Abstract: Setting parameters for hydrological models requires not only specialized knowledge but also tedious operation steps. This is a major difficulty in hydrological modeling that largely constrains the ease of use of hydrological models. Using a knowledge-driven method, the knowledge on parameter setting was divided into the knowledge on parameter extraction and the knowledge on parameter value range. The above knowledge was formalized and inference engines were designed for setting parameters in hydrological modeling intelligently. A prototype system for intelligent hydrological modeling was implemented using web service, workflow, and automatic calibration of parameters. A case study of automatic intelligent parameter setting was conducted for TOPMODEL in a real watershed. The results showed that the knowledge-driven method was able to conduct parameter settings automatically and achieve satisfying modeling results. Therefore, the proposed knowledge-driven method and the intelligent system have great potential to simplify hydrological modeling processes.