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Climatological Drought Forecasting Using Bias Corrected CMIP6 Climate Data: A Case Study for India

Author

Listed:
  • Alen Shrestha

    (Department of Civil and Environmental Engineering, Southern Illinois University, 1230 Lincoln Drive, Carbondale, IL 62901-6603, USA)

  • Md Mafuzur Rahaman

    (AECOM, 2380 McGee St Suite 200, Kansas City, MO 64108, USA)

  • Ajay Kalra

    (Department of Civil and Environmental Engineering, Southern Illinois University, 1230 Lincoln Drive, Carbondale, IL 62901-6603, USA)

  • Rohit Jogineedi

    (Department of Mechanical Engineering and Energy Processes, Southern Illinois University, 1230 Lincoln Drive, Carbondale, IL 62901-6603, USA)

  • Pankaj Maheshwari

    (Louis Berger U.S.; Inc.; A WSP Company, 300 S. 4th Street, Suite 1200, Las Vegas, NV 89101, USA)

Abstract

This study forecasts and assesses drought situations in various regions of India (the Araveli region, the Bundelkhand region, and the Kansabati river basin) based on seven simulated climates in the near future (2015–2044). The self-calibrating Palmer Drought Severity Index (scPDSI) was used based on its fairness in identifying drought conditions that account for the temperature as well. Gridded temperature and rainfall data of spatial resolution of 1 km were used to bias correct the multi-model ensemble mean of the Global Climatic Models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) project. Equidistant quantile-based mapping was adopted to remove the bias in the rainfall and temperature data, which were corrected on a monthly scale. The outcome of the forecast suggests multiple severe-to-extreme drought events of appreciable durations, mostly after the 2030s, under most climate scenarios in all the three study areas. The severe-to-extreme drought duration was found to last at least 20 to 30 months in the near future in all three study areas. A high-resolution drought index was developed and proven to be a key to assessing the drought situation.

Suggested Citation

  • Alen Shrestha & Md Mafuzur Rahaman & Ajay Kalra & Rohit Jogineedi & Pankaj Maheshwari, 2020. "Climatological Drought Forecasting Using Bias Corrected CMIP6 Climate Data: A Case Study for India," Forecasting, MDPI, vol. 2(2), pages 1-26, April.
    • Handle: RePEc:gam:jforec:v:2:y:2020:i:2:p:4-84:d:348981
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    References listed on IDEAS

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    Cited by:

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    2. Ayat-Allah Bouramdane, 2022. "Assessment of CMIP6 Multi-Model Projections Worldwide: Which Regions Are Getting Warmer and Are Going through a Drought in Africa and Morocco? What Changes from CMIP5 to CMIP6?," Sustainability, MDPI, vol. 15(1), pages 1-32, December.
    3. Indira Pokhrel & Ajay Kalra & Md Mafuzur Rahaman & Ranjeet Thakali, 2020. "Forecasting of Future Flooding and Risk Assessment under CMIP6 Climate Projection in Neuse River, North Carolina," Forecasting, MDPI, vol. 2(3), pages 1-23, August.
    4. Brian Ayugi & Zablon Weku Shilenje & Hassen Babaousmail & Kenny T. C. Lim Kam Sian & Richard Mumo & Victor Nnamdi Dike & Vedaste Iyakaremye & Abdelghani Chehbouni & Victor Ongoma, 2022. "Projected changes in meteorological drought over East Africa inferred from bias-adjusted CMIP6 models," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(2), pages 1151-1176, September.
    5. Swarupa Paudel & Neekita Joshi & Ajay Kalra, 2023. "Projected Future Flooding Pattern of Wabash River in Indiana and Fountain Creek in Colorado: An Assessment Utilizing Bias-Corrected CMIP6 Climate Data," Forecasting, MDPI, vol. 5(2), pages 1-19, April.

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