Please wait for the process to complete.
HS-1. Comparing Approaches to Achieve Reproducible Computational Modeling for Hydrological and Environmental Systems
||This resource does not have an owner who is an active HydroShare user. Contact CUAHSI (email@example.com) for information on this resource.|
|Storage:||The size of this collection is 2.4 KB|
|Created:||Nov 24, 2020 at 7:05 a.m.|
|Last updated:|| Jan 29, 2023 at 1 p.m.
|Citation:||See how to cite this resource|
|+1 Votes:||Be the first one to this.|
|Comments:||No comments (yet)|
This is a collection resource for "Comparing containerization-based approaches for reproducible computational modeling of environmental systems" manuscript in Environmental Modeling and Software Journal.
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
Then there is a HS resource for reproducible approaches in local computational environments.
HS-4. 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
Next, there are four HS resources and a GitHub repository for reproducible approaches in remote computational environments.
HS-5. Approach-6 Using CUAHSI JupyterHub
HS-6. Approach-7 Using CyberGIS-Jupyter for water
HS-7. Approach-8 Using Sciunit in CUAHSI JupyterHub
HS-8. Approach-9 Using Sciunit in CyberGIS-Jupyter for water
Git-1. Approach-10 Using Binder (https://github.com/uva-hydroinformatics/SUMMA_Binder.git)
Lastly, we created a notebook for performance tests using the different reproducible approaches.
HS-9. Jupyter notebook for performance test using the different reproducible approaches
For additional description, we created two GitHub repositories to show how to create Docker and Singularity image for Approach-2,3, and 4.
Git-2. Description of Approach-3 to show how to create Docker environments (https://github.com/uva-hydroinformatics/SUMMA_Docker_Training.git)
Git-3. Description of Approach-4 to show how to use a Singularity image (https://github.com/uva-hydroinformatics/SUMMA_Singularity_In_Rivanna.git)
As a result, we shared a created Singularity image for a model program resource.
HS-10: A singularity image for the reproducibility of SUMMA modeling
|This resource is described by||https://github.com/uva-hydroinformatics/SUMMA_Singularity_In_Rivanna.git|
|This resource is described by||https://github.com/uva-hydroinformatics/SUMMA_Docker_Training.git|
|This resource is described by||https://github.com/uva-hydroinformatics/SUMMA_Binder.git|
This resource was created using funding from the following sources:
|Agency Name||Award Title||Award Number|
|National Science Foundation||EarthCube Data Capabilities: Collaborative Research: Integration of Reproducible Methods into Community Cyberinfrastructure||ICER-1928369, ICER-1928315|
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.http://creativecommons.org/licenses/by/4.0/