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The integrated distributed hydrological model ParFlow-CLM was used to predict water and energy transport between subsurface, land surface, and atmosphere for the Stettbach headwater catchment, Germany. Based on this model, global sensitivity analysis was performed using the Latin-Hypercube (LH) sampling strategy followed by the One-factor-Ata-Time (OAT) method to identify the most influential and interactive parameters affecting the main hydrologic processes. total 12 parameters were evaluated including soil hydraulic properties, storage, Manning coefficient, leaf area index, stem area index, and aerodynamic resistance that characterize water and energy fluxes in soil and vegetation. In addition, the sensitivity analysis was also carried out for different slopes and meteorological conditions to test the transferability of the results regions with other topographies and climates. Our results show that the simulated energy fluxes, i.e. latent heat flux and sensible heat flux are sensitive to the parameters such as wilting point, leaf area index, and stem area index, especially for steep slope and subarctic climate conditions. The simulated soil evaporation, plant transpiration, infiltration, and runoff, are most sensitive to soil porosity, the van Genuchten parameter n representing the soil pore size distribution, soil wilting point, and leaf area index. The subsurface soil water storage and groundwater storage are most sensitive to soil porosity, while the surface water storage was most sensitive to the soil roughness parameter. For the different slope and climate conditions, the rank order of input parameter sensitivity is consistent, but the magnitude of parameter sensitivity is very different. The strongest deviation in parameter sensitivity occurred for sensible heat flux under the different slope conditions as well as for transpiration under different climate conditions. Overall, this study provides an insight into the most important input parameters that control hydrological fluxes and how the simulated variables vary with the change in parameter values, which can improve our understanding of the key processes in the model and help us to reduce the computational demands of completing multiple simulations of expensive domains. [ABSTRACT FROM AUTHOR]