Computational Geography

PCRaster 4.4.3 released

Computational Geography group — Wed, 08 Jul 2026 08:24:58 GMT

We are glad to announce the release of PCRaster 4.4.3. This is a maintenance release to allow for compilation with the latest compiler, GDAL and Boost versions. Thanks to Jürgen Fischer for his contribution!

The new PCRaster package is available on conda-forge. Supported platforms are Linux, macOS and Windows, available for Python versions 3.10 up to 3.14.

Information on the installation procedure: http://pcraster.geo.uu.nl/pcraster/4.4.3/documentation/pcraster_project/install.html

Happy installing!

EGU 2026 session about high-performance computation in the geosciences

Computational Geography group — Tue, 11 Nov 2025 06:46:20 GMT

Together with colleagues from five European research institutes we have submitted a session proposal for the EGU 2026 General Assembly in Vienna, Austria. The session is about high-performance computation in the geosciences. We are happy to report that the session has been accepted into the programme as part of the programme group Earth & Space Science Informatics.

You can find more information about this session on the EGU26 home page.

If your work fits within its scope, then we invite you to submit an abstract to this session. Abstract submission will be open until 15 Jan 2026, 13:00 CET. We look forward to meeting you in Vienna in May 2026!

LUE and PCRaster available for Python 3.14

Computational Geography group — Wed, 03 Sep 2025 15:41:24 GMT

LUE and PCRaster are now available for Python 3.14! Packages for Python 3.9 are no longer provided, users should migrate to a more recent Python version. All our packages are available on conda-forge!

Happy modelling!

PCRaster 4.4.2 released

Computational Geography group — Tue, 27 May 2025 07:27:21 GMT

We are glad to announce the release of PCRaster 4.4.2. This is a minor bugfix release that fixes converting to and from NumPy when using NumPy 2 (#413). The new PCRaster package is available on conda-forge. Supported platforms are Linux, macOS and Windows, available for Python versions 3.9 up to 3.13. Information on the installation procedure: http://pcraster.geo.uu.nl/pcraster/4.4.2/documentation/pcraster_project/install.html

Happy installing!

PCRaster available for Python 3.13

Computational Geography group — Wed, 23 Oct 2024 15:21:09 GMT

PCRaster 4.4.1 is now available for Python 3.13! Packages for Python 3.8 are no longer updated, users should migrate to a more recent Python version. All packages are available on conda-forge! Happy modelling!

LUE application: “Making the IMAGE model FAIR is a major step for science and policy”

Computational Geography group — Mon, 26 Aug 2024 23:00:00 GMT

We have participated in a project making the IMAGE integrated assessment model more FAIR by rewriting its code and using LUE to enable parallel processing. Please find the interview with our colleagues from PBL and the Department of Human Geography and Spatial Planning here.

Error correction of predictions from a simulation model using Random Forests

Computational Geography group — Mon, 03 Jan 2022 23:00:00 GMT

Reducing errors in predictions from forward simulation models is often achieved by model calibration or data assimilation. In our recently published manuscript, Youchen Shen and our team explore an alternative that involves correction of predictions using a machine learning algorithm (Random Forests). We use streamflow predictions from the global water balance model PCRGLOB-WB as case study. It is hypothesized that the forcings (e.g. precipitation) as well as the simulated state variables (e.g. streamflow) of the simulation are informative for the magnitude of the error in streamflow predicted by the model. In particular the use of the simulated state variables is an innovative aspect of our study.

The figure below compares the different scenarios (Basel (Rhine); NSE and KGE, larger values indicate smaller errors). Black-outlined boxes give the performance of the simulation model without error correction, for the calibrated and the uncalibrated simulation model. The coloured bars give the performance after error correction. Using only meteorogical driving variables in error correction (red bars) considerably reduces error. The use of simulated state variables (green, blue) further reduces errors.

The figure below shows the effect on the predicted hydrographs (black, observed streamflow; blue calibrated simulation model; red after error correction).

Our approach is promising as it shows that error correction using a random forest provides errors in streamflow predictions that are considerably smaller than those from a calibrated model. We also show that simulated model state is informative of the magnitude of the error. Read the full paper at https://doi.org/10.1016/j.cageo.2021.105019

Global Air Pollution Mapping

Computational Geography group — Thu, 29 Apr 2021 23:00:00 GMT

Meng Lu presented our work on global ambient NO2 air pollution mapping at the EGU General Assembly 2021

EGU21-6355 | vPICO presentations | AS3.19
Global, high-resolution statistical modelling of NO2
Meng Lu, Oliver Schmitz, Kees de Hoogh, Perry Hystad, Luke Knibbs, Qin Kai, and Derek Karssenberg
https://doi.org/10.5194/egusphere-egu21-6355

EGU General Assembly 2021

Computational Geography group — Tue, 13 Apr 2021 23:00:00 GMT

Like almost every year, we present our work at the EGU General Assembly, 19-30 April 2021. It is an online event. Our presentations are:

EGU21-7154 | vPICO presentations | HS2.5.1
Global scale hydrological modelling at 100 m, 1 h resolution, in Python
Kor de Jong, Marc van Kreveld, Debabrata Panja, Oliver Schmitz, and Derek Karssenberg
Thu, 29 Apr, 09:19–09:21
https://doi.org/10.5194/egusphere-egu21-7154

EGU21-7081 | vPICO presentations | AS3.19
Nationwide estimation of personal exposure to air pollution using activity-based field-agent modelling
Oliver Schmitz, Meng Lu, Kees de Hoogh, Nicole Probst-Hensch, Ayoung Jeong, Benjamin Flückiger, Danielle Vienneau, Gerard Hoek, Kalliopi Kyriakou, Roel C. H. Vermeulen, and Derek Karssenberg
Wed, 28 Apr, 11:34–11:36
https://doi.org/10.5194/egusphere-egu21-7081

EGU21-6355 | vPICO presentations | AS3.19
Global, high-resolution statistical modelling of NO2
Meng Lu, Oliver Schmitz, Kees de Hoogh, Perry Hystad, Luke Knibbs, Qin Kai, and Derek Karssenberg
Wed, 28 Apr, 11:06–11:08
https://doi.org/10.5194/egusphere-egu21-6355

EGU21-12504 | vPICO presentations | HS2.2.1
The nature and extent of bomb tritium remaining in deep soils
Jaivime Evaristo, Yanan Huang, Zhi Li, Kwok P. Chun, Edwin H. Sutanudjaja, and Marc F.P. Bierkens
Wed, 28 Apr, 13:52–13:57
https://doi.org/10.5194/egusphere-egu21-12504

EGU21-2340 | vPICO presentations | HS5.2.1
ULYSSES: a system for global multi-model hydrological seasonal predictions
Luis Samaniego, Stephan Thober, Matthias Kelbling, Robert Schweppe, Oldrich Rakovec, Pallav Shrestha, Alberto Martinez-de la Torre, Eleanor M. Blyth, Katie A. Smith, Gwyn Rees, Matthew Fry, Edwin Sutanudjaja, Niko Wanders, Marc FP Bierkens, and Rens van Beek
Thu, 29 Apr, 13:48–13:50
https://doi.org/10.5194/egusphere-egu21-2340

EGU21-2069 | vPICO presentations | GM6.9/HS13.30/NH1.24/NP8.2 | Highlight
The past and future dynamics of salt intrusion in the Mekong Delta
Sepehr Eslami, Maarten van der Vegt, Philip Minderhoud, Nam Nguyen Trung, Jannis Hoch, Edwin Sutanudjaja, Dung Do Doc, Tho Tran Quang, Hal Voepel, and Marie-Noëlle Woillez
Wed, 28 Apr, 15:55–15:57
https://doi.org/10.5194/egusphere-egu21-2069

EGU21-684 | vPICO presentations | HS2.5.1
A conceptual analytical framework to assess the large-scale effects of groundwater withdrawal on groundwater storage and surface water flow
Marc F.P. Bierkens, Edwin H. Sutanudjaja, and Niko Wanders
Thu, 29 Apr, 09:07–09:09
https://doi.org/10.5194/egusphere-egu21-684

EGU21-125 | vPICO presentations | HS2.5.1
On the influence and limitations of hyper-resolution hydrological modelling – application of the 1 km PCR-GLOBWB model over Europe
Jannis Hoch, Edwin Sutanudjaja, Rens van Beek, and Marc Bierkens
Thu, 29 Apr, 09:05–09:07
https://doi.org/10.5194/egusphere-egu21-125

A modelling paradigm for field-agent based modelling, hands-on workshop with open source software

Computational Geography group — Mon, 24 Aug 2020 23:00:00 GMT

Karssenberg¹, D., Schmitz¹, O., Verstegen², J.A., de Jong¹, K.

¹Utrecht University, the Netherlands, ²University of Münster, Germany

Correspondence: d.karssenberg@uu.nl

We are organising this online workshop at the iEMSs Conference 2020, https://iemss2020.com

Platform: ZOOM

The heterogeneous nature of environmental systems poses a challenge on researchers constructing environmental models. Many simulation models need to incorporate phenomena that are represented as spatially and temporally continuous fields as well as phenomena that are modelled as spatially and temporally bounded agents. Examples include mobile animals (agents) interacting with vegetation (fields) or water reservoirs (agents) as components of hydrological catchments (fields). We share ideas on the design and implementation of a new data model^1,2 and modelling system for development of such field-agent based models. In addition, we present a short hands-on workshop with the software.

References

de Bakker, M. P., de Jong, K., Schmitz, O. & Karssenberg, D. Design and demonstration of a data model to integrate agent-based and field-based modelling. Environ. Model. Softw. 89, 172–189 (2017).
de Jong, K. & Karssenberg, D. A physical data model for spatio-temporal objects. Environ. Model. Softw. 122, 104553 (2019).

Tentative schedule

Important note: during the workshop we will demo the prototype software. If you wish, you can also run the examples that we will show yourself, during or after the workshop. For installation instructions and an explanation how to run the scripts, refer to https://campo.computationalgeography.org/workshops/iemss2020/

Workshop duration: 2 hours

Introduction to workshop (5 minutes)
Presentation and discussion on concepts and software for field-agent based modelling (workshop organizers) (30 minutes)
Demo / Hands-on workshop (workshop participants)
Discussion (all)
Wrap-up and next steps