Current feature tracking frameworks in sensor networks exploit advantages of either mobility, where mobile sensors can provide micro scale information of a small sensing area or of numerical models that can provide macro scale information of the environment but not both. With the continual development of sensor networks, mobility becomes an important feature to integrate next generation sensing systems. In addition, recent advances in environmental modeling also allow us to better understand basic behavior of the environment. In order to further improve existing sensing systems, we need a new framework that can take advantage of existing fixed sensor networks, mobile sensors and numerical models. We develop CoTrack, a Collaborative Tracking framework, that allows mobile sensors to cooperate with fixed sensors and numerical models to accurately track dynamic features in an environment. The key innovation in CoTrack is the incorporation of numerical models at different scales along with sensor measurements to guide mobile sensors for tracking. The framework includes three components: a macro model for large-scale estimation, a micro model for locale estimation of specific features based on sensor measurements, and an adaptive sampling scheme that guides mobile sensors to accurately track dynamic features. We apply our framework to track salinity intrusion in the Columbia River estuary in Oregon, United States. Our framework is fast and can reduce tracking error by more than 50% compared to existing data assimilation frameworks and state-of-the-art numerical models.