Performance evaluation of a physically informed ANN machine learning model for short-term and extended-range streamflow prediction in the Himalayan Catchment

Accurate streamflow prediction is vital for effective reservoir operation, flood forecasting, and water resource planning, particularly in snow and glacier-fed Himalayan catchments. Existing studies reveal that physically based models (PBMs) often face challenges such as parameter uncertainty, scale mismatches, and the need for extensive calibration. In contrast, data-driven models (DDMs) depend heavily on large datasets and lack integration of physical processes. Addressing these limitations, this study presents a Physically Informed Artificial Neural Network (PIANN) framework that integrates climatic, remote sensing, and hydrological inputs for streamflow prediction at daily and ten-daily time scales in the Tehri Catchment, Indian Himalayas. Two model configurations were evaluated: (i) a four-variable model including rainfall (R _t ), temperature (T _t ), snow cover area (SCA _t ), and previous discharge (Q _t-1 ), and (ii) a three-variable model excluding Q _t-1 . The short-term (daily) model with four variables achieved high accuracy (NSE = 0.957, R² = 0.955 in calibration; NSE = 0.939, R² = 0.940 in validation), but in three variable model, accuracy is reduced (NSE = 0.837, R² = 0.845 in calibration; NSE = 0.734, R² = 0.753 in validation). The ten-daily model also attained high performance (NSE = 0.969, R² = 0.973 in calibration; NSE = 0.954, R² = 0.966 in validation). Excluding Q _t-1 reduced model accuracy, underscoring its importance in maintaining flow memory and baseflow continuity. The model consistently captured low- and high-flow events, exhibiting physical plausibility and generalisation across extreme hydrological conditions. Error model analysis showed minimal change in prediction accuracy, indicating the robustness of the original PIANN outputs. This study demonstrates the strength of integrating physical understanding into ANN frameworks, offering a scalable and reliable tool for streamflow prediction in complex, data-scarce mountainous regions.