Data Lineage (if applicable). Please include versions (e.g., input and forcing data, models, and coupling modules; instrument measurements; surveys; sample collections; etc.)
1. Description of methods used for collection/generation of data:
Temperature and snow observations (snow course and snow pillow) obtained from government repositories. Elevation data obtained from GLOBE DEM. Model outputs generated by CRHM.
Manual snow survey site locations (Government of BC -
https://catalogue.data.gov.bc.ca/dataset/9f653102-5627-45a7-bd4c-686e365ee04a)
Manual snow survey site data (Government of BC -
https://catalogue.data.gov.bc.ca/dataset/705df46f-e9d6-4124-bc4a-66f54c07b228)
Snow pillow data (Government of BC -
https://aqrt.nrs.gov.bc.ca/Data/)
Climate Normals (ECCC -
https://climate.weather.gc.ca/climate_normals/index_e.html)
Global Land One-kilometer Base Elevation (NOAA -
https://doi.org/10.7289/V52R3PMS)
2. Methods for processing the data: NA
3. Instrument- or software-specific information needed to interpret the data:
The version of Python used in this analysis is 3.7.6. Required packages are listed below in the code-specific information section and in the notebook preamble.
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CODE-SPECIFIC INFORMATION FOR: BasinHypsometryCRHM-Final.ipynb
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1. Brief description of the code:
Jupyter notebook does the following:
- calculates and clusters basin hypsometries
- assesses the skill of simple linear accumulation gradients and synthetic temperatures against observational data
- imports results of the CRHM model runs to assess snow pack and snow melt responses to increased temperatures
- produces figures contained in the Shea et al. (2021) publication
2. List required dependencies:
CRHM 05/15/19
Python 3.7.6
datetime 4.3
matplotlib 3.1.2
matplotlib-base 3.1.2
pandas 0.25.3
glob2 0.7
scipy 1.3.2
geopandas 0.8.0
scikit-learn 0.22
3. Instructions to install or use the code:
Use jupyter notebook to open the code, and ensure that all packages are installed. Outputs from the CRHM model are provided in the data folder, but users can also obtain and run the CRHM model using the .obs files contained in the CRHMobs subfolder.
4. List any required input:
Data subfolder.
5. List expected output:
To produce figures used in the paper, create a new subfolder called 'Figure' and uncomment the plt.savefig() commands.