Python wrapped implementation of the Snobal model applied at a point, provided by  M3 Works.

The code in pointsnobal/c_snobal is the same underlying algorithms described in A spatially distributed energy balance snowmelt model for application in mountain basins (Marks 1999), which details iSnobal. This code was originally available in IPW.

This software takes in a csv of HOURLY input data and writes a csv of daily snowpack data.

🚀 Calling Snow Researchers & Students! 🚀

At M3 Works, we’re passionate about advancing scientific research and education! 🌍📊

That’s why we offer free access to our PointSnobal API for qualifying research and educational projects. Whether you’re modeling snow processes or exploring hydrology, we’re here to support your work!

🔑 Request an API key on our website -> https://m3works.io/contact

Let’s collaborate and push the boundaries of environmental modeling together! ❄️📡

This API is provided under the same license listed in this directory. The API and code are provided “as is”, and M3 Works LLC makes no guarantees of functionality, performance, or fitness for any particular purpose.

M3 Works LLC shall not be held liable for any direct, indirect, incidental, or consequential damages arising from the use or inability to use this API or its associated code.

Use of this API constitutes acceptance of the terms and conditions outlined in the accompanying license.

Example of api usage in python

from pathlib import Path
import requests
import pandas as pd

api_key = "<your key>"
api_id = "bktiz24e19"
file_path = Path("<path to your input csv file>")
elevation = 1000  # your point elevation in meters
url = f"https://{api_id}.execute-api.us-west-2.amazonaws.com/m3works/snobal"
params = {"elevation": elevation}

output_file_name = file_path.name.replace('inputs', 'snobal')
output_file = file_path.parent.joinpath(output_file_name)

# Headers
headers = {
    "x-api-key": api_key,
    "Content-Type": "text/csv"
}

print("Reading file and calling API")
# Load the CSV file as binary data
with open(str(file_path), "rb") as file:
    response = requests.post(
        url, headers=headers, params=params, data=file)

print("API request finished")
# error if we failed
response.raise_for_status()

result = response.json()

print("Parsing results")
# Get result into pandas
df = pd.DataFrame.from_dict(result['results']["data"])

Use scripts/use_api.py to call the api from the command line

python3 scripts/use_api.py <path to your file>  \
<your point elevation> --api_key <your api key>

This will output a csv file of the results. Run python3 scripts/use_api.py --help for a full list of options.

These variables are directly used within snobal

• air_temp - modeled air temp at 2m above ground
• percent_snow - % mix of snow vs rain (1 == all snow) [decimal percent]
• precip - precipitation mass [mm]
• precip_temp - wet bulb temperature
• snow_density - density of the NEW snow that hour [kg/m^3]
• vapor_pressure - modeled vapor pressure
• wind_speed - Wind speed at 5m above ground [m/s]
• soil_temp - Average temperature of the soil column down to 30cm
• net_solar - net solar into the snowpack [w/m^2]
• thermal - net longwave radiation into the snowpack [w/m^2]

See ./tests/data/inputs_csl_2023.csv for an example of data format

• wind height: 5m
• air temp height: 2m
• soil temp depth: 0.3m

• Snobal expects temperatures to be in Kelvin. This code expects Celcius. We do the conversion in get_timestep_force
• Precip mass (precip) is a big driver here. Without accurate conditions, model results will be poor