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| Created: | Apr 03, 2019 at 2:34 p.m. | |
| Last updated: | Apr 08, 2019 at 4:43 p.m. | |
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Abstract
Reproducible model development and evaluation environment.
WHAT
Problem Statement:What is a suitable model development and evaluation for both existing NWM-subset(s) and new/novel model formulation investigation(s)?
WHY
Goal: Work with NWM-subset(s) and model formulation investigation(s) to design and demonstrate a base-line development and testing environment for reproducible model development and evaluation.
HOW
Approach: Review literature and community practices for reproducible model development and evaluation. Start with existing NWM containerization scheme. Determine appropriate abstraction to encapsulate a variety of hydrologic models and formulations. Implement example in cooperation with other themes.
Student Learning Goals: Students learn and demonstrate modern best practices for reproducible model formulation and software development.
Links to other projects: If completed successfully, outcomes of projects working with both subsets of them NWM and newly developed formulations should work with the approach implemented here. A final demonstration could compare new formulations to existing NWM using this same environment and evaluation architecture.
Training opportunities: This project will be part of the Scaling theme: students who participate in this theme will have 3 days of intensive training at the SI, including creating NWM subsets for CZO watersheds, running the NWM using jupyter notebooks/docker containers, using hydrologic data to evaluate hydrologic processes, using and analyzing CZO data.
Supplementary Materials:
Explore research toward community model development. https://doi.org/10.1002/2015WR017910 https://doi.org/10.1002/2016WR019285
Install and work through basic tutorials with Docker. https://docs.docker.com/get-started/ https://docker-curriculum.com/
DATA - What (or what types of) input data will be required?
Model Data: CUAHSI tool for NWM cutouts and forcings.
Observed Data: Readily available NWIS data.
Subject Keywords
Content
README.md
Reproducible model development and evaluation environment.
WHAT
Problem Statement: What is a suitable model development and evaluation for both existing NWM-subset(s) and new/novel model formulation investigation(s)?
WHY
Goal: Work with NWM-subset(s) and model formulation investigation(s) to design and demonstrate a base-line development and testing environment for reproducible model development and evaluation.
HOW
Approach: Review literature and community practices for reproducible model development and evaluation. Start with existing NWM containerization scheme. Determine appropriate abstraction to encapsulate a variety of hydrologic models and formulations. Implement example in cooperation with other themes.
Student Learning Goals: Students learn and demonstrate modern best practices for reproducible model formulation and software development.
Links to other projects: If completed successfully, outcomes of projects working with both subsets of them NWM and newly developed formulations should work with the approach implemented here. A final demonstration could compare new formulations to existing NWM using this same environment and evaluation architecture.
Training opportunities: This project will be part of the Scaling theme: students who participate in this theme will have 3 days of intensive training at the SI, including creating NWM subsets for CZO watersheds, running the NWM using jupyter notebooks/docker containers, using hydrologic data to evaluate hydrologic processes, using and analyzing CZO data.
Supplementary Materials:
Explore research toward community model development. https://doi.org/10.1002/2015WR017910 https://doi.org/10.1002/2016WR019285
Install and work through basic tutorials with Docker. https://docs.docker.com/get-started/ https://docker-curriculum.com/
DATA - What (or what types of) input data will be required?
Model Data: CUAHSI tool for NWM cutouts and forcings.
Observed Data: Readily available NWIS data.
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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