Scripts to analyze data generated using the "MountAiN glacier Transient snowline Retrieval Algorithm". The data must have been postprocessed using the MANTRA Postprocessing scripts prior to analysis.
In case you have a Slurm-based HPC cluster at hand, you may use the Bash shell files to run scripts in parallel.
First, you will need to process some MANTRA TSLA data. An small example file is ./data/MANTRA/.
Many of the scripts require a CSV file of Randolph Glacier Inventory (RGI) data to work. An example for High Mountain Asia is provided in ./data/RGI. For other regions, you may download the according shapefile, open it in your preferred GIS environment, and export the attribute table to CSV.
- Python 3 (a current version of Anaconda is recommended).
- Pandas, Numpy, MatPlotLib and quite a lot of other Python libs (that will come with Anaconda).
- Some scripts use Scientific Color Maps v6.
Create a stacked bar plot displaying the number of glaciers that have their maxima in each year.
Empirical Orthognal Function analysis of spatio-temporal patterns.
Plot full TSLA dataset into one time series figure.
Plot TSL timeseries for an individual glacier.
python plot-glacier-timeseries.py <tsl_file> <glacier_list_file> <lower_limit> <upper_limit> <output_dir>\n\n") With tsl_file being a MANTRA TSL result file in HDF format, glacier_list_file an ASCII text file containing the RGI_IDs, one ID per row, lower_limit and upper_limit the first and last dataset to process, referring to number of IDs in glacier_list_file, and output_dir a valid directory to which the output will be written.
If you publish work based on MANTRA, please cite as:
David Loibl (2022): MountAiN glacier Transient snowline Retrieval Algorithm (MANTRA) v0.8.2, doi: 10.5281/zenodo.7133644
MANTRA was developed within the research project "TopoClimatic Forcing and non-linear dynamics in the climate change adaption of glaciers in High Asia" (TopoClif). TopoCliF and David Loibl's work within the project were funded by DFG under the ID LO 2285/1-1.