Niaz et al. (2025) Assessing seasonal drought persistence using a Bayesian logistic regression approach

Identification

• Journal: Physics and Chemistry of the Earth Parts A/B/C
• Year: 2025
• Date: 2025-12-17
• Authors: Rizwan Niaz, Ahmad Raza, Zulfiqar Ali, Abdelkader T. Ahmed, Mir Jafar Sadegh Safari, Ali Danandeh Mehr
• DOI: 10.1016/j.pce.2025.104253

Research Groups

• School of Energy and Environment Science, Yunnan Normal University, Kunming, China
• Department of Computer Science, Federal Urdu University of Arts, Science and Technology Islamabad, Islamabad, Pakistan
• College of Statistical Sciences, University of the Punjab, Lahore, Punjab, Pakistan
• Civil Engineering Department, Faculty of Engineering, Islamic University of Madinah, Al Madinah, Saudi Arabia
• Department of Geography and Environmental Studies, Toronto Metropolitan University, Toronto, Ontario, Canada
• Department of Civil Engineering, Yas¸ar University, Izmir, Turkey
• Department of Civil Engineering, Antalya Bilim University, Antalya, Turkey

Short Summary

This study investigates meteorological drought patterns and intraseasonal predictability using a Bayesian Logistic Regression approach on 52 years of precipitation data from Ankara Province, Türkiye, revealing that drought frequency and persistence range from 40% to 90% and identifying areas vulnerable to drought persistence between successive seasons.

Objective

• To assess the patterns and intraseasonal predictability of meteorological drought by exploring the frequency and persistence of drought events.

Study Configuration

• Spatial Scale: Six meteorology stations in Ankara Province, Türkiye.
• Temporal Scale: 52 years of precipitation measurements.

Methodology and Data

• Models used: Standardized Precipitation Index (SPI) at a 3-month accumulation period (SPI-3), single variable Bayesian Logistic Regression.
• Data sources: 52 years of precipitation measurements from meteorology stations.

Main Results

• The frequency and intraseasonal persistence of meteorological drought events range from 40% to 90% in the studied region.
• Specific areas, including Beypazari, Nallihan, and Kizilcahamam, were identified as particularly vulnerable to drought and more prone to experiencing drought persistence between successive seasons.
• A negative correlation was found between spring drought occurrences and winter SPI-3 records, indicating increased exposure to drought persistence from winter to spring.
• The study demonstrated reduced vulnerability to drought persistence during the transition from summer to fall.

Contributions

• Provides a robust probabilistic framework for assessing drought persistence.
• Contributes to improving drought risk management strategies in the region.

Funding

• Not explicitly stated in the provided text.

Citation

@article{Niaz2025Assessing,
  author = {Niaz, Rizwan and Raza, Ahmad and Ali, Zulfiqar and Ahmed, Abdelkader T. and Safari, Mir Jafar Sadegh and Mehr, Ali Danandeh},
  title = {Assessing seasonal drought persistence using a Bayesian logistic regression approach},
  journal = {Physics and Chemistry of the Earth Parts A/B/C},
  year = {2025},
  doi = {10.1016/j.pce.2025.104253},
  url = {https://doi.org/10.1016/j.pce.2025.104253}
}