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An investigation of the short-term meteorological drought variability over Asir Region of Saudi Arabia

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Abstract

Changes in precipitation as a result of climate change are becoming a widespread issue all around the world. A lack of rainfall causes a meteorological drought. The short-term Standardized Precipitation Index (SPI-6) index was used to estimate meteorological drought conditions in Saudi Arabia's Asir region from 1970 to 2017. Innovative trend analysis (ITA), the Modified Mann–Kendall test (MMK), the Sequential Mann–Kendall test, and Morlet wavelet transformation were used to detect trend and periodicity in meteorological drought conditions in the Asir region. In addition, the meteorological drought conditions were forecasted by integrating Particle Swarm Optimization (PSO) ensemble machine learning algorithm and an artificial neural network (ANN). Droughts of varying severity have become more frequent in Asir, according to the findings. In most stations, ITA and MMK tests have revealed a significant increase in drought. In all stations, the SQMK test revealed a big sudden year-over-year drought trend. With the exception of one station, all stations experienced extreme drought frequency discovered using Morlet Wavelet Transformation over a long period of time (10 years or more) (station 34). The PSO-ANN hybrid learning algorithm predicted SPI-6 values that had a strong correlation with actual SPI-6 values and also had lower error values, indicating that this model performed well. The PSO-ANN model predicts that the Asir region of Saudi Arabia will experience major moderate to extreme drought events in the coming years (2018–2025). The findings of this analysis will assist planners and policymakers in planning for the acquisition of sustainable agriculture in the study area.

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Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for proving administrative and financial supports. Authors also would like to thank the Ministry of Environment, Water and Agriculture (MEWA), Saudi Arabia for providing rainfall data for 30 meteorological stations.

Availability of data and material

Data are available upon request.

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Not applicable.

Funding

Funding for this research was given under award numbers R.G.P2 /92/41 by the Deanship of Scientific Research; King Khalid University, Ministry of Education, Kingdom of Saudi Arabia.

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Authors and Affiliations

  1. Department of Civil Engineering, College of Engineering, King Khalid University, P.O. Box: 394, Abha, 61411, Kingdom of Saudi Arabia

    Majed Alsubih, Javed Mallick & Saeed AlQadhi

  2. Department of Geography, University of Gour Banga, Malda, India

    Swapan Talukdar

  3. Department of Disaster Management, Begum Rokeya University, Rangpur, 5400, Bangladesh

    Roquia Salam

  4. Department of Urban and Regional Planning, Khulna University of Engineering and Technology, Khulna, 9203, Bangladesh

    Md. Abdul Fattah

  5. Faculty of Civil Engineering, University of Transport and Communications, Hanoi, Vietnam

    Nguyen Viet Thanh

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  1. Majed Alsubih
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Contributions

Conceptualization: Majed Alsubih, Javed Mallick, Swapan Talukdar, and Roquia Salam; data curation: Md. Abdul Fattah; formal analysis: Majed Alsubih, Javed Mallick, Swapan Talukdar, and Roquia Salam; funding acquisition: Majed Alsubih; methodology: Javed Mallick, Swapan Talukdar, Saeed Alqadhi, and Nguyen Viet Thanh; project administration: Majed Alsubih, Saeed Alqadhi, and Javed Mallick; resources: Saeed Alqadhi, Majed Alsubih, and Nguyen Viet Thanh; software: Javed Mallick and Swapan Talukdar; supervision: Majed Alsubih and Javed Mallick; validation: Swapan Talukdar and Javed Mallick; writing—original draft: Javed Mallick, Swapan Talukdar, and Saeed Alqadhi; writing—review and editing: Javed Mallick and Roquia Salam.

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Correspondence to Javed Mallick.

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Alsubih, M., Mallick, J., Talukdar, S. et al. An investigation of the short-term meteorological drought variability over Asir Region of Saudi Arabia. Theor Appl Climatol 145, 597–617 (2021). https://doi.org/10.1007/s00704-021-03647-4

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