With the advancement of wireless communication and miniaturization of digital electronics, long term observation of remote hydrologic systems using adaptive sensor networks at high spatio-temporal resolutions and across multiple scales, has become a reality. However, for large spatial scales embedded multi-sensor networks with fine temporal sampling rates, the amount and distribution of data generated by these networks becomes unmanageably large. While the sensor network installation itself is generally supported by basic data management software, in the hydrologic sciences there is little support available to directly incorporate the data generated into the hydrologic model. We contend that a seamless transfer of the observed data to the model can be achieved by developing a shared data model which will standardize storage and management of data both at the sensor base station and the hydrologic model. This will lead to enhanced data transfer integrity and will also result in direct input of the sensor network data to the model in realtime without having to go through intermediate pre-processing steps which are error prone. Here we present the shared Data Model structure along with its design considerations in terms of data types, identification of data-classes, relationships and constraints.