pointsnobal 0.1.0

Python wrapper of the Snobal mass and energy balance snow model

PointSnobal

Python wrapped implementation of the Snobal model applied at a point.

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.

Research API

🚀 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! ❄️📡

Disclaimer

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.

API usage

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"])

Running locally

Script usage

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.

Input files

Variables that inform snobal

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

Height settings for snobal

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

Watch out for

• 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

PointSnobal script

The entrypoint is make_snow once installed. Example:

make_snow <path to input file> <elevation in meters>

Install

Requirements

Requirements can be found in requirements.txt NOTE - GCC is required

For local build: Create a virtual environment to isolate the code

pip install -r requirements.txt
python3 setup.py build_ext --inplace

Install issues on mac

If you're getting 'omp.h' file not found on setup.py install

This can fix that issue

export CC=/usr/local/bin/gcc-14

for M1 or M2 mac this would be

export CC=/opt/homebrew/bin/gcc-14

This was after brew install gcc to get a version with openmp On M2 mac I also had to run brew install libomp

Then I had the error ld: library not found for -lomp which I fixed with

export LDFLAGS="-L/usr/local/opt/libomp/lib"

for M1 and M2 this would be

export LDFLAGS="-L/opt/hombrew/Cellar/libomp/lib"

or

export LDFLAGS="-L/opt/homebrew/opt/libomp/lib"

depending on the path. You can find that path with brew info libomp

Validation data

Using metloom for station data that can be used for validation. get_daily_data returns a GeoPandas DataFrame of the variables and units on a daily timestep. Validation is crucial in snowpack modeling!

# Imports
import pandas as pd
from metloom.pointdata import CDECPointData
from metloom.variables import CdecStationVariables

# Specify start and end date
start_date = pd.to_datetime("2019-10-01")
end_date = pd.to_datetime("2020-06-01")

# List of variables to request
desired_variables = [
    CdecStationVariables.SWE, CdecStationVariables.SNOWDEPTH
]

# Define the point
point = CDECPointData("GRV", "Graveyard Meadow")

# Request the data
df = point.get_daily_data(
    start_date, end_date, desired_variables
)
# Data comes back indexed on `datetime` and `site`, reset to just datetime
df = df.reset_index().set_index("datetime")
# Show the results
print(df)
# store in csv if you want
df.to_csv(f"{point.id}_station_data.csv", index_label="datetime")