Ensuring the reproducibility of scientific studies is crucial for advancing research, with effective data management serving as a cornerstone for achieving this goal. Ensuring the reproducibility of scientific studies is crucial for advancing research, with effective data management serving as a cornerstone for achieving this goal. In hydrologic and environmental modeling, spatial data is used as model input and sharing of this spatial data is a main step in the data management process. However, by focusing only on sharing data at the file level through small files rather than providing the ability to Find, Access, Interoperate with, and directly Reuse subsets of larger datasets, online data repositories are missing an opportunity to foster more reproducible science. This leads to challenges when accommodating large files which benefit from consistent data quality and seamless geographic extent. To utilize the benefits of large datasets, the objective of this study is therefore to create and test an approach for exposing large extent spatial (LES) datasets to support catchment-scale hydrologic modeling needs. GeoServer and THREDDS Data Server connected to HydroShare were used to provide seamless access to LES datasets. The approach is demonstrated using the Regional Hydro-Ecologic Simulation System (RHESSys) for three different sized watersheds in the US. We assessed data consistency across three different data acquisition approaches: the ‘conventional’ approach, which involves sharing data at the file level through small files, as well as GeoServer, and THREDDS Data Server. This assessment is conducted using RHESSys to evaluate differences in model streamflow output. This approach provides an opportunity to serve datasets needed to create catchment models in a consistent way that can be accessed and processed to serve individual modeling needs.

This collection resource (HS 1) comprises 7 individual HydroShare resources (HS 2-8), each containing different datasets or workflows. These 7 HydroShare resources consist of the following: three resources for three state-scale LES datasets (HS 2-4), one resource with Jupyter notebooks for three different approaches and three different watersheds (HS 5), one resource for RHESSys model instances (i.e., input) of the conventional approach and observation data for all data access approaches in three different watersheds (HS 6), one resource with Jupyter notebooks for automated workflows to create LES datasets (HS 7), and finally one resource with Jupyter notebooks for the evaluation of data consistency (HS 8). More information on each resource is provided within it.