Science Score: 67.0%
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✓CITATION.cff file
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✓codemeta.json file
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✓.zenodo.json file
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✓DOI references
Found 3 DOI reference(s) in README -
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○Scientific vocabulary similarity
Low similarity (11.0%) to scientific vocabulary
Repository
pyflood2022
Basic Info
- Host: GitHub
- Owner: jlerat
- License: mit
- Language: Python
- Default Branch: master
- Size: 34.2 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 1
- Releases: 3
Metadata Files
README.md
pyflood2022
This package contains the data and code used in the paper by Lerat and Vaze (2025), Communications Earth & Environment, in review.
Data
The data provided in this package is in the data/floods folder and includes * major_floods.csv: List of major floods in Australia.
sites_info.csv: List of gauging stations.
streamflowdatasites_info.csv: Additional information related to upstream/downstream flow propagation between sites.
flooddatav5.zip: List of flood events and associated data including peak flow, maximum 10 days runoff and results of flood frequency analysis.
flooddatacensored_v5.zip: Same data with flood frequency results using a censored fit where the lowest 20% of annual maximums are left censored.
propag_data.csv: Data on flood propagation from site to site during the 2022 flood.
streamflowdata138110.csv: Streamflow data during the 2022 flood for site 138110. Similar data is available for sites 202001, 203010, 203014.
awap_data.nc: Gridded rainfall data during the 2022 flood.
awrav6data.nc: Gridded soil moisture data during the 2022 flood.
Code
The code used to generate the figures of the paper is provided as separate script for each figure:
- map_plot.py: Script to generate Figure 1 (map).
- propag_plot.py: Script to generate Figure 2 (peak time informations).
- scatter_plot.py: Script to generate Figure 3 (scatter plots).
- regionalfloodsmaps.py: Script to generate Figure 4 (maps of regional flood events).
- surprise_plot.py: Script to generate Figure 5 (statistics of the surprise index).
- regionalfloodsstats.py: Script to generate Figures from the supplementary material (statistics related to regional flood events).
Attribution
This project is licensed under the MIT License, which allows for free use, modification, and distribution of the code under the terms of the license.
For proper citation of this project, please refer to the CITATION.cff file, which provides guidance on how to cite the software and relevant publications.
Owner
- Name: Julien Lerat
- Login: jlerat
- Kind: user
- Repositories: 1
- Profile: https://github.com/jlerat
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: pyflood2022
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Julien
family-names: Lerat
email: julien.lerat@csiro.au
affiliation: CSIRO
orcid: '0000-0003-4521-8874'
identifiers:
- type: doi
value: 10.5281/zenodo.13928254
repository-code: 'https://github.com/jlerat/pyflood2022'
url: 'https://github.com/jlerat/pyflood2022'
abstract: >-
This package includes the code and data supporting the
2022 flood paper.
keywords:
- Flood
- Hydrology
- Australia
license: MIT
references:
- type: article
title: When the impossible happens, how February 2022 event redefines extreme floods in Australia
authors:
- family-names: "Lerat"
given-names: "Julien"
orcid: "0000-0003-4521-8874"
year: 2024
journal: "Communications Earth & Environment"
volume: "in review"
url: "https://www.researchsquare.com/article/rs-5575327/v1"
GitHub Events
Total
- Release event: 3
- Delete event: 8
- Public event: 1
- Push event: 17
- Pull request event: 13
- Fork event: 1
- Create event: 11
Last Year
- Release event: 3
- Delete event: 8
- Public event: 1
- Push event: 17
- Pull request event: 13
- Fork event: 1
- Create event: 11
Dependencies
- actions/checkout v2 composite
- actions/setup-python v3 composite
- actions/upload-artifact v3 composite
- matplotlib >= 3.8.4
- netcdf4 >= 1.6.4
- numpy >= 1.26.4
- pandas >= 2.2.1
- pyproj >= 3.5.0
- pyshp >= 2.3.1
- python >= 3.10.6
- scipy >= 1.13.0
- statsmodels >= 0.14.1