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<p>Occurrence of hydro-climatological extremes, explicitly drought, is expected to aggravate manifolds in the future due to the changing climate. Thus, a need for long term drought projection arises, heavily relying upon the estimates of state-of-art Coupled Model Intercomparison Project Phase 6 (CMIP6) general circulation models (GCMs). However, climate models exhibit considerable uncertainty in projection of meteorological parameters, ultimately posing a great challenge in the efficient prognosis of dry conditions. Therefore, the present study aims to assess the performance of different CMIP6 models in simulating historical drought against the observations and identify the best performing models, over the Indian extent of Indus River basin during the period 1979–2014. For this purpose, gridded precipitation, maximum and minimum temperature data of 16 CMIP6 climate models is utilized to comprehend the drought simulations. Drought quantification is done using Standardized Precipitation Evapotranspiration Index (SPEI) at a time scale of 3-months to capture the meteorological drought. Symmetrical Uncertainty (SU), a robust feature selection entropy-based approach has been used to accomplish the ranking of GCMs. Subsequently, performance evaluation of models in simulating precipitation, minimum and maximum temperature, and potential evapotranspiration (PET) has also been carried out, since they serve as key input variables in characterizing droughts. The results revealed that the spatial and density distribution of model biases demonstrates a high amount of heterogeneity corresponding to different parameters. Largely, the precipitation attributes are underestimated by the GCMs whereas, a converse behavior is depicted by the models in simulating minimum and maximum temperature. A significant variation in the performance of GCMs is observed while simulating individual parameters. For precipitation, INM-CM4-8, ACCESS-ESM1-5, NorESM2-LM and INM-CM5-0 are observed to be the highest ranking models. Whereas, NESM3, ACCESS-ESM1-5, ACCESS-CM2, MPI-ESM1-2-HR and MPI-ESM1-2-LR show better performance for minimum and maximum temperature. Drought characteristics (SPEI) seem to be efficiently captured by KACE-1-0-G, NorESM2-LM, INM-CM5-0 and NorESM2-MM models. Finally, a trade-off developed to identify the models that can effectively reproduce all the parameters introduces MIROC6, MPI-ESM1-2-HR and NorESM2-LM as the most suitable GCMs for climate projections over the Indus River basin. Emphasis of this study is on the importance of identifying and ranking best GCMs from the historical simulation experiments, which ultimately will lead to the better management of future drought events under various climate change scenarios.</p>

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Entropy theory-based performance appraisal of CMIP6 climate models in regional drought simulation over the Indus River basin: a multifactorial investigation

  • 期刊论文

  • 发表日期：

    2025年10月17日

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  • Amit Dubey,
  • Deepak Swami,
  • Vivek Gupta,
  • Nitin Joshi

摘要

Occurrence of hydro-climatological extremes, explicitly drought, is expected to aggravate manifolds in the future due to the changing climate. Thus, a need for long term drought projection arises, heavily relying upon the estimates of state-of-art Coupled Model Intercomparison Project Phase 6 (CMIP6) general circulation models (GCMs). However, climate models exhibit considerable uncertainty in projection of meteorological parameters, ultimately posing a great challenge in the efficient prognosis of dry conditions. Therefore, the present study aims to assess the performance of different CMIP6 models in simulating historical drought against the observations and identify the best performing models, over the Indian extent of Indus River basin during the period 1979–2014. For this purpose, gridded precipitation, maximum and minimum temperature data of 16 CMIP6 climate models is utilized to comprehend the drought simulations. Drought quantification is done using Standardized Precipitation Evapotranspiration Index (SPEI) at a time scale of 3-months to capture the meteorological drought. Symmetrical Uncertainty (SU), a robust feature selection entropy-based approach has been used to accomplish the ranking of GCMs. Subsequently, performance evaluation of models in simulating precipitation, minimum and maximum temperature, and potential evapotranspiration (PET) has also been carried out, since they serve as key input variables in characterizing droughts. The results revealed that the spatial and density distribution of model biases demonstrates a high amount of heterogeneity corresponding to different parameters. Largely, the precipitation attributes are underestimated by the GCMs whereas, a converse behavior is depicted by the models in simulating minimum and maximum temperature. A significant variation in the performance of GCMs is observed while simulating individual parameters. For precipitation, INM-CM4-8, ACCESS-ESM1-5, NorESM2-LM and INM-CM5-0 are observed to be the highest ranking models. Whereas, NESM3, ACCESS-ESM1-5, ACCESS-CM2, MPI-ESM1-2-HR and MPI-ESM1-2-LR show better performance for minimum and maximum temperature. Drought characteristics (SPEI) seem to be efficiently captured by KACE-1-0-G, NorESM2-LM, INM-CM5-0 and NorESM2-MM models. Finally, a trade-off developed to identify the models that can effectively reproduce all the parameters introduces MIROC6, MPI-ESM1-2-HR and NorESM2-LM as the most suitable GCMs for climate projections over the Indus River basin. Emphasis of this study is on the importance of identifying and ranking best GCMs from the historical simulation experiments, which ultimately will lead to the better management of future drought events under various climate change scenarios.