Quantifying the variability of hydro-meteorological variables and various land-surface processes in response to the diverse sources of uncertainties arising from the structure of hydrological models, model parameters, calibration procedures, and schemes related to the various physical process representations is a significant scientific endeavour with important implications in hydrology. Sensitivity analysis is the only plausible way to provide valuable insights into how different parameters influence model outputs and helps evaluate the rationality of each model parameter. Hence, the present study employs sensitivity analysis for comprehensive evaluation of the impact of an enhanced hydrological parameterized and newly developed WRF-Hydro model towards the simulation of hydro-meteorological variables, in terms of model parameter uncertainty and structural variability. The study is carried out in the vicinity of the Krishna River basin in India with a sub grid-scale of 200 m. We evaluate the model's fidelity to accurately simulate streamflow and key hydrometeorological variables, including Evapotranspiration, soil moisture, and land heat and moisture fluxes, through a series of sensitivity experiments. These experiments quantify the impact of uncertainties related to land surface model parameterization, thresholds of model parameters, and structural configurations of the WRF-Hydro framework. The characteristics of the simulations thus generated have been validated against in-situ gauge data and remote sensing-based multi-platform observations, revealing a significant improvement in the simulation accuracy, with correlation coefficients exceeding 0.9 and 0.7 for soil moisture and soil temperature, respectively. Our findings show marked enhancement in the model’s predictive accuracy. By identifying the configurations with superior performance, this study provides practical insight for selecting optimal model parameterizations, thereby contributing to improving the hydro-meteorological forecasting frameworks and supporting better water management practices in regions with limited data availability.