Supporting Materials for: Advancing Open and Reproducible Water Data Science by Integrating Data Analytics with an Online Data Repository
| Authors: | |
|---|---|
| Owners: | Jeffery S. Horsburgh |
| Type: | Resource |
| Storage: | The size of this resource is 1.6 MB |
| Created: | Oct 11, 2024 at 7:04 p.m. |
| Last updated: | Mar 10, 2025 at 1:38 p.m. |
| Published date: | Mar 10, 2025 at 1:38 p.m. |
| DOI: | 10.4211/hs.7440c7feae1f428d91c1e510d23d3e54 |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
|---|---|
| Views: | 475 |
| Downloads: | 43 |
| +1 Votes: | Be the first one to this. |
| Comments: | No comments (yet) |
Abstract
This HydroShare resource was created as a demonstration of how a reproducible data science workflow can be created and shared using HydroShare. The hsclient Python Client package for HydroShare is used to show how the content files for the analysis can be managed and shared automatically in HydroShare. The content files include a Jupyter notebook that demonstrates a simple regression analysis to develop a model of annual maximum discharge in the Logan River in northern Utah, USA from annual maximum snow water equivalent data from a snowpack telemetry (SNOTEL) monitoring site located in the watershed. Streamflow data are retrieved from the United States Geological Survey (USGS) National Water Information System using the dataretrieval package. Snow water equivalent data are retrieved from the United States Department of Agriculture Natural Resources Conservation Service (NRCS) SNOTEL system. An additional notebook demonstrates how to use hsclient to retrieve data from HydroShare, load it into a performant data object, and then use the data for visualization and analysis. For a full description of the workflows and technologies used, see the paper linked in the Related Resources section on this page.
Subject Keywords
Coverage
Spatial
Content
Related Resources
| This resource is described by | Horsburgh, J. S., Black, S., Castronova, A., Dash, P. K. (2025). Advancing open and reproducible water data science by intgrating data analytics with an online repository, Environmental Modelling & Software, 106422, https://doi.org/10.1016/j.envsoft.2025.106422 |
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
|---|---|---|
| National Science Foundation | Collaborative Research: Elements: Advancing Data Science and Analytics for Water (DSAW) | OAC 1931297 |
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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