2024/12/05 by Touqeer Ahmad, Safoorah Sabir, Irshad Ahmad Arshad +2 · 1 citation
Agricultural and Biological Sciences · Environmental Science · #Climate change impacts on agriculture #Climate variability and models #Hydrology and Drought Analysis
paper · pdf · doi:10.1002/joc.8705
openalex publication_date 2024/12/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
ABSTRACT Drought poses significant challenges to both the environment and the economy, necessitating proactive mitigation strategies. This study introduces both classical and Bayesian Markov Chain Monte Carlo (MCMC) extreme value probabilistic models for quantifying drought risk. The models utilise the generalised extreme value (GEV) distribution to characterise the distribution of standardised precipitation index (SPI) and non‐stationary standardised precipitation index (NSSPI) variables. Drought risk is probabilistically assessed across five regions in Baluchistan (a drought‐prone area of Pakistan) over two 20‐year periods per region. The study presents a novel approach in probabilistic quantification models, demonstrating slight performance improvement with the Bayesian MCMC paradigm, as evaluated by the continuously ranked probability scoring. Moreover, the application of the presented methodology can be extended to other climatic zones using Bayesian MCMC with informative priors constructed from historical records of the neighbouring regions.