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Role of vertical velocity in improving finer scale statistical downscaling for projection of extreme precipitation

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Abstract

Increase in human-induced climate warming is unequivocal and is subsequently causing an increase in the magnitude of precipitation extremes around the globe, inducing substantial damages to the socioeconomic sectors. Thus, a reliable projection of extreme precipitation scenarios is crucial for designing suitable adaptation strategies. At present, this is being attempted with the use of general circulation models (GCMs) projections. However, the GCMs simulate the extreme precipitation events rather poorly, especially over the tropical regions, and also suffer from frequent lack of reliability at local/regional scales. Therefore, it is of paramount significance to identify the relevant physical parameters for the extreme precipitation scenarios, and further implement these parameters in downscaling approaches to significantly improve the impact assessment at a regional scale. Previous studies have reported that the dynamic component (mainly vertical wind velocity) has a significant influence on the precipitation extremes over South Asian regions, along with the thermodynamic component. This indicates that the consideration of vertical wind velocity for projecting the precipitation extremes may significantly increase the efficacy of the downscaling approach, a contemplation that provoked its inclusion in statistical downscaling in this study. The methodology was demonstrated over the Mahanadi river basin (India), which often experiences heavy rainfall during the monsoon and post-monsoon disturbances originating from the Bay of Bengal (BoB). We observed that the dynamic component plays a crucial role in changing the pattern of precipitation extremes over the basin. Further, we noticed that inclusion of this dynamic component in statistical downscaling significantly improved the extreme precipitation projections. Based on these observations, we projected (2026–2055) the mean and extreme precipitation events, considering six most efficient CMIP5 models for the Indian subcontinent under RCP 4.5 and RCP 8.5 scenarios. The outcomes of this study can be utilized in deriving reliable water resource management practices.

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Acknowledgements

The work presented here is supported financially by ISRO-IIT(B)-Space Technology Cell (STC) through a sponsored research project (No. 14ISROC009). The authors acknowledge the World Climate Research Programme’s working Group on coupled Modelling, which is responsible for CMIP, and we thank the modeling groups for producing and making available their model output through the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison (PCMDI) portal. We also would like to thank APHRODITE, Japan, for making observed gridded datasets available. The reanalysis data were obtained from the website http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html. The authors sincerely thank the Indian Institute of Technology Bombay (IIT Bombay) for providing all the computational facilities. The authors are grateful to the editors and the anonymous reviewers for their suggestions.

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Authors and Affiliations

  1. Centre for Environmental Science and Engineering, Indian Institute of Technology Bombay, 400076, Mumbai, Powai, India

    Aditya Gusain, H. Vittal & Subhankar Karmakar

  2. Civil Engineering Department, Manipal Institute of Technology, 576104, Manipal, India

    H. Vittal

  3. Department of Civil Engineering, University College of Engineering, Osmania University, Hyderabad, India

    Shashikanth Kulkarni

  4. Department of Civil Engineering, Indian Institute of Technology Bombay Powai, Mumbai, 400076, India

    Shashikanth Kulkarni & Subimal Ghosh

  5. Interdisciplinary Programme in Climate Studies, Indian Institute of Technology Bombay, 400076, Mumbai, India

    Subimal Ghosh & Subhankar Karmakar

  6. Centre for Urban Science and Engineering, Indian Institute of Technology Bombay, 400076, Mumbai, India

    Subimal Ghosh & Subhankar Karmakar

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  1. Aditya Gusain
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Correspondence to Subhankar Karmakar.

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Gusain, A., Vittal, H., Kulkarni, S. et al. Role of vertical velocity in improving finer scale statistical downscaling for projection of extreme precipitation. Theor Appl Climatol 137, 791–804 (2019). https://doi.org/10.1007/s00704-018-2615-1

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