Quantification of Snow/Glacier Melt Impacts of Historical and Future Climate Change on Eastern Himalayan River

Open Access

Year : 2025 | Volume : 13 | Special Issue 01 | Page : 508 524
    By

    A K Shukla,

  • I Ahmad,

  • S K Jain,

  • M K Verma,

  1. Research Scholar, Department of Civil Engineering, NIT Raipur, GE Road, 492010, Chhattisgarh, India
  2. Assistant Professor, Department of Civil Engineering, NIT Raipur, GE Road, 492010, Chhattisgarh, India
  3. Visiting Professor, Department of Civil Engineering COEDMM, IIT Roorkee, Haridwar Road, Uttarakhand, India
  4. Assistant Professor, Department of Civil Engineering, NIT Raipur, GE Road, 492010, Chhattisgarh, India

Abstract

Millions of the population on Eastern Himalayan Rivers (EHR) totally depend on, domestic use, agriculture, and hydropower. Several studies found that climate warming threatens this EHR’s hydrological regime. In order to understand the hydrologic response of their headwaters and how climate change affects streamflow, a hydrological modeling study is conducted in the Teesta River Basin (TRB) in EHR using an open-source Quantum Geographical Information System (QGIS) with semi-distributed QSWAT1.5 (Soil and Water Assessment Tools). The model performed well in 1985-2015 simulations, warmup period (2001-2002) for calibration (2003-2010) and validation (2011-2015) versus measured daily streamflow at Teesta IV DAM (R2 ≈ 0.87). The study found that snowmelt runoff from a bigger snow-covered area at higher TRB accounts for 62–74% of the average annual water supply of 2000 mm on average 200. Approximately 16% of precipitation is evapotranspiration. Basin water yield is 78% of precipitation, with most generated in early summer from mid-May to September. The Coupled Model Intercomparison Project Phase 6 (CMIP6) data for thirteen models, which ACCESS-ESM1-5 was the most suitable model, which was calculated by the Taylor diagram. CMIP6 has four Shared Socioeconomic Pathway scenarios (SSPs), but the research has used average data to explore how climate change will affect future streamflow. First, calculate monthly discharge for all SSPs but got the same result from all, and model performed well. Global Climate Models (GCM) to Regional Scale Model (RCM) and Quantile Mapping are used to correct bias and load into SWAT to simulate end-of-century stream flows. The region’s predicted precipitation and temperature fluctuations showed a wetter and warmer climate.

Keywords: TRB, snowmelt, QGIS, runoff, CMIP6 & SWAT

[This article belongs to Special Issue under section in Journal of Polymer and Composites (jopc)]

aWQ6MTkwMTQzfGZpbGVuYW1lOjI0OWZhMTdlLTEtcG5nLndlYnB8c2l6ZTp0aHVtYm5haWw=
How to cite this article:
A K Shukla, I Ahmad, S K Jain, M K Verma. Quantification of Snow/Glacier Melt Impacts of Historical and Future Climate Change on Eastern Himalayan River. Journal of Polymer and Composites. 2024; 13(01):508-524.
How to cite this URL:
A K Shukla, I Ahmad, S K Jain, M K Verma. Quantification of Snow/Glacier Melt Impacts of Historical and Future Climate Change on Eastern Himalayan River. Journal of Polymer and Composites. 2024; 13(01):508-524. Available from: https://journals.stmjournals.com/jopc/article=2024/view=190151


Browse Figures

References

  1. Aayog, N. I. T. I. (2018). Sustainable Tourism in the Indian Himalayan Region. Report of Working Group II, NITI Aayog, Government of India.
  2. Abbas, T., Hussain, F., Nabi, G., Boota, M. W., & Wu, R. S. (2019). Uncertainty evaluation of SWAT model for snowmelt runoff in a Himalayan watershed. Terrestrial, Atmospheric & Oceanic Sciences, 30(2).
  3. Abdul Hakeem, K., Abirami, S., Rao, V. V., Diwakar, P. G., &Dadhwal, V. K. (2018). Updated inventory of Glacial Lakes in Teesta Basin using remote sensing data for use in GLOF risk assessment. Journal of the Indian Society of Remote Sensing, 46, 463-470.
  4. Amoako Johnson, F., & Hutton, C. W. (2014). Dependence on agriculture and ecosystem services for livelihood in Northeast India and Bhutan: vulnerability to climate change in the Tropical River Basins of the Upper Brahmaputra. Climatic change, 127, 107-121.
  5. Basnett, S., Kulkarni, A. V., & Bolch, T. (2013). The influence of debris cover and glacial lakes on the recession of glaciers in Sikkim Himalaya, India. Journal of Glaciology, 59(218), 1035-1046.
  6. Berthier, E., Arnaud, Y., Kumar, R., Ahmad, S., Wagnon, P., & Chevallier, P. (2007). Remote sensing estimates of glacier mass balances in the Himachal Pradesh (Western Himalaya, India). Remote Sensing of Environment, 108(3), 327-338.
  7. Covey, C., AchutaRao, K. M., Fiorino, M., Gleckler, P. J., Taylor, K. E., & Wehner, M. F. (2002). Intercomparison of climate data sets as a measure of observational uncertainty. PCMDI Report, 69.
  8. Das, S., Datta, P., Sharma, D., & Goswami, K. (2022). Trends in temperature, precipitation, potential evapotranspiration, and water availability across the teesta river basin under 1.5 and 2 C temperature rise scenarios of CMIP6. Atmosphere, 13(6), 941.
  9. Du, X., Silwal, G., & Faramarzi, M. (2022). Investigating the impacts of glacier melt on stream temperature in a cold-region watershed: Coupling a glacier melt model with a hydrological model. Journal of Hydrology, 605, 127303.
  10. Gleckler, P. J., Taylor, K. E., &Doutriaux, C. (2008). Performance metrics for climate models. Journal of Geophysical Research: Atmospheres, 113(D6).
  11. Goyal, M. K., Shivam, Sarma, A. K., & Singh, D. S. (2018). Subansiri: largest tributary of Brahmaputra River, Northeast India. The Indian Rivers: Scientific and Socio-economic Aspects, 523-535.
  12. Grusson, Y., Sun, X., Gascoin, S., Sauvage, S., Raghavan, S., Anctil, F., &Sáchez-Pérez, J. M. (2015). Assessing the capability of the SWAT model to simulate snow, snow melt and streamflow dynamics over an alpine watershed. Journal of Hydrology, 531, 574-588.
  13. Guo, J., & Su, X. (2019). Parameter sensitivity analysis of SWAT model for streamflow simulation with multisource precipitation datasets. Hydrology Research, 50(3), 861-877.
  14. Huang, X., Sillanpää, M., Gjessing, E. T., Peräniemi, S., & Vogt, R. D. (2011). Water quality in the southern Tibetan Plateau: chemical evaluation of the YarlungTsangpo (Brahmaputra). River research and applications, 27(1), 113-121.
  15. Kaser, G., Großhauser, M., &Marzeion, B. (2010). Contribution potential of glaciers to water availability in different climate regimes. Proceedings of the National Academy of Sciences, 107(47), 20223-20227.
  16. Khawas, V. (2015). Pathways for Climate Resilient Livelihoods: The Case of a Large Cardamom Farming in the Dzongu Valley of the Tista River Basin, Sikkim Himalaya. Climate Change in the Asia-Pacific Region, 319-334.
  17. Kouchi, D. H., Esmaili, K., Faridhosseini, A., Sanaeinejad, S. H., Khalili, D., & Abbaspour, K. C. (2017). Sensitivity of calibrated parameters and water resource estimates on different objective functions and optimization algorithms. Water, 9(6), 384.
  18. Kumar, B., Lakshmi, V., Patra, K. C., Sen, I. S., & Srinivasan, R. (2016, December). Assessing Hydrological Uncertainties Using the SWAT Model to Simulate Streamflow Over the Alpine Himalayas. In 2016 AGU Fall Meeting. AGU.
  19. Mishra, V., Bhatia, U., & Tiwari, A. D. (2020). Bias-corrected climate projections for South Asia from coupled model intercomparison project-6. Scientific data, 7(1), 338.
  20. Mukhopadhyay, S. C. (1982). The Tista basin: a study in fluvial geomorphology. KP Bagchi.
  21. Nguyen, T. V., Dietrich, J., Dang, T. D., Tran, D. A., Van Doan, B., Sarrazin, F. J., … & Srinivasan, R. (2022). An interactive graphical interface tool for parameter calibration, sensitivity analysis, uncertainty analysis, and visualization for the Soil and Water Assessment Tool. Environmental Modelling & Software, 156, 105497.
  22. Rahman, K., Maringanti, C., Beniston, M., Widmer, F., Abbaspour, K., & Lehmann, A. (2013). Streamflow modeling in a highly managed mountainous glacier watershed using SWAT: the Upper Rhone River watershed case in Switzerland. Water resources management, 27, 323-339.
  23. Raj, K. B. G., Remya, S. N., & Kumar, K. V. (2013). Remote sensing-based hazard assessment of glacial lakes in Sikkim Himalaya. Current Science, 359-364.
  24. Raman, A., Kulkarni, A. V., & Prasad, V. (2022). Glacier mass balance estimation in Garhwal Himalaya using improved accumulation area ratio method. Environmental monitoring and assessment, 194(8), 583.
  25. Rupper, S., Johnson, E., Olson, M., Steenburgh, J., Kochanski, A., Riley, C., & Skiles, M. (2020, July). Climate-Driven Glacier and Snowpack Changes in the Water Towers of Asia (Invited Presentation). In 19th Conference on Mountain Meteorology. AMS.
  26. Sam Albers, Ilaria Prosdocimi (2023). Link: https://github.com/cran-task-views/Hydrology/
  27. Sharma, A., & Goyal, M. K. (2020). Assessment of the changes in precipitation and temperature in Teesta River basin in Indian Himalayan Region under climate change. Atmospheric Research, 231, 104670.
  28. Shukla, A. K., Ahmad, I., Jain, S. K., & Verma, M. K. (2024). Assessment of Continuous Growth of Glacial Lakes in the Teesta River Basin Using Semi-Automated Geospatial Approach. Nature Environment & Pollution Technology, 23 (2), 875-886. https://doi.org/10.46488/NEPT.2024.v23i02.023.
  29. Shukla, A. K., Ahmad, I., Jain, S. K., & Verma, M. K. (2024). Enhancing Model Calibration and Uncertainty Estimation through MultiObjective Optimization with SUFI-2 Algorithm. Journal of Environmental Informatics Letters, 11(2) 109-122. https:// doi:10.3808/jeil.202400130.
  30. Shukla, S., Jain, S. K., & Kansal, M. L. (2021). Hydrological modelling of a snow/glacier-fed western Himalayan basin to simulate the current and future streamflows under changing climate scenarios. Science of the Total Environment, 795, 148871.
  31. Singh, V., Kumar Goyal, M., Surampalli, R. Y., & Munoz-Arriola, F. (2017). Sub catchment assessment of snowpack and snowmelt change by analyzing elevation bands and parameter sensitivity in the high Himalayas. Hydrology and Earth System Sciences Discussions, 1-31.
  32. Schürz, C. (2019). Development of tools and interfaces for the implementation, pre-processing, and sensitivity analysis of the SWAT model in an R programming environment.
  33. Talukdar, S., Eibek, K. U., Akhter, S., Ziaul, S. K., Islam, A. R. M. T., & Mallick, J. (2021). Modeling fragmentation probability of land-use and land-cover using the bagging, random forest and random subspace in the Teesta River Basin, Bangladesh. Ecological indicators, 126, 107612.T
  34. sering, T., Wahed, M. S. A., Iftekhar, S., & Sillanpää, M. (2019). Major ion chemistry of the Teesta River in Sikkim Himalaya, India: Chemical weathering and assessment of water quality. Journal of Hydrology: Regional Studies, 24, 100612.
  35. Wu, Y., & Liu, S. (2012). Automating calibration, sensitivity and uncertainty analysis of complex models using the R package Flexible Modeling Environment (FME): SWAT as an example. Environmental Modelling & Software, 31, 99-109.
  36. Zaman, S., &Pramanick, P. (2020). Ecosystem Services of Mahananda Wild Life Sanctuary: An Overview of Faunal and Floral Diversity. NATURAL RESOURCES AND THEIR ECOSYSTEM SERVICES, 63.
  37. Zannah, R., Mohammed, K., Rahman, A., & Yunus, A. (2020). Analysis on flow and water balance parameters of Teesta River basin due to climate change and upstream intervention. In Water, Flood Management and Water Security Under a Changing Climate: Proceedings from the 7th International Conference on Water and Flood Management (pp. 267-282). Springer International Publishing.
  38. Zhang, Y., Luo, Y., Sun, L., Liu, S., Chen, X., & Wang, X. (2016). Using glacier area ratio to quantify effects of melt water on runoff. Journal of Hydrology, 538, 269-277.
Special Issue Open Access Original Research
Volume 13
Special Issue 01
Received 23/04/2024
Accepted 19/06/2024
Published 18/12/2024
388


My IP

PlumX Metrics