swe2hs 1.0.4

Conceptual snow density model for transferring snow water equivalent to snow depth.

swe2hs

SWE2HS is a conceptual snow density model for transferring daily snow water equivalent (SWE) of the snow cover to snow depth (HS). Some people informally call it JOPACK, which is an acronym for Just density Of the snowPACK.

The density model calculates snow depth at a daily resolution and is driven by the daily snow water equivalent of the snow cover only. The model creates a new layer with a fixed new snow density \(\rho_{new}\) for every increase in SWE such that, over time, a snowpack of individual layers builds up. The density of a layer increases with an exponential decay function towards a time-varying maximum density. The maximum density starts with an initial value at creation time of the layer and subsequently increases towards a higher value based on the overburden a layer has experienced and the occurrence of SWE losses in the snow pack. When SWE decreases, the model removes layers from the top of the snowpack. The layer number \(n\) can thus undergo changes over time based on the number of SWE increases and losses in the snowpack. The model neglects constructive metamorphism, refreezing, and is not able to capture rain-on-snow events which might lead to an increase in SWE but no increase in snow depth.

Citation

For more information on the model and how it was calibrated, please refer to the model description paper:

Aschauer, J.; Michel, A.; Jonas, T.; Marty, C., 2023: An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0. Geoscientific Model Development, 16, 14: 4063-4081. doi: 10.5194/gmd-16-4063-2023

The figure shows the schematic modeled snow pack evolution for the station Kühtai in the winter 2001/02. The red dotted line is the measured snow depth (HS), the black solid line bounding the colored area is the modeled snow depth, the thin black lines depict the layer borders within the modeled snowpack, and the coloring refers to the modeled layer densities. The bottom panel shows the daily snow water equivalent time series which was used to force the model. The data for station Kühtai is available from Krajci et al. (2017) [1].

Installation

Please create a dedicated environment before you install the package in order to avoid dependency issues with other installed Python packages. You can do this by using virtualenv:

virtualenv <PATH TO VENV>
source <PATH TO VENV>/bin/activate

or if you use Anaconda/Miniconda:

conda create -n swe2hs_env
conda activate swe2hs_env

Afterwards you can install the latest version of swe2hs to the newly created and activated environment by running:

pip install swe2hs

This will also install all dependencies which are necessary for the package to work correctly.

Verify the installation by running the following commands in a Python console:

>>> import swe2hs as jopack
>>> print(jopack.__version__)

Installing from source

If you want to work on the package and make changes, it is recommended to clone a copy of this repositoy and install the package from source in editable mode. Clone the repository:

$ git clone https://gitlabext.wsl.ch/aschauer/swe2hs.git

A new directory swe2hs will be created. After navigating to this directory with:

$ cd swe2hs

You can install the package in editable mode which allows you to import the package under development in the Python REPL:

$ pip install -e .

Tests

Testing is done with tox and pytest. In order to run the tests locally on your machine, navigate to the root directory of the project and run:

$ tox

You can also run only the tests from a single module with:

$ tox tests/test_module.py

Documentation and Examples

API documentation as well as examples on how to use the package are available at <https://aschauer.gitlab-pages.wsl.ch/swe2hs/>. There you can also find instructions on how to contribute and a changelog.

Help

If something is not working or you find an error, please get in touch via a new issue on the GitLab repository in case you do not find any relevant information in existing issues.

[1]

Krajci, P., Kirnbauer, R., Parajka, J., Schöber, J., & Blöschl, G. (2017). The Kühtai data set: 25 years of lysimetric, snow pillow, and meteorological measurements, Water Resources Research, 53, 5158-5165, https://doi.org/10.1002/2017WR020445.