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https://github.com/juseg/pypdd

A positive degree day model for glacier surface mass balance
https://github.com/juseg/pypdd

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A positive degree day model for glacier surface mass balance

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README 

        
.. Copyright (c) 2013-2023, Julien Seguinot (juseg.dev)

.. GNU General Public License v3.0+ (https://www.gnu.org/licenses/gpl-3.0.txt)



PyPDD

=====



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A Python positive degree day model for glacier surface mass balance.



This module provides a simple model to compute accumulation and melt on a

glacier using near-surface air temperature and precipitation time series. The

model assumes that melt is proportional to the number of positive degree-days,

which corresponds to the integral of temperature above 0°C. Temperature

variability is included by assuming a normal temperature distribution around the

mean. The model optionally includes refreezing of melted snow and ice at the

glacier surface.



PyPDD_ can be used as a module within Python to operate on Numpy_ arrays. In

addition, it reads and writes netCDF_ files directly from the command line, and

provides a raster module for `GRASS GIS`_. The PDD model is based on an

algorithm that was initially developed for the `Parallel Ice Sheet Model`_ and

adopted here with very few changes.



Installation::



   pip install pypdd



The PDDModel class

------------------



**Requires:** NumPy_, SciPy_, Xarray_.



A PDD model instance can be created by::



   from pypdd import PDDModel

   pdd = PDDModel()



Several model parameters can be set at initialization. See ``help(PDDModel)``

for a list. Provided two arrays ``temp`` and ``prec`` of shape ``(t, x, y)``

containing temperature and precipitation data, the PDD model can be called

with::



   pdd(temp, prec)



This will return a dictionary containing a number of two- and three-dimensional

arrays, including the number of positive degree day ``'pdd'`` and total surface

mass balance ``'smb'``. Temperature variability can be included in a third array

``stdv`` containing temperature standard deviation values::



	pdd(temp, prec, stdv)



If any of ``temp``, ``prec``, or ``stdv`` has shape ``(x, y)``, it will be

interpreted as constant in time and expanded along the time dimension. Floats

with be interpreted as constant in time and space and expanded along all

dimensions.



NetCDF interface

----------------



**Requires:** netCDF4-Python_.



The PDDModel class holds a netCDF operator, which can be called by::



   pdd.nco('input.nc', 'output.nc')



The file ``'input.nc'`` should contain temperatures and precipitation in

variables ``'temp'`` and ``'prec'``. The calculated number of positive degree

days and total surface mass balance are stored in variables ``'pdd'`` and

``'smb'`` of ``'output.nc'``. Keyword argument ``output_size`` or

``output_variables`` can be used to produce more output.



The netCDF interface can be used directly from the command line by executing the

module as a script::



   python pypdd.py -i 'input.nc' -o 'output.nc'



If no input file is provided, an artificial climate will be generated under

``atm.nc`` and used by the model. By default, output is saved as ``smb.nc``.

Many more command-line options are available. For an overview type::



   python pypdd.py --help



GRASS GIS interface

-------------------



**Requires:** `GRASS GIS`_.



PyPDD can also operate on GRASS raster maps using the attached module ``r.pdd``.

Temperature, precipitation and standard deviation maps should be provided as

comma-separated lists::



   r.pdd.py temp=list,of,temp,maps prec=list,of,prec,maps pdd=pdd_map smb=smb_map



All time-independent PyPDD output variables can currently be exported as raster

maps. Alike any other GRASS module, a graphical prompt can be invoked by calling

``r.pdd`` without arguments, and a list of options can be obtained with::



   r.pdd.py --help



References

----------



Applications of PyPDD:



* N. Gandy, L. J. Gregoire, J. C. Ely, C. D. Clark, D. M. Hodgson, V. Lee,

  T. Bradwell, and R. F. Ivanovic.

  Marine ice sheet instability and ice shelf buttressing of the Minch Ice

  Stream, northwest Scotland.

  *The Cryosphere*, 12(11):3635--3651,

  https://doi.org/10.5194/tc-12-3635-2018, 2018.



* A. Plach, K. H. Nisancioglu, S. Le clec'h, A. Born, P. M. Langebroek, C. Guo,

  M. Imhof, and T. F. Stocker.

  Eemian Greenland SMB strongly sensitive to model choice.

  *Clim. Past*, 14:1463--1485,

  https://doi.org/10.5194/cp-14-1463-2018, 2018.



* J. Seguinot.

  Spatial and seasonal effects of temperature variability in a positive

  degree-day glacier surface mass-balance model.

  *J. Glaciol.*, 59(218):1202--1204,

  http://doi.org/10.3189/2013JoG13J081, 2013.



.. links



.. _GRASS GIS: http://grass.osgeo.org

.. _netCDF: http://www.unidata.ucar.edu/software/netcdf

.. _netCDF4-Python: https://github.com/Unidata/netcdf4-python

.. _NumPy: http://numpy.scipy.org

.. _Parallel Ice Sheet Model: http://www.pism-docs.org

.. _PyPDD: https://github.com/jsegu/pypdd

.. _SciPy: http://www.scipy.org

.. _Xarray: https://xarray.dev