Hydrological models play a critical role in advancing environmental modeling. They are particularly significant in contexts requiring short-term decision-making, where real-time simulation capabilities support timely and informed actions. The advancement of Internet of Things (IoT) technology has provided new opportunities for enhancing real-time hydrological modeling. However, most widely used hydrological models were originally designed as desktop applications with process-oriented execution workflows, which hinder fine-grained state access and standardized integration with IoT systems, thereby limiting their suitability for real-time, observation-driven modeling scenarios. This paper proposes a method for describing hydrological model components and data using a standard IoT conceptual model. By establishing a generic object-oriented framework, we integrate hydrological models with IoT systems, systematically representing model elements and data while mapping them to the Open Geospatial Consortium (OGC) SensorThings API conceptual model. This approach enables real-time, observation-driven hydrological modeling and facilitates fine-grained state acquisition. Finally, we developed a prototype system based on the Storm Water Management Model (SWMM) and validated the feasibility of our methodology through case studies.