Discrete global grid systems have been proposed as a data model for an emerging digital earth framework capable of integrating heterogeneous types of spatial data. In northern communities experiencing rapid environmental change, a mix of locally produced and globally managed data are often required. While models and satellites produce spatially explicit representations of environmental processes, communities are also being engaged in monitoring through citizen science and community-based monitoring. In this report, we outline a new data model based on a DGGS for integration of these two forms of spatial data. A relational hybrid data model is presented and sample applications for monitoring change presented. Preliminary results indicate significant performance gains over traditional spatial data architectures. Given the need for a mix of local and cloud-based storage in many applications in small communities, further research is needed to identify optimal application configurations.