  Current hydrological modeling methods combine data-driven Machine Learning
(ML) algorithms and traditional physics-based models to address their
respective limitations incorrect parameter estimates from rigid physics-based
models and the neglect of physical process constraints by ML algorithms.
Despite the accuracy of ML in outcome prediction, the integration of scientific
knowledge is crucial for reliable predictions. This study introduces a Physics
Informed Machine Learning (PIML) model, which merges the process understanding
of conceptual hydrological models with the predictive efficiency of ML
algorithms. Applied to the Anandapur sub-catchment, the PIML model demonstrates
superior performance in forecasting monthly streamflow and actual
evapotranspiration over both standalone conceptual models and ML algorithms,
ensuring physical consistency of the outputs. This study replicates the
methodologies of Bhasme, P., Vagadiya, J., & Bhatia, U. (2022) from their
pivotal work on Physics Informed Machine Learning for hydrological processes,
utilizing their shared code and datasets to further explore the predictive
capabilities in hydrological modeling.
