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<p>Increasing uncertainty in global climate presents significant challenges in assessing extreme climate hazards, particularly drought episodes. Global Climate Models (GCMs) are essential for drought assessment; however, their performance inconsistencies over longer timescales remain a challenge. This study employs the Maximum Overlap Discrete Wavelet Transform (MODWT) with Daubechies (db6) as the mother wavelet function to decompose precipitation data across multiple scales. GCM performance is evaluated using multiple error metrics at each scale. To address the non-stationary nature of precipitation variability, a Multi-Scale Wavelet Ensemble (MSWE) is developed to enhance the ensemble performance. Additionally, this study introduces a novel drought index, the Wavelet-Based Multi-Scale Gaussian Drought Index (WMS-GDI), for future drought characterization. WMS-GDI integrates multi-scale weights with the K-Component Gaussian Mixture Model (K-CGMM) normalization framework, improving probabilistic drought assessment. The analysis is based on monthly precipitation data from 22 GCMs across 94 grid points in Pakistan. The proposed MSWE achieves the highest Kling-Gupta Efficiency (KGE) value (0.4884) compared to competing frameworks over the historical period (1950–2014). Future drought trends are evaluated using three Shared Socioeconomic Pathways (SSPs)—SSP1-2.6, SSP2-4.5, and SSP5-8.5—for the period 2015–2100. Findings under WMS-GDI reveal an increased risk of dry periods in the second half of the century, with extreme dryness intensifying over long timescales (24 and 48 months) under SSP5-8.5. The steady-state average likelihood change in Extreme Drought (ED) is 0.0081, 0.0079, and 0.0096 for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively, indicating a heightened risk of drought toward the end of the 21st century. In general, this study presents a robust framework for capturing multi-scale climate variability. The findings from this paper provide valuable insights for policymakers, enabling more effective planning to mitigate the long-term impacts of drought on water resources.</p>

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Integrating multi-scale precipitation variability into ensemble modeling using maximum overlap discrete wavelet transform for improved drought projections under emission scenarios

  • 期刊论文

  • 发表日期：

    2026年1月28日

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  • Hussnain Abbas,
  • Zulfiqar Ali,
  • Muhamad Shakeel,
  • Muhamad Mohsin,
  • Amna Nazeer,
  • Sadia Qamar

摘要

Increasing uncertainty in global climate presents significant challenges in assessing extreme climate hazards, particularly drought episodes. Global Climate Models (GCMs) are essential for drought assessment; however, their performance inconsistencies over longer timescales remain a challenge. This study employs the Maximum Overlap Discrete Wavelet Transform (MODWT) with Daubechies (db6) as the mother wavelet function to decompose precipitation data across multiple scales. GCM performance is evaluated using multiple error metrics at each scale. To address the non-stationary nature of precipitation variability, a Multi-Scale Wavelet Ensemble (MSWE) is developed to enhance the ensemble performance. Additionally, this study introduces a novel drought index, the Wavelet-Based Multi-Scale Gaussian Drought Index (WMS-GDI), for future drought characterization. WMS-GDI integrates multi-scale weights with the K-Component Gaussian Mixture Model (K-CGMM) normalization framework, improving probabilistic drought assessment. The analysis is based on monthly precipitation data from 22 GCMs across 94 grid points in Pakistan. The proposed MSWE achieves the highest Kling-Gupta Efficiency (KGE) value (0.4884) compared to competing frameworks over the historical period (1950–2014). Future drought trends are evaluated using three Shared Socioeconomic Pathways (SSPs)—SSP1-2.6, SSP2-4.5, and SSP5-8.5—for the period 2015–2100. Findings under WMS-GDI reveal an increased risk of dry periods in the second half of the century, with extreme dryness intensifying over long timescales (24 and 48 months) under SSP5-8.5. The steady-state average likelihood change in Extreme Drought (ED) is 0.0081, 0.0079, and 0.0096 for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively, indicating a heightened risk of drought toward the end of the 21st century. In general, this study presents a robust framework for capturing multi-scale climate variability. The findings from this paper provide valuable insights for policymakers, enabling more effective planning to mitigate the long-term impacts of drought on water resources.