Data Acquisition, Preprocessing and Modelling using the PCRaster Python Framework

The course starts with acquisition and preprocessing of data for modelling using open source GIS and spatial analysis tools. Next, scripting in Python and environmental modelling using the PCRaster Python framework will be introduced. 

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2018

For whom?

The course is designed for professionals (engineers and scientists) active in the water sector, especially those involved in using simulation models for water management.

Dates, Fee, ECTS

Start: 17 September 2018
End: 28 September 2018
Deadline IHE application: 16 August 2018 - 23.59 (CET)
Course fee: € 1900

For many studies models are used or developed. During modelling courses not much attention is paid to the preprocessing of input data and parameters needed for the models. A lot of open source software is available for this purpose. Besides desktop tools with graphical user interfaces, scripting is very useful for processing large datasets and timeseries. With the skills learned in the first week of this course you will be able to find open access GIS data and more efficiently process your data for your models using tools like QGIS, GDAL and Python.

Numerical modelling of spatial and temporal processes is of major importance in many studies of the natural and human environment. Computer simulation models help us to understand better the processes involved and can predict future changes. The second week of this course introduces model construction using the PCRaster Python modelling language. The modelling language has been developed to free the modeller from problems with data input and output and provides a large range of basic primitive operators at the level of understanding of the researcher.

Prerequisites

It is expected that participants have good computer skills and basic knowledge of GIS.

Course content

  1. Introduction to Open Source software for GIS and hydrological modelling;
  2. Spatial Data Infrastructures for Open Access Water Data;
  3. Using QGIS to digitize vector layers from a scanned map;
  4. Using QGIS for importing tabular data into GIS, data correction and interpolation;
  5. Using QGIS for catchment and stream delineation;
  6. File conversions using GDAL and Python;
  7. Introduction to Python;
  8. Map algebra using PCRaster Python
  9. Dynamic modelling using PCRaster Python

Methods

  • Interactive lectures;
  • Computer exercises with open source -freely available- software only.

Lecturers