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Characterizing Inter-Seasonal Meteorological Drought Using Random Effect Logistic Regression

Author

Listed:
  • Anwar Hussain

    (Department of Statistics, Quaid-I-Azam University, Islamabad 45320, Pakistan)

  • Masoud Reihanifar

    (Department of Civil and Environmental Engineering, University of California, Berkeley, CA 94720, USA
    Department of Civil and Environmental Engineering, Barcelona TECH, Technical University of Catalonia (UPC), 08034 Barcelona, Spain)

  • Rizwan Niaz

    (Department of Statistics, Quaid-I-Azam University, Islamabad 45320, Pakistan
    Department of Statistics, Kohsar University Murree, Murree 47150, Pakistan)

  • Olayan Albalawi

    (Department of Statistics, Faculty of Science, University of Tabuk, Tabuk 47512, Saudi Arabia)

  • Mohsen Maghrebi

    (Department of Civil Engineering, University of Gonabad, Gonabad 9691957678, Iran)

  • Abdelkader T. Ahmed

    (Civil Engineering Department, Faculty of Engineering, Islamic University of Madinah, Al Madinah 42351, Saudi Arabia)

  • Ali Danandeh Mehr

    (Department of Civil Engineering, Antalya Bilim University, 07191 Antalya, Türkiye
    MEU Research Unit, Middle East University, Amman 11831, Jordan)

Abstract

Sustainable watershed development focuses on building resilience to drought through better water resource management, ecosystem protection, and adaptation strategies. In this study, the spatiotemporal dynamics and inter-seasonal characteristics of meteorological drought across Ankara Province, Turkey, were investigated and compared using a conditional fixed effect logistic regression model (CFELogRM) and a random effect logistic regression model (RELogRM). To assess the statistical validity and effectiveness of these models, we conducted significance tests, including the log-likelihood ratio chi-square, and Wald chi-square tests. The obtained p -values associated with both the RELogRM and CFELogRM models for the selected seasons demonstrate their statistical significance. Additionally, we conducted the Hausman test (HT) to compare the efficiency of the RELogRM and CFELogRM models. Remarkably, the results of the HT suggest that RELogRM is the optimal model for modeling fall-to-winter season drought dynamics across the study area. Notably, the significant coefficient derived from RELogRM indicates a statistically significant negative correlation between spring moisture conditions and the probability of summer droughts. Specifically, the odds ratio of 0.2416 reflects a 24.16% reduction in the likelihood of transitioning to a higher drought category, emphasizing the crucial role of antecedent moisture conditions in influencing drought propensity.

Suggested Citation

  • Anwar Hussain & Masoud Reihanifar & Rizwan Niaz & Olayan Albalawi & Mohsen Maghrebi & Abdelkader T. Ahmed & Ali Danandeh Mehr, 2024. "Characterizing Inter-Seasonal Meteorological Drought Using Random Effect Logistic Regression," Sustainability, MDPI, vol. 16(19), pages 1-20, September.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:19:p:8433-:d:1487581
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    References listed on IDEAS

    as
    1. Dilayda Soylu Pekpostalci & Rifat Tur & Ali Danandeh Mehr & Mohammad Amin Vazifekhah Ghaffari & Dominika Dąbrowska & Vahid Nourani, 2023. "Drought Monitoring and Forecasting across Turkey: A Contemporary Review," Sustainability, MDPI, vol. 15(7), pages 1-23, March.
    2. Rizwan Niaz & Xiang Zhang & Nouman Iqbal & Mohammed M.A. Almazah & Tajammal Hussain & Ijaz Hussain & Feng Li, 2021. "Logistic Regression Analysis for Spatial Patterns of Drought Persistence," Complexity, Hindawi, vol. 2021, pages 1-13, November.
    3. Xiaoliang Xie & Bingqi Xie & Jiaqi Cheng & Qi Chu & Thomas Dooling, 2021. "A simple Monte Carlo method for estimating the chance of a cyclone impact," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(3), pages 2573-2582, July.
    4. Lirong Yin & Lei Wang & Tingqiao Li & Siyu Lu & Jiawei Tian & Zhengtong Yin & Xiaolu Li & Wenfeng Zheng, 2023. "U-Net-LSTM: Time Series-Enhanced Lake Boundary Prediction Model," Land, MDPI, vol. 12(10), pages 1-18, September.
    5. Veysel Gumus & Oguz Simsek & Yavuz Avsaroglu & Berivan Agun, 2021. "Spatio‐temporal trend analysis of drought in the GAP Region, Turkey," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 109(2), pages 1759-1776, November.
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