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<p>This study develops a warming-sensitive and dispersion-flexible spatial panel count data modeling framework to investigate the influence of climatic and topographic drivers of meteorological drought, by explicitly addressing multicollinearity, and spatiotemporal heterogeneity of drought dynamics. Global Moran's I and local indicators of spatial association (LISA) revealed that drought events across Punjab follow a dynamic spatial pattern over time. In general, spatial autocorrelation exhibits an increasing trend from 1981 to 2010, followed by a gradual decline in its magnitude. Lasso regression effectively address multicollinearity and assists variable selection, whereas Cameron &amp; Trivedi's test suggests the presence of significant under-dispersion in drought counts. Subsequently, a spatial COM–Poisson mixed effect modeling framework under frequentist and Bayesian paradigms is e employed through a rigorous suite of performance metrics. Final model identify the temperature (T2M), specific humidity (QV2M), wind speed (WS2M), and wind direction (WD2M) as key drought drivers. Spatial random effects highlight a contrasting display of pronounced spatial heterogeneity and clustering in drought events with drought hotspots concentrated in intensively cropped central and arid northwestern Punjab, while southern Punjab and an alluvial belt connecting Khanewal, Sahiwal, and Faisalabad districts emerged as drought-resilient zones. Additionally, temporal random effects reveal a highly nonlinear and episodic pattern of drought occurrence, characterized by alternating phases of elevated drought intensity during the early 1980s, mid-1990s, early 2000s, late 2000s to early 2010s, and 2019, interspersed with relatively low drought regimes observed during the mid-1980s, late 1980s, early 2000s, mid-2000s, and most years after 2013. The presence of statistically significant multi-year clusters indicates temporal persistence, while abrupt shifts highlight the influence of external climatic shocks. These findings underscore the critical importance of incorporating spatial and temporal heterogeneity in drought modeling and calls for zone-specific adaptation strategies that recognize the climate-induced, spatially clustered and temporally dynamic nature of drought events.</p>

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Improving drought risk assessment using advanced statistical approaches

  • 期刊论文

  • 发表日期：

    2026年6月20日

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  • Rizwan Farooq,
  • Rizwan Niaz,
  • Ijaz Hussain,
  • Hefa Cheng,
  • Huda M. Alshanbari,
  • Hanen Louati,
  • Shreefa O. Hilali

摘要

This study develops a warming-sensitive and dispersion-flexible spatial panel count data modeling framework to investigate the influence of climatic and topographic drivers of meteorological drought, by explicitly addressing multicollinearity, and spatiotemporal heterogeneity of drought dynamics. Global Moran's I and local indicators of spatial association (LISA) revealed that drought events across Punjab follow a dynamic spatial pattern over time. In general, spatial autocorrelation exhibits an increasing trend from 1981 to 2010, followed by a gradual decline in its magnitude. Lasso regression effectively address multicollinearity and assists variable selection, whereas Cameron & Trivedi's test suggests the presence of significant under-dispersion in drought counts. Subsequently, a spatial COM–Poisson mixed effect modeling framework under frequentist and Bayesian paradigms is e employed through a rigorous suite of performance metrics. Final model identify the temperature (T2M), specific humidity (QV2M), wind speed (WS2M), and wind direction (WD2M) as key drought drivers. Spatial random effects highlight a contrasting display of pronounced spatial heterogeneity and clustering in drought events with drought hotspots concentrated in intensively cropped central and arid northwestern Punjab, while southern Punjab and an alluvial belt connecting Khanewal, Sahiwal, and Faisalabad districts emerged as drought-resilient zones. Additionally, temporal random effects reveal a highly nonlinear and episodic pattern of drought occurrence, characterized by alternating phases of elevated drought intensity during the early 1980s, mid-1990s, early 2000s, late 2000s to early 2010s, and 2019, interspersed with relatively low drought regimes observed during the mid-1980s, late 1980s, early 2000s, mid-2000s, and most years after 2013. The presence of statistically significant multi-year clusters indicates temporal persistence, while abrupt shifts highlight the influence of external climatic shocks. These findings underscore the critical importance of incorporating spatial and temporal heterogeneity in drought modeling and calls for zone-specific adaptation strategies that recognize the climate-induced, spatially clustered and temporally dynamic nature of drought events.