Articles | Volume 15, issue 7
https://doi.org/10.5194/gmd-15-3161-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-3161-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
12 Apr 2022
Model description paper |
| 12 Apr 2022
| 12 Apr 2022
GSTools v1.3: a toolbox for geostatistical modelling in Python
Sebastian Müller
CORRESPONDING AUTHOR
Department of Computational Hydrosystems, UFZ – Helmholtz Centre for Environmental Research, Leipzig, Germany
Institute of Earth and Environmental Sciences, University of Potsdam, Potsdam, Germany
Lennart Schüler
Institute of Earth and Environmental Sciences, University of Potsdam, Potsdam, Germany
Department of Computational Hydrosystems, UFZ – Helmholtz Centre for Environmental Research, Leipzig, Germany
Center for Advanced Systems Understanding (CASUS), Görlitz, Germany
Alraune Zech
Department of Earth Sciences, Utrecht University, Utrecht, the Netherlands
Department of Computational Hydrosystems, UFZ – Helmholtz Centre for Environmental Research, Leipzig, Germany
Falk Heße
Institute of Earth and Environmental Sciences, University of Potsdam, Potsdam, Germany
Department of Computational Hydrosystems, UFZ – Helmholtz Centre for Environmental Research, Leipzig, Germany
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Latest update: 24 Apr 2024
Short summary
The GSTools package provides a Python-based platform for geoostatistical applications. Salient features of GSTools are its random field generation, its kriging capabilities and its versatile covariance model. It is furthermore integrated with other Python packages, like PyKrige, ogs5py or scikit-gstat, and provides interfaces to meshio and PyVista. Four presented workflows showcase the abilities of GSTools.
The GSTools package provides a Python-based platform for geoostatistical applications. Salient...
