Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 27, issue 5
Articles | Volume 27, issue 5
https://doi.org/10.5194/hess-27-1047-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/hess-27-1047-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 27, issue 5
Research article
 | 
13 Mar 2023
Research article |  | 13 Mar 2023

Machine-learning- and deep-learning-based streamflow prediction in a hilly catchment for future scenarios using CMIP6 GCM data

Dharmaveer Singh, Manu Vardhan, Rakesh Sahu, Debrupa Chatterjee, Pankaj Chauhan, and Shiyin Liu

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Abbasian, M., Moghim, S., and Abrishamchi, A.: Performance of the general circulation models in simulating temperature and precipitation over Iran, Theor. Appl. Climatol., 135, 1465–1483, https://doi.org/10.1007/s00704-018-2456-y, 2019. 
Adib, M. N. M. and Harun, S.: Metalearning Approach Coupled with CMIP6 Multi-GCM for Future Monthly Streamflow Forecasting, J. Hydrol. Eng., 27, 05022004, https://doi.org/10.1061/(ASCE)HE.1943-5584.0002176, 2022. 
Adnan, R. M., Yuan, X., Kisi, O., Yuan, Y., Tayyab, M., and Lei, X.: Application of soft computing models in streamflow forecasting. In Proceedings of the institution of civil engineers-water, Manage., 172, 123–134, https://doi.org/10.1680/jwama.16.00075, 2019. 
Adnan, R. M., Liang, Z., Heddam, S., Zounemat-Kermani, M., Kisi, O., and Li, B.: Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs, J. Hydrol., 586, 124371, https://doi.org/10.1016/j.jhydrol.2019.124371, 2020. 
Ali, S. A., Aadhar, S., Shah, H. L., and Mishra, V.: Projected increase in hydropower production in India under climate change, Sci. Rep.​​​​​​​, 8, 1–12, https://doi.org/10.1038/s41598-018-30489-4, 2018. 
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This study examines, for the first time, the potential of various machine learning models in streamflow prediction over the Sutlej River basin (rainfall-dominated zone) in western Himalaya during the period 2041–2070 (2050s) and 2071–2100 (2080s) and its relationship to climate variability. The mean ensemble of the model results shows that the mean annual streamflow of the Sutlej River is expected to rise between the 2050s and 2080s by 0.79 to 1.43 % for SSP585 and by 0.87 to 1.10 % for SSP245.
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Hydrological response to climatic and cryospheric changes...