Kumar, M., Tiwari, R. K., Rautela, K. S., Kumar, K., Khajuria, V., Verma, I., ... & Elhag, M. (2024). Comparative Assessment of Process Based Models for Simulating the Hydrological Response of the Himalayan River Basin. Earth Systems and Environment, 1-15

The current study presents a comprehensive assessment of hydrological models, including the Snowmelt Runoff Model (SRM), MIKE HYDRO RIVER NAM model, and Soil and Water Assessment Tool (SWAT), for simulating runoff dynamics in the Beas River Basin (BRB). Utilizing data spanning seven years from 2014 to 2020, the models underwent rigorous calibration and validation processes to evaluate their performance under varying hydrological conditions. The SRM model exhibited commendable performance, with high correlation coefficients (R²) of 0.85 during calibration and 0.82 during validation, indicating strong agreement between observed and simulated runoff volumes. Similarly, the MIKE HYDRO RIVER NAM model demonstrated satisfactory performance, albeit with a slightly higher root mean square error (RMSE), indicating a reasonable fit between observed and simulated data. In contrast, the SWAT model exhibited relatively lower performance metrics, particularly regarding R² and Nash-Sutcliffe Efficiency (NSE) values, suggesting limitations in accurately capturing runoff dynamics, especially during peak flow events. Comparison of model performance highlighted the superior capability of the SRM and MIKE HYDRO RIVER NAM models in simulating runoff dynamics, attributed to their robust representation of hydrological processes and comprehensive consideration of relevant parameters. Analysis of water resource management in the BRB emphasized the importance of understanding flow dynamics, particularly seasonal variations in water availability, for effective water resource management. Overall, this study underscores the significance of accurate hydrological modelling for informed decision-making in water resource management and highlights the potential of the SRM and MIKE HYDRO RIVER NAM models for such applications.