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The WRF model is nowadays the most widely applied mesoscale numerical weather prediction model. Its land-surface hydrological modeling module, WRF-Hydro, which is designed to facilitate the land-surface modeling being coupled with WRF, draws more and more attentions from both the meteorological and the hydrological community. In this study, four sensitive and principle parameters of WRF-Hydro are tested in semi-humid and semi-arid areas of northern China. These parameters include the runoff infiltration parameter (REFKDT), the surface retention depth (RETDEPRT) controlled by a scaling parameter named RETDEPRTFAC, the channel Manning roughness parameter (MannN), and the overland flow roughness parameter (OVROUGHRT) controlled by the scaling parameter OVROUGHRTFAC. WRF-Hydro is designed with a 100-m horizontal grid spacing in two catchments of northern China. The performance of WRF-Hydro with different parameterisation combination schemes is tested for simulating a typical 24-h storm events with uniform rainfall evenness in space and time. The Nash-Sutcliffe efficiency and the root mean squared error of the simulated streamflow, together with the cumulative amount of the simulated rainfall is chosen as the evaluation statistics. It is found that REFKDT and MannN are the most sensitive parameter among the four parameters, and the case is especially evident with unsaturated soil conditions. In order to obtain the most reasonable value range, REFKDT and MannN are further verified by another three 24-h storm events with different spatial and temporal evenness. The range of REFKDT from 2.0 to 3.0, and the MannN scale factor from 1.5 to 1.8 is found to give the best results. The findings of this study can be used as references for calibration of the WRF-Hydro modeling system in semi-humid and semi-arid regions with similar rainfall-runoff response characteristics. The methodologies to design and test the combination schemes of parameterisations can also be regarded as a reference for evaluation of the WRF/WRF-Hydro coupled system for land-surface process modeling.