Abstract
Accurate forecast of daily streamflow of the major Himalayan rivers is crucial for understanding changes in their hydrological regimes caused by precipitation variability, as well as their potential implications for hydropower generation and flood management in the downstream region. However, the study pertaining to daily streamflow forecasting in the western Himalayas is lacking, primarily due to the limited availability and accessibility of long-term daily gauged data. In this study, four machine learning (ML) models, namely support vector machine, random forest, deep neural networks, and bi-directional long-short-term memory (BLSTM), were employed to forecast daily streamflow in the Sutlej river basin. The models were trained (11 years) and tested (2 years) using publicly accessible earth observation and reanalysis datasets for a period of 13 hydrological years, grouped into four distinct scenarios, denoted as M1, M2, M3, and M4. In each scenario, feature parameters were generated from the pool of six hydro-meteorological variables namely- precipitation (P), air temperature (\({{\text{T}}}_{{\text{a}}}\)), evapotranspiration (ET), snowfall, snow cover area (SCA), and discharge. The optimal feature combinations (e.g., scenarios) were identified using the time lag cross correlation technique. All of the ML models demonstrated reasonable performance (NSE > 0.82, R > 0.91, and RMSE < 101.2 m3/s) in simulating streamflow during testing period, the BLSTM model showed a slightly superior level of accuracy across all scenarios (NSE = 0.97, R = 0.99 and RMSE = 42.7 m3/s for M4). The BLSTM was further used to reconstruct daily timeseries discharge for the period 2012–2021. The variability in \({{\text{T}}}_{{\text{a}}}\), ET, and P exerts a greater influence on streamflow generation in the glacierized catchment in comparison to snowfall. The observed phenomenon can be ascribed to the dampening effect of snowfall on discharge, as their effects become apparent within a timeframe of five to seven days.
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Data availability
No datasets were generated or analysed during the current study. The code developed for this work is available at GitHub repository: https://github.com/jaydharpure2007/Earth_Science_Informatics.
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Acknowledgements
We are thankful to the editor and three anonymous reviewers for providing the valuable comments and suggestions, which improved the manuscript. We would like to acknowledge the Centre for Excellence in Disaster Mitigation and Management, Indian Institute of Technology Roorkee, India, for providing necessary infrastructure facilities. We are also thankful to the Ministry of Education (MoE) and National Centre for Polar and Ocean Research, Goa, India, for providing the necessary financial support. Our sincere appreciation to the National Snow and Ice Data Center (NSIDC) for providing MODIS snow cover products and Copernicus Climate Data Store for providing ERA5 Land reanalysis data in open access.
Funding
This is sponsored by National Centre for Polar and Ocean Research, Goa, India (Grant number- NCAOR/ 2018/HiCOM/ 03/).
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JKD and AP wrote the original draft of the manuscript. JKD, AG and AP conceptualized the study. Data collection was performed by SKJ. Statistical analysis and data interpretation were conducted by JKD, AP, AG, AVK, and DS. Funds collection was managed by AG and SKJ. All authors have read and approved the manuscript.
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Communicated by: Hassan Babaie
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Dharpure, J.K., Goswami, A., Patel, A. et al. Synergistic approach for streamflow forecasting in a glacierized catchment of western Himalaya using earth observation and machine learning techniques. Earth Sci Inform (2024). https://doi.org/10.1007/s12145-024-01322-6
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DOI: https://doi.org/10.1007/s12145-024-01322-6