PCR-GLOBWB (PCRaster Global Water Balance) is a large-scale hydrological model intended for global to regional studies and developed at the Department of Physical Geography, Utrecht University (Netherlands). This repository holds the model scripts of PCR-GLOBWB.

contact: Edwin H. Sutanudjaja ([email protected]).

Please also see the file README.txt.

Main reference/paper: Sutanudjaja, E. H., van Beek, R., Wanders, N., Wada, Y., Bosmans, J. H. C., Drost, N., van der Ent, R. J., de Graaf, I. E. M., Hoch, J. M., de Jong, K., Karssenberg, D., López López, P., Peßenteiner, S., Schmitz, O., Straatsma, M. W., Vannametee, E., Wisser, D., and Bierkens, M. F. P.: PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model, Geosci. Model Dev., 11, 2429-2453, https://doi.org/10.5194/gmd-11-2429-2018, 2018.

The input files for the runs made in the aformentioned paper (Sutanudjaja et al., 2018) are available on the OPeNDAP server: https://opendap.4tu.nl/thredds/catalog/data2/pcrglobwb/version_2019_11_beta/pcrglobwb2_input/catalog.html. The OPeNDAP protocol (https://www.opendap.org) allow users to access PCR-GLOBWB input files from the remote server and perform PCR-GLOBWB runs without the need to download the input files (with total size ~250 GB for the global extent).

Some output files are also provided: https://opendap.4tu.nl/thredds/catalog/data2/pcrglobwb/version_2019_11_beta/example_output/global_05min_gmd_paper_output/catalog.html. More output files are available on https://geo.data.uu.nl/research-pcrglobwb/pcr-globwb_gmd_paper_sutanudjaja_et_al_2018/ (for requesting access, please send an e-mail to [email protected]).

Please follow the following steps required to install PCR-GLOBWB:

1. You will need a working Python environment, we recommend to install Miniconda, particularly for Python 3. Follow their instructions given at https://docs.conda.io/en/latest/miniconda.html. The user guide and short reference on conda can be found here.

2. Get the requirement or environment file from this repository conda_env/pcrglobwb_py3.yml and use it to install all modules required (e.g. PCRaster, netCDF4) to run PCR-GLOBWB:

   conda env create --name pcrglobwb_python3 -f pcrglobwb_py3.yml

   This will create a environment named pcrglobwb_python3.

3. Activate the environment in a command prompt:

   conda activate pcrglobwb_python3

4. Clone or download this repository. We suggest to use the latest version of the model, which should also be in the default branch.

   git clone https://github.com/UU-Hydro/PCR-GLOBWB_model.git

   This will clone PCR-GLOBWB into the current working directory.

For running PCR-GLOBWB, a configuration .ini file is required. Some configuration .ini file examples are given in the config directory. To be able to run PCR-GLOBWB using these .ini file examples, there are at least two things that must be adjusted.

First, please make sure that you edit or set the outputDir (output directory) to the directory that you have access. You do not need to create this directory manually.

Moreover, please also make sure that the cloneMap file is stored locally in your computing machine. The cloneMap file defines the spatial resolution and extent of your study area and must be in the pcraster format. Some examples are given in this repository clone_landmask_maps/clone_landmask_examples.zip.

By default, the configuration .ini file examples given in the config directory will use PCR-GLOBWB input files from the 4TU.ResearchData server, as set in their inputDir (input directory).

inputDir = https://opendap.4tu.nl/thredds/dodsC/data2/pcrglobwb/version_2019_11_beta/pcrglobwb2_input/

This can be adjusted to any (local) locations, e.g. if you have the input files stored locally in your computing machine.

Please make sure that the correct conda environment in a command prompt:

conda activate pcrglobwb_python3

We included some exercise/cooking recipes for running PCR-GLOBWB. You can find these documents in the folder exercise within this repository. While these exercises were generally designed for our own computing facilities (e.g. velocity and eejit servers), they should be adaptable for use on other computing machines.