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A novel semi data dimension reduction type weighting scheme of the multi-model ensemble for accurate assessment of twenty-first century drought

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Abstract

Accurately and reliably predicting droughts under multiple models of Global Climate Models (GCMs) is a challenging task. To address this challenge, the Multimodel Ensemble (MME) method has become a valuable tool for merging multiple models and producing more accurate forecasts. This paper aims to enhance drought monitoring modules for the twenty-first century using multiple GCMs. To achieve this goal, the research introduces a new weighing paradigm called the Multimodel Homo-min Pertinence-max Hybrid Weighted Average (MHmPmHWAR) for the accurate aggregation of multiple GCMs. Secondly, the research proposes a new drought index called the Condensed Multimodal Multi-Scalar Standardized Drought Index (CMMSDI). To assess the effectiveness of MHmPmHWAR, the research compared its findings with the Simple Model Average (SMA). In the application, eighteen different GCM models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) were considered at thirty-two grid points of the Tibet Plateau region. Mann–Kendall (MK) test statistics and Steady States Probabilities (SSPs) of Markov chain were used to assess the long-term trend in drought and its classes. The analysis of trends indicated that the number of grid points demonstrating an upward trend was significantly greater than those displaying a downward trend in terms of spatial coverage, at a significance level of 0.05. When examining scenario SSP1-2.6, the probability of moderate wet and normal drought was greater in nearly all temporal scales than other categories. The outcomes of SSP2-4.5 demonstrated that the likelihoods of moderate drought and normal drought were higher than other classifications. Additionally, the results of SSP5-8.5 were comparable to those of SSP2-4.5, underscoring the importance of taking effective actions to alleviate drought impacts in the future. The results demonstrate the effectiveness of the MHmPmHWAR and CMMSDI approaches in predicting droughts under multiple GCMs, which can contribute to effective drought monitoring and management.

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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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Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through large group Research Project under grant number RGP2/337/44.

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The authors did not receive support from any organization for the submitted work.

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Authors and Affiliations

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

    Alina Mukhtar & Zulfiqar Ali

  2. Department of Mathematics, COMSATS University, Islamabad, Pakistan

    Amna Nazeer

  3. Department of Computer Science, College of Science and Art at Mahayil, King Khalid University, 62529, Muhayil Aseer, Saudi Arabia

    Sami Dhahbi

  4. Civil Engineering, Engineering Faculty, Siirt University, Siirt, Turkey

    Veysi Kartal

  5. Department of Mathematics, College of Science & Arts, King Abdul Aziz University, Rabigh, Saudi Arabia

    Wejdan Deebani

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  1. Alina Mukhtar
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Contributions

Alina Mukhtar and Zulfiqar Ali conceived the presented idea. Alina Mukhtar developed the theory and performed the computations. Zulfiqar Ali investigated the findings, supervised this work, and drafted the final manuscript. Amna Nazeer and Vesi Kartal, as a subject expert, significantly contributed by providing detailed consultations to address reviewer comments pertaining to the water engineering aspect. Sami Dhahbi and Wejdan Deebani undertook the responsibility of working out almost all the technical details, overseeing and enhancing the manuscript's English language, grammar, and mathematical aspects. Both have added their expertise to improve readability of the results section also. All authors discussed the results and contributed to the final manuscript.

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Correspondence to Zulfiqar Ali.

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Mukhtar, A., Ali, Z., Nazeer, A. et al. A novel semi data dimension reduction type weighting scheme of the multi-model ensemble for accurate assessment of twenty-first century drought. Stoch Environ Res Risk Assess (2024). https://doi.org/10.1007/s00477-024-02723-1

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