ABSTRACT:

This HydroShare resource was created to support the study presented in Choi et al. (2024), titled "Toward Reproducible and Interoperable Environmental Modeling: Integration of HydroShare with Server-side Methods for Exposing Large-Extent Spatial Datasets to Models." 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 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 have missed an opportunity to foster more reproducible science. This has led to challenges when accommodating large files that benefit from consistent data quality and seamless geographic extent.

To utilize the benefits of large datasets, the objective of the Choi et al. (2024) study was 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 was demonstrated using the Regional Hydro-Ecologic Simulation System (RHESSys) for three different-sized watersheds in the US. Data consistency was assessed across three different data acquisition approaches: the 'conventional' approach, which involved sharing data at the file level through small files, as well as GeoServer and THREDDS Data Server. This assessment was conducted using RHESSys to evaluate differences in model streamflow output. This approach provided an opportunity to serve datasets needed to create catchment models in a consistent way that could be accessed and processed to serve individual modeling needs. For full details on the methods and approach, please refer to Choi et al. (2024). This HydroShare resource is essential for accessing the data and workflows that were integral to the study.

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.

ABSTRACT:

This HydroShare resource provides the Jupyter Notebooks for RHESSys End-to-End modeling workflow using the GeoServer approach at Spout Run, VA

To find out the instructions on how to run Jupyter Notebooks, please refer to the README file which is provided in this resource.

ABSTRACT:

RHESSys notebooks for Spout run simulation

ABSTRACT:

Notebook Tutorials for RHESSys Modeling using pyRHESSys: Watts Branch example

ABSTRACT:

This resource models outflow at USGS station 01665500 using HEC-HMS software. Precipitation data was imported from the Charlottesville-Albemarle regional airport, which is located about 13 miles northeast of the outflow gage. SCS-curve number, SCS Unit Hydrograph, baseflow recession, and Muskingum routing methods were selected in HEC-HMS. No canopy or surface method was used. The model was calibrated using a precipitation event on 05/05/2016, and tested using an event from 4/19-20/2015. Results show an underestimation of peak outflow of 15% compared to observed data for one model test. The most significant discrepancy between the model and observed outflow is peak flow time, which is likely due in large part to the 13 mile distance between precipitation and outflow gages. Differences may also be due to varying antecedent moisture conditions. This project was an assignment for CE-6230 (Hydrology) at the University of Virginia.