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<p>Drought is a critical climate hazard that threatens agriculture, ecosystems, and water security, particularly in climatically sensitive regions such as the Tibetan (TP) Plateau. Accurate projection of future drought characteristics is essential for effective mitigation and adaptation strategies. However, existing approaches often suffer from uncertainties due to variability among climate models and inadequate representation of precipitation extremes. To address these challenges, we propose the Kling–Gupta hybrid weighted ensemble (KG-HWE), a novel two-phase ensemble weighting framework. The framework integrates historical model performance with divergence-based weights using the Kling–Gupta efficiency (KGE) metric, enhancing the reliability of drought projections from a multi-model ensemble of eighteen Coupled Model Coupled Model Intercomparison Project Phase 6(CMIP6) General Circulation Models (GCMs). Additionally, steady-state probabilities are estimated using a Markov chain approach to evaluate the long-term likelihood of different drought classes under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Results indicate that the KG-HWE consistently outperforms traditional methods, achieving a lower average Normalized root mean square error (NRMSE = 1.990), reduced normalized relative absolute error (NRAE = 1.484), and higher correlation (0.670) with observed data compared to equal weighted averaging and mutual information weighting. Probabilistic analysis further reveals a marked increase in severe and near-extreme drought probabilities under the high-emission SSP5- 8.5 scenario, highlighting heightened long-term drought risks. Overall, the proposed KG-HWE framework provides a robust tool for improved drought characterization and prediction, supporting climate adaptation and sustainable water resource management in regions with complex hydro-climatic conditions.</p>

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Development of Kling–Gupta Hybrid Weighted Ensemble for Improving Future Projections of Drought Characterization Under Different Climate Change Scenarios

  • 期刊论文

  • 发表日期：

    2025年11月28日

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  • Nosheen Amjad,
  • Muhammad Ismail,
  • Zulfiqar Ali

摘要

Drought is a critical climate hazard that threatens agriculture, ecosystems, and water security, particularly in climatically sensitive regions such as the Tibetan (TP) Plateau. Accurate projection of future drought characteristics is essential for effective mitigation and adaptation strategies. However, existing approaches often suffer from uncertainties due to variability among climate models and inadequate representation of precipitation extremes. To address these challenges, we propose the Kling–Gupta hybrid weighted ensemble (KG-HWE), a novel two-phase ensemble weighting framework. The framework integrates historical model performance with divergence-based weights using the Kling–Gupta efficiency (KGE) metric, enhancing the reliability of drought projections from a multi-model ensemble of eighteen Coupled Model Coupled Model Intercomparison Project Phase 6(CMIP6) General Circulation Models (GCMs). Additionally, steady-state probabilities are estimated using a Markov chain approach to evaluate the long-term likelihood of different drought classes under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Results indicate that the KG-HWE consistently outperforms traditional methods, achieving a lower average Normalized root mean square error (NRMSE = 1.990), reduced normalized relative absolute error (NRAE = 1.484), and higher correlation (0.670) with observed data compared to equal weighted averaging and mutual information weighting. Probabilistic analysis further reveals a marked increase in severe and near-extreme drought probabilities under the high-emission SSP5- 8.5 scenario, highlighting heightened long-term drought risks. Overall, the proposed KG-HWE framework provides a robust tool for improved drought characterization and prediction, supporting climate adaptation and sustainable water resource management in regions with complex hydro-climatic conditions.