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Assessment of Climate Change Impact on Snowmelt Runoff in Himalayan Region

Author

Listed:
  • Rohitashw Kumar

    (College of Agricultural Engineering and Technology, Shalimar Campus, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Srinagar, Jammu and Kashmir 190025, India)

  • Saika Manzoor

    (College of Agricultural Engineering and Technology, Shalimar Campus, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Srinagar, Jammu and Kashmir 190025, India)

  • Dinesh Kumar Vishwakarma

    (Department of Irrigation and Drainage Engineering, College of Technology, G. B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand 263145, India)

  • Nadhir Al-Ansari

    (Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187 Lulea, Sweden)

  • Nand Lal Kushwaha

    (Division of Agricultural Engineering, ICAR-Indian Agricultural Research Institute, Pusa Campus, New Delhi 110012, India)

  • Ahmed Elbeltagi

    (Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura 35516, Egypt)

  • Kallem Sushanth

    (Agricultural and Food Engineering Department, IIT–Kharagpur, West Bengal 721302, India)

  • Vishnu Prasad

    (Water Technology Center, Indian Agricultural Research Institute, New Delhi 110012, India)

  • Alban Kuriqi

    (CERIS, Instituto Superior Técnico, University of Lisbon, 1649-004 Lisbon, Portugal)

Abstract

Under different climate change scenarios, the current study was planned to simulate runoff due to snowmelt in the Lidder River catchment in the Himalayan region. A basic degree-day model, the Snowmelt-Runoff Model (SRM), was utilized to assess the hydrological consequences of change in the climate. The performance of the SRM model during calibration and validation was assessed using volume difference (Dv) and coefficient of determination (R 2 ). The D v was found to be 11.7, −10.1, −11.8, 1.96, and 8.6 in 2009–2014, respectively, while the respective R 2 was 0.96, 0.92, 0.95, 0.90, and 0.94. The D v and R 2 values indicate that the simulated snowmelt runoff closely agrees with the observed values. The simulated findings were assessed under three different climate change scenarios: (a) an increase in precipitation by +20%, (b) a temperature rise of +2 °C, and (c) a temperature rise of +2 °C with a 20% increase in snow cover. In scenario (b), the simulated results showed that runoff increased by 53% in summer (April–September). In contrast, the projected increased discharge for scenarios (a) and (c) was 37% and 67%, respectively. The SRM efficiently forecasts future water supplies due to snowmelt runoff in high elevation, data-scarce mountain environments.

Suggested Citation

  • Rohitashw Kumar & Saika Manzoor & Dinesh Kumar Vishwakarma & Nadhir Al-Ansari & Nand Lal Kushwaha & Ahmed Elbeltagi & Kallem Sushanth & Vishnu Prasad & Alban Kuriqi, 2022. "Assessment of Climate Change Impact on Snowmelt Runoff in Himalayan Region," Sustainability, MDPI, vol. 14(3), pages 1-23, January.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:3:p:1150-:d:729076
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    Citations

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    Cited by:

    1. Abhinav Kumar Singh & Pankaj Kumar & Rawshan Ali & Nadhir Al-Ansari & Dinesh Kumar Vishwakarma & Kuldeep Singh Kushwaha & Kanhu Charan Panda & Atish Sagar & Ehsan Mirzania & Ahmed Elbeltagi & Alban Ku, 2022. "An Integrated Statistical-Machine Learning Approach for Runoff Prediction," Sustainability, MDPI, vol. 14(13), pages 1-30, July.
    2. Zhanassyl Teleubay & Farabi Yermekov & Ismail Tokbergenov & Zhanat Toleubekova & Amangeldy Igilmanov & Zhadyra Yermekova & Aigerim Assylkhanova, 2022. "Comparison of Snow Indices in Assessing Snow Cover Depth in Northern Kazakhstan," Sustainability, MDPI, vol. 14(15), pages 1-23, August.

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