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watershed-workflow

Python workflows for data-rich, hyper-resolution simulations of hydrologic models on watersheds.

https://github.com/environmental-modeling-workflows/watershed-workflow

Science Score: 49.0%

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    CITATION.cff file
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    codemeta.json file
    Found codemeta.json file
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    .zenodo.json file
    Found .zenodo.json file
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    DOI references
    Found 2 DOI reference(s) in README
  • ○
    Academic publication links
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    Committers with academic emails
    3 of 10 committers (30.0%) from academic institutions
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    Scientific vocabulary similarity
    Low similarity (14.8%) to scientific vocabulary
Last synced: 7 months ago · JSON representation

Repository

Python workflows for data-rich, hyper-resolution simulations of hydrologic models on watersheds.

Basic Info
  • Host: GitHub
  • Owner: environmental-modeling-workflows
  • License: other
  • Language: Python
  • Default Branch: master
  • Size: 184 MB
Statistics
  • Stars: 72
  • Watchers: 4
  • Forks: 34
  • Open Issues: 7
  • Releases: 10
Created over 6 years ago · Last pushed 7 months ago
Metadata Files
Readme License Authors

README.md

Watershed Workflow

Docs Release

Build Status Issues Issues

sample image

Please prefer to see our documentation.

Watershed Workflow is a python-based, open source chain of tools for generating meshes and other data inputs for hyper-resolution hydrology, anywhere in the (conterminous + Alaska?) US.

Hyper-resolution hydrologic models have huge data requirements, thanks to their large extent (full river basins) and very high resolution (often ~10-100 meters). Furthermore, most process-rich models of integrated, distributed hydrology at this scale require meshes that understand both surface land cover and subsurface structure. Typical data needs for simulations such as these include:

  • Watershed delineation (what is your domain?)
  • Hydrography data (river network geometry, hydrographs for model evaluation)
  • A digital elevation model (DEM) for surface topography
  • Surface land use / land cover
  • Subsurface soil types and properties
  • Meterological data,

and more.

This package is a python library of tools and a set of jupyter notebooks for interacting with these types of data streams using free and open (both free as in freedom and free as in free beer) python and GIS libraries and data. Critically, this package provides a way for automatically and quickly downloading, interpreting, and processing data needed to generate a "first" hyper-resolution simulation on any watershed in the conterminous United States (and most of Alaska/Hawaii/Puerto Rico).

To do this, this package provides tools to automate downloading a wide range of open data streams, including data from United States governmental agencies, including USGS, USDA, DOE, and others. These data streams are then colocated on a mesh which is generated based on a watershed delineation and a river network, and that mesh is written in one of a variety of mesh formats for use in hyper-resolution simulation tools.

Note: Hypothetically, this package works on all of Linux, Mac, and Windows. It has been tested on the first two, but not the third.

Installation

Visit our Installation documentation.

For more...

Funding, attribution, etc

This work was supported by multiple US Department of Energy projects, and was mostly developed at the Oak Ridge National Laboratory. Use of this codebase in the academic literature should cite:

Coon, E. T., & Shuai, P. (2022). Watershed Workflow: A toolset for parameterizing data-intensive, integrated hydrologic models. Environmental Modelling & Software, 157, 105502.

Collaborators and contributions are very welcome!

Owner

  • Name: environmental-modeling-workflows
  • Login: environmental-modeling-workflows
  • Kind: organization

GitHub Events

Total
  • Create event: 17
  • Commit comment event: 1
  • Release event: 1
  • Issues event: 22
  • Watch event: 6
  • Delete event: 18
  • Issue comment event: 33
  • Push event: 166
  • Pull request review comment event: 5
  • Pull request review event: 11
  • Pull request event: 44
  • Fork event: 4
Last Year
  • Create event: 17
  • Commit comment event: 1
  • Release event: 1
  • Issues event: 22
  • Watch event: 6
  • Delete event: 18
  • Issue comment event: 33
  • Push event: 166
  • Pull request review comment event: 5
  • Pull request review event: 11
  • Pull request event: 44
  • Fork event: 4

Committers

Last synced: 7 months ago

All Time
  • Total Commits: 498
  • Total Committers: 10
  • Avg Commits per committer: 49.8
  • Development Distribution Score (DDS): 0.11
Past Year
  • Commits: 120
  • Committers: 4
  • Avg Commits per committer: 30.0
  • Development Distribution Score (DDS): 0.108
Top Committers
Name Email Commits
Ethan Coon c****t@o****v 443
benliebersohn b****n@g****m 17
saubhagya-gatech s****e@o****m 15
pinshuai p****8@g****m 10
Rich Fiorella r****a@l****v 5
jgomezvelez j****z@g****m 4
Soumendra Bhanja s****a@g****m 1
Jemma Stachelek j****a 1
Bo Gao 8****b 1
Benjamin Liebersohn l****t@o****v 1
Committer Domains (Top 20 + Academic)
ornl.gov: 2 lanl.gov: 1

Issues and Pull Requests

Last synced: 7 months ago

All Time
  • Total issues: 44
  • Total pull requests: 85
  • Average time to close issues: 9 months
  • Average time to close pull requests: 27 days
  • Total issue authors: 12
  • Total pull request authors: 8
  • Average comments per issue: 1.59
  • Average comments per pull request: 0.71
  • Merged pull requests: 62
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 12
  • Pull requests: 32
  • Average time to close issues: about 2 months
  • Average time to close pull requests: 12 days
  • Issue authors: 4
  • Pull request authors: 6
  • Average comments per issue: 0.42
  • Average comments per pull request: 0.38
  • Merged pull requests: 19
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • ecoon (13)
  • saubhagya-gatech (8)
  • rfiorella (8)
  • pinshuai (4)
  • lijingwang (3)
  • zexuanxu (2)
  • Orilu (1)
  • jsta (1)
  • daniellivingston (1)
  • gaobhub (1)
  • ZhiLiHydro (1)
  • akirke (1)
Pull Request Authors
  • ecoon (31)
  • saubhagya-gatech (25)
  • pinshuai (13)
  • jgomezvelez (6)
  • rfiorella (5)
  • jsta (2)
  • soumendrabhanja (2)
  • gaobhub (1)
Top Labels
Issue Labels
enhancement (7) geopandas (4) bug (1) v2.0 (1) meta (1)
Pull Request Labels

Dependencies

requirements.txt pypi
  • rosetta-soil *
.github/workflows/ats_user_container.yml actions
  • actions/checkout v2 composite
  • docker/build-push-action v3 composite
  • docker/login-action v2 composite
  • docker/setup-buildx-action v2 composite
.github/workflows/env.yml actions
  • actions/checkout v2 composite
  • docker/build-push-action v3 composite
  • docker/login-action v2 composite
  • docker/setup-buildx-action v2 composite
.github/workflows/main.yml actions
  • actions/checkout v2 composite
  • docker/build-push-action v3 composite
  • docker/login-action v2 composite
  • docker/setup-buildx-action v2 composite
.github/workflows/user_container.yml actions
  • actions/checkout v2 composite
  • docker/build-push-action v3 composite
  • docker/login-action v2 composite
  • docker/setup-buildx-action v2 composite