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Development of a new hybrid ensemble method for accurate characterization of future drought using multiple global climate models

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Abstract

Drought as a natural disaster, can have devastating effects on various sectors. This study proposes a new weighting scheme called weighted aggregation (WA) and introduces the Multi-model weighted drought severity index (MMWDSI) as an improved indicator for drought assessment. The methodology of MMWDSI involves ensemble modeling using the WA scheme, incorporating the K-components Gaussian mixture model (K-CGMM) for appropriate distribution fitting. It employs a multi-stage statistical procedure that considers point-to-point variations and the past performance of climate models through the Taylor Skill Score in the initial stage. Subsequent stages involve linear models and K-CGMM for prediction and standardization. Similar to other standardized drought indices, the proposed index allows for inferring the probabilistic behavior of extreme events, such as extreme drought or extreme wet conditions, and assessing trends using various statistical techniques. For the application of the index, historical precipitation data from 1961 to 2014 was utilized from 32 grid points on the Tibetan Plateau as the reference dataset. Additionally, simulations from 18 models of the Coupled Model Intercomparison Project phase 6, both past and future, were employed for the estimation procedure. The findings demonstrate that the developed weighting scheme surpasses the Equal Weighted Averaging approach. In conclusion, the MMWDSI approach proves to be a flexible and effective method that enhances accuracy in drought monitoring.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Funding

The current research is a part of a funded research project awarded by the University of the Punjab Lahore, Pakistan (2022). Therefore, the authors are thankful to the project awarding institution.

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Authors and Affiliations

  1. College of Statistical Sciences, University of the Punjab, Lahore, Pakistan

    Mahrukh Yousaf, Zulfiqar Ali, Muhammad Mohsin, Maryam Ilyas & Muhammad Shakeel

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  1. Mahrukh Yousaf
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  2. Zulfiqar Ali
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  3. Muhammad Mohsin
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  4. Maryam Ilyas
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  5. Muhammad Shakeel
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Contributions

MY and ZA conceived the presented idea. MY developed the theory and performed the computations. MM, MI and MS verified the analytical methods and computations. All authors discussed the results and contributed to the final manuscript.

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Correspondence to Zulfiqar Ali.

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Yousaf, M., Ali, Z., Mohsin, M. et al. Development of a new hybrid ensemble method for accurate characterization of future drought using multiple global climate models. Stoch Environ Res Risk Assess 37, 4567–4587 (2023). https://doi.org/10.1007/s00477-023-02526-w

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