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<p>Drought projections are critical for understanding how climate change will influence different regions; they assist governments and regions in developing adaptation measures to ensure long-term sustainability. This study assessed the performance of ten different CMIP6 models in Indian meteorological subdivisions (MS), with the goal of determining the optimal CMIP6 model for drought projection in each of these subdivisions. For this purpose, the model-simulated data obtained from NASA Earth Exchange (NEX) Global Daily Downscaled Projections (GDDP) NEX-GDDP-CMIP6 dataset are statistically compared with the observed Indian Meteorological Department (IMD) data using R<sup>2</sup>, RMSE, NSE, KGE, PBIAS and the optimal CMIP6 models for each MS are identified using Taylor diagram and Spider plot. The technique for order of preference by similarity to ideal solution (TOPSIS) analysis reveals, for precipitation MPI-ESMI-2-HR is observed to be the best performing model for Arunachal Pradesh, Chhattisgarh and Kerala, CanESM5 for Gujarat and West Rajasthan and EC-Earth3-Veg-LR for Gangetic West Bengal respectively. Whereas for temperature ACCESS-CM2 is observed to be the best performing model for Arunachal Pradesh and Gujarat, MRI-ESM2 for Chhattisgarh and Gangetic West Bengal, and MPI-ESMI-2-HR for West Rajasthan respectively. Thereafter the drought projections have been performed across different scenarios using the optimal model identified for these MS across different climatic zones in India using SPEI. Results from the drought analysis suggests that the maximum ED events are observed for steppe hot &amp; arid climatic subdivision and minimum ED events for tropical monsoon climatic subdivision. The Modified Mann Kendall Trend analysis for Hot Desert &amp; Arid (Western Rajasthan) and Steppe Hot &amp; Arid (Gujarat) climatic subdivisions reveal statistically significant and progressively severe trends in drought. SPEI-24 indicates the most severe consequences. The increasingly severe drought conditions pointing out the necessity of quick attention and action to manage water resources in these areas.</p>

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Optimal CMIP6 Model Identification Using Multi-criteria Statistical Analysis for Drought Projection in Indian Meteorological Subdivisions

  • 期刊论文

  • 发表日期：

    2026年5月12日

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  • Siddhant Panigrahi,
  • Vikas Kumar Vidyarthi

摘要

Drought projections are critical for understanding how climate change will influence different regions; they assist governments and regions in developing adaptation measures to ensure long-term sustainability. This study assessed the performance of ten different CMIP6 models in Indian meteorological subdivisions (MS), with the goal of determining the optimal CMIP6 model for drought projection in each of these subdivisions. For this purpose, the model-simulated data obtained from NASA Earth Exchange (NEX) Global Daily Downscaled Projections (GDDP) NEX-GDDP-CMIP6 dataset are statistically compared with the observed Indian Meteorological Department (IMD) data using R2, RMSE, NSE, KGE, PBIAS and the optimal CMIP6 models for each MS are identified using Taylor diagram and Spider plot. The technique for order of preference by similarity to ideal solution (TOPSIS) analysis reveals, for precipitation MPI-ESMI-2-HR is observed to be the best performing model for Arunachal Pradesh, Chhattisgarh and Kerala, CanESM5 for Gujarat and West Rajasthan and EC-Earth3-Veg-LR for Gangetic West Bengal respectively. Whereas for temperature ACCESS-CM2 is observed to be the best performing model for Arunachal Pradesh and Gujarat, MRI-ESM2 for Chhattisgarh and Gangetic West Bengal, and MPI-ESMI-2-HR for West Rajasthan respectively. Thereafter the drought projections have been performed across different scenarios using the optimal model identified for these MS across different climatic zones in India using SPEI. Results from the drought analysis suggests that the maximum ED events are observed for steppe hot & arid climatic subdivision and minimum ED events for tropical monsoon climatic subdivision. The Modified Mann Kendall Trend analysis for Hot Desert & Arid (Western Rajasthan) and Steppe Hot & Arid (Gujarat) climatic subdivisions reveal statistically significant and progressively severe trends in drought. SPEI-24 indicates the most severe consequences. The increasingly severe drought conditions pointing out the necessity of quick attention and action to manage water resources in these areas.