ALPGM (ALpine Parameterized Glacier Model) v1.1

• Main Author: Jordi Bolibar
• Format: Software
• Language: unknown
• Published: 2020
• Subjects: glacier evolution model; glaciers; glacier mass balance; deep learning; The Cryosphere
• Online Access: https://zenodo.org/record/3609136; https://doi.org/10.5281/zenodo.3609136

New release with the latest version of ALPGM for the published version of the Bolibar et al. (2020) The Cryosphere paper. Model overview ALPGM is a fully parameterized glacier evolution model based on data science. Glacier-wide surface mass balance (SMB) are simulated using a deep artificial neural network (i.e. deep learning) or Lasso (i.e. regularized multilinear regression). Glacier dynamics are parameterized using glacier-specific delta-h functions (Huss et al. 2008). The model has so far been implemented with a dataset of French alpine glaciers, using climate forcings for past (SAFRAN, Durand et al. 1993) and future (ADAMONT, Verfaillie et al. 2018) periods. The machine learning SMB modelling approach is built upon widely used Python libraries (Keras, Scikit-learn and Statsmodels). Workflow ALPGM's workflow can be controlled via the alpgm_interface.py file. In this file, different settings can be configured, and each step can be run or skipped with a boolean flag. The default workflow runs as it follows: (1) First of all, the meteorological forcings are pre-processed (safran_forcings.py / adamont_forcings.py) in order to extract the necessary data closest to each glacier’s centroid. The meteorological features are stored in intermediate files in order to reduce computation times for future runs, automatically skipping this preprocessing step when the files are already generated. (2) The SMB machine learning module retrieves the pre-processed meteorological features and assembles the spatio-temporal training dataset, comprised by both climatic and topographical data. An algorithm is chosen for the SMB model, which can be loaded from a previous training or it can be trained again with the training dataset (smb_model_training.py). These model(s) are stored in intermediate files, allowing to skip this step for future runs. (3) The performances of these SMB models can be evaluated performing a leave-one-glacier-out (LOGO) cross-validation (smb_validation.py). This step can be skipped when using already ...