department of Statistics, Kohsar University Murree, Murree, Pakistan
• Department of Computer Science, Federal Urdu University of Arts, Sciences and Technology, Islamabad, Pakistan
• Department of Civil Engineering, Akdeniz University, Antalya, Türkiye
• Civil Engineering Department, University of Bojnord, Bojnord, Iran
• Civil Engineering Department, Antalya Bilim University, Antalya, Türkiye

Short Summary

This study introduces BAMPP, a novel Bayesian network enhanced by Average Marginal Posterior Probabilities, to identify critical meteorological stations for regional drought monitoring based on the Standardized Precipitation Index (SPI) at multiple timescales, demonstrating its effectiveness in Ankara, Türkiye. The method revealed distinct spatiotemporal patterns, with critical stations varying seasonally for short-term droughts but Beypazari consistently being key for medium- and long-term droughts.

Objective

• To introduce and demonstrate a novel Bayesian network enhanced by Average Marginal Posterior Probabilities (BAMPP) for identifying critical meteorological stations essential for analyzing spatiotemporal dynamics of drought events at a regional scale.

Study Configuration

• Spatial Scale: Province of Ankara, Türkiye
• Temporal Scale: Multiple timescales for drought indices (3-month, 6-month, and 12-month SPI), analyzed seasonally and across all months.

Methodology and Data

• Models used: Bayesian network enhanced by Average Marginal Posterior Probabilities (BAMPP), Standardized Precipitation Index (SPI).
• Data sources: Historical observations from meteorological stations, used to calculate the frequency, severity, and persistence of SPI at multiple timescales.

Main Results

• The analysis revealed distinct spatiotemporal patterns of drought across the Ankara region at all time scales.
• For short-term droughts (SPI-3), the identity of critical stations varied seasonally, indicating localized dynamics; for example, Esenboga was key in February, March, July, October, and December, while Beypazari was influential in other months.
• For medium- and long-term droughts (SPI-6, SPI-12), Beypazari was consistently identified as the most critical station across all months, establishing it as the representative station for long-term drought monitoring in the region.

Contributions

• Introduces BAMPP, a novel Bayesian network enhanced by Average Marginal Posterior Probabilities, as a new approach for identifying critical meteorological stations.
• Provides a robust, probabilistic tool for optimizing drought monitoring networks.
• Enhances regional water resource management by identifying key stations for effective drought monitoring.

Funding

• Not explicitly mentioned in the provided text.

Citation

@article{Niaz2025BAMPP,
  author = {Niaz, Rizwan and Munir, Sarfraz and Raza, Ahmad and Tür, Rıfat and Partani, Sadegh and Mehr, Ali Danandeh},
  title = {BAMPP: A novel Bayesian network enhanced by average marginal posterior probabilities to identify critical ground truth meteorological stations for drought monitoring},
  journal = {Physics and Chemistry of the Earth Parts A/B/C},
  year = {2025},
  doi = {10.1016/j.pce.2025.104215},
  url = {https://doi.org/10.1016/j.pce.2025.104215}
}