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<p>Drought is a natural hazard that affects various sectors across the globe. It could not be stopped, but early mitigation policies based on the analysis of previous drought event patterns could reduce their drastic effects. Therefore, the study mainly focused on providing an efficient and practical framework for analyzing the drought events in various regions based on the proposed multi-regional weighted aggregative standardized precipitation index (MRWASPI). Initially, the proposed framework suggested using hierarchical clustering to identify homogeneous regions based on their precipitation temporal pattern. The next step of the framework aggregates the regional precipitations of each cluster using a weighted aggregation scheme; further, the weighted aggregated data is used to calculate the proposed index, i.e., MRWASPI. The last step of the proposed framework is based on non-homogeneous Poisson processes (NHPP), which are used to analyze the accumulated drought events for each cluster. This novel framework integrates a weighted aggregative index with Bayesian inference and change-point modeling, providing a unique approach for spatiotemporal drought analysis that surpasses existing methodologies in precision and regional adaptability. For this purpose, the accumulated drought events over time were calculated. Different existing parametric forms depending on time and unknown parameters are assumed for the intensity/rate function ß(t), t ≥ 0 of the NHPP. In the present context, the Poisson events of interest are the number of months that the MRWASDI has exceeded a given threshold of interest, i.e., MRWASDI ≤-1. Two versions of the NHPP model are considered in the study, one without change points and the other with a change point. The parameters included in the model are estimated using the Bayesian approach with the standard Markov chain Monte Carlo (MCMC) method under Gibbs sampling. For the validity of our proposed framework, monthly precipitation records for 41 years (1981–2021) of 136 districts of Pakistan are considered. The results revealed the significance of the proposed framework and provided expressive insights regarding the spatiotemporal drought severity and its pattern for a homogeneous group of regions.</p>

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A framework for spatiotemporal drought analysis using proposed multi-regional weighted aggregative SPI and Bayesian inference

  • 期刊论文

  • 发表日期：

    2025年10月17日

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  • Asad Ellahi,
  • Shreefa O. Hilali,
  • Jorge Alberto Achcar,
  • Ijaz Hussain,
  • Maysaa Elmahi Abd Elwahab,
  • Abdulkareem M. Basheer

摘要

Drought is a natural hazard that affects various sectors across the globe. It could not be stopped, but early mitigation policies based on the analysis of previous drought event patterns could reduce their drastic effects. Therefore, the study mainly focused on providing an efficient and practical framework for analyzing the drought events in various regions based on the proposed multi-regional weighted aggregative standardized precipitation index (MRWASPI). Initially, the proposed framework suggested using hierarchical clustering to identify homogeneous regions based on their precipitation temporal pattern. The next step of the framework aggregates the regional precipitations of each cluster using a weighted aggregation scheme; further, the weighted aggregated data is used to calculate the proposed index, i.e., MRWASPI. The last step of the proposed framework is based on non-homogeneous Poisson processes (NHPP), which are used to analyze the accumulated drought events for each cluster. This novel framework integrates a weighted aggregative index with Bayesian inference and change-point modeling, providing a unique approach for spatiotemporal drought analysis that surpasses existing methodologies in precision and regional adaptability. For this purpose, the accumulated drought events over time were calculated. Different existing parametric forms depending on time and unknown parameters are assumed for the intensity/rate function ß(t), t ≥ 0 of the NHPP. In the present context, the Poisson events of interest are the number of months that the MRWASDI has exceeded a given threshold of interest, i.e., MRWASDI ≤-1. Two versions of the NHPP model are considered in the study, one without change points and the other with a change point. The parameters included in the model are estimated using the Bayesian approach with the standard Markov chain Monte Carlo (MCMC) method under Gibbs sampling. For the validity of our proposed framework, monthly precipitation records for 41 years (1981–2021) of 136 districts of Pakistan are considered. The results revealed the significance of the proposed framework and provided expressive insights regarding the spatiotemporal drought severity and its pattern for a homogeneous group of regions.