This is a collection resource for "Comparing Approaches to Achieve Reproducible Computational Modeling for Hydrological and Environmental Systems" manuscript in Environmental Modeling and Software.

HS-1: Collection Resource for Comparing Approaches to Achieve Reproducible Computational Modeling for Hydrological and Environmental Systems

For SUMMA simulation, we created two SUMMA model instances.
HS-2. Model Instance for the Impact of Stomatal Resistance Parameterizations on ET of SUMMA Model in Aspen stand at Reynolds Mountain East
HS-3. Model Instance for the Impact of Lateral Flow Parameterizations on Runoff of SUMMA Model at Reynolds Mountain East

For SUMMA simulation, we created a SUMMA model program
HS-4: Remote Approach-11: Using HPC Cluster (Rivanna: HPC at University of Virginia) for the reproducibility of SUMMA modeling

There are five HS resources for reproducible approaches.
HS-5. A Virtual Box image that includes five local approaches:
- Approach-1 Compiling the core model software
- Approach-2 Containerizing the core model software only with Docker
- Approach-3 Containerizing all software with Docker
- Approach-4 Containerizing all software with Singularity
- Approach-5 Containerizing all software and modeling workflows with Sciunit
HS-6. Approach-6 Using CUAHSI JupyterHub
HS-7. Approach-7 Using CyberGIS-Jupyter for water
HS-8. Approach-8 Using Sciunit in CUAHSI JupyterHub
HS-9. Approach-9 Using Sciunit in CyberGIS-Jupyter for water

Lastly, we created a notebook for performance tests using the different reproducible approaches.
HS-10. Jupyter notebook for performance test using the different reproducible approaches

In addition, there are three GitHub repositories for reproducible approaches in related resources in the reference section.
Git-1. Approach-10 Using Binder
Git-2. Description of Approach-3 to show how to create Docker environments
Git-3. Description of Approach-4 and 11 to show how to use a Singularity image in HPC