Estimating extreme values drought risk through classical and Bayesian paradigm

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 utilize the generalized extreme value distribution to characterize the distribution of standardized precipitation index and non-stationary standardized precipitation index 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 represents an innovative 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 neighboring regions.