<p>Hydrologic models often exhibit inaccuracies in representing key hydrological fluxes due to uncertainties arising from the necessary simplification of complex processes and input data. Soil databases, commonly used in hydrological models, vary in format, resolution, and parameter range, leading to diverse approaches for generating soil inputs in process-based models. This study employs both linear (FOSM) and non-linear (iES) methods to quantify parameter and prediction uncertainty. A comparative perspective on how these approaches reflect uncertainty when using different soil databases is provided. The study area is the Mohaka catchment with an area of 2,428 km², situated within the Hawke’s Bay Region of New Zealand. Four different soil databases were used in this study (FSL, S-map, HWSD, and ISRIC) with different spatial resolutions and the number of soil units covering the catchment. Although similar model evaluation metrics were obtained for streamflow simulation using the different soil databases, flow prediction uncertainty varied significantly for average, low, and high flows. For example, low and high flow predictions showed particularly high uncertainties for the global, low-resolution ISRIC database. Conversely, the local soil database S-map produced the lowest uncertainty range for low and high flow conditions. These findings highlight that while different soil databases may yield similar performance statistics during calibration, selecting those that minimise variance in key predictions can improve the reliability of model predictions. The findings emphasise the importance of selecting an appropriate soil database to enhance model reliability for the purpose under consideration.</p>