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Impact of climate change on stormwater drainage in urban areas

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Abstract

Climate change and urbanization are significantly magnifying flood hazard, leading to a greater vulnerability of urban concentrations. This paper investigates the impact of climate change on urban flooding using future projected rainfall data and a calibrated hydraulic model. Two urban watersheds in Delhi, India (the Qudesia Nallah catchment and the Jahangirpuri drain catchment) are considered to evaluate the climate change impact on urban flooding. Regional climate models (RCMs) are used to project future precipitation, which is then utilized by the hydraulic model to evaluate the impact on flooding. Climate data from three RCMs extracted from the Coordinated Regional Climate Downscaling Experiment (CORDEX) are used to study the impact of climate change for historical (1990–2016) and future scenario (Representative Concentration Pathway (RCP) 4.5, 2021–2100). The rainfall projections are fed as 2-, 5-, 10-, and 20-year return periods to a calibrated hydrodynamic Storm Water Management Model (SWMM). The results show that the flooded nodes vary between 2–6 and 12–43, respectively, in the Qudesia Nallah catchment and the Jahangirpuri drain catchment under present conditions but increase from 11 to 51 and 42 to 91, respectively, for future climate conditions. The results suggest that the risk of occurrence of flooding, duration, and frequency in the two study areas will increase in the future when compared to those under the present conditions. The results also indicate that the damage induced by the 20-year return period rainfall at the present time will likely be caused just by the 2-year return period in the future. This is due to the greater likelihood of rainfall extremes in the region. The potential flooding sites identified in this study will provide the urban municipalities with substantive information to perform ameliorative strategies.

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Acknowledgements

The authors thank the Delhi Government for providing the data to carry out the work. SK would like to acknowledge Computational Hydraulics International (CHI) Canada for providing PCSWMM academics license for SWMM model development. The authors would like to thank the three anonymous reviewers and the Associate Editor for their constructive comments and useful suggestions on earlier versions of this manuscript.

Funding

SK, VKG, SP, and DK acknowledge the financial support provided by the Indian Institute of Technology (Indian School of Mines) Dhanbad, for conducting this research work. DRK and AKG acknowledge the financial support provided by the Indian Institute of Technology, Delhi, for conducting this research work. AA and AG acknowledge the joint funding support from the University Grant Commission (UGC) and DAAD under the framework of the Indo-German Partnership in Higher Education (IGP).

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Authors and Affiliations

  1. Department of Mining Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, 826004, India

    Satish Kumar, Vasant Govind Kumar Villuri & Dheeraj Kumar

  2. Department of Hydrology, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, 247667, India

    Ankit Agarwal & Abinesh Ganapathy

  3. GFZ German Research Centre for Geosciences, Section Hydrology, Telegrafenberg, 14473, Potsdam, Germany

    Ankit Agarwal

  4. Department of Civil Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, 826004, India

    Srinivas Pasupuleti

  5. Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India

    Deo Raj Kaushal & Ashwin Kumar Gosain

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

    Bellie Sivakumar

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  1. Satish Kumar
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  2. Ankit Agarwal
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Fig. 9
figure 9

Results of sensitivity analysis of the parameters

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, Table 6

Table 6 Definition of sensitive parameters
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Sensitivity analysis

For developing the SWMM, sensitivity analysis of the parameters is carried out, i.e. before the calibration procedure, the sensitivity analysis is done using all the eight parameters to analyze which parameters are more sensitive to minimize the error between simulated and measured hydrographs. A similar methodology has been adopted by Jewell et al. (1978), in which parameters are varied by definite percentage, i.e. ± 5%, upon their initial values holding other parameters constant and recording the difference between the model results. The sensitivity analysis of the parameters is shown in Figure A1 and descriptions of the sensitivity parameters along with the ranking is shown in Table A1.

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Kumar, S., Agarwal, A., Ganapathy, A. et al. Impact of climate change on stormwater drainage in urban areas. Stoch Environ Res Risk Assess 36, 77–96 (2022). https://doi.org/10.1007/s00477-021-02105-x

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