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HLAVO

System for numerical prediction of water table using continuously collected meteorological and soil mosture profile data.

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Developers corner

Kalman

Local run

Use docker image: martinspetlik/kalman_parflow:v1.0.0

docker run --rm -it -v HLAVO_repository:/HLAVO martinspetlik/kalman_parflow:v1.0.0 python3.10 /HLAVO/soil_model/kalman.py work_dir /HLAVO/soil_model/configs/case_parameter_inv_synth.yaml

Charon cluster run

First singularity image has to be created:

export SINGULARITY_CACHEDIR="user home dir"
export SINGULARITY_LOCALCACHEDIR="user scratch dir"
export SINGULARITY_TMPDIR=$SCRATCHDIR

singularity build kalman_parflow.sif docker://martinspetlik/kalman_parflow:v1.0.0

Run Kalman on a directory that contains a configuration file

./charon_run_kalman.sh directory

DVC management of large files

Do not upload large binary files (over few MB) to the git repository, there are tight limits of the git technology and GitHub hosting to 500MB for the whole repository including all history. Binary files are not stored as diffs so they bite large chunks of the available space. Use DVC instaed to automate upload of large files to Google Drive. See detailed instructions.

Surface model (surface_model dir)

Model of surface infiltration layer. Composed of Richards model and Kalman filter for assimilation of the soil moisture profile measurement.

#- code #- run configurations

Deep Vadose Zone model (deep_model)

subfolder GIS is for various GIS resources.