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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. 

Apply now for

20162017

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: 12 September 2016
End: 23 September 2016
Deadline IHE application: 12 August 2016 - 23.59 (CET)
Course fee: € 1900

Start: 18 September 2017
End: 29 September 2017
Deadline IHE application: 18 August 2017 - 23.59 (CET)
Course fee: € 1900

Learning objectives

Upon completion, the participant should be able to:

  1. Find and analyse freely available GIS and Remote Sensing datasets
  2. Preprocess data (GIS, remote sensing, tabular data) for use in models (e.g. conversion, projection, catchment delineation, etc.) using open source software (QGIS, GDAL)
  3. Use scripts (Python) to batch process datasets
  4. Develop environmental models using the PCRaster Python framework

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. In the second week you will learn how to create your own environmental models using PCRaster Python (e.g. rainfall-runoff models, vegetation competition models, slope stability models).

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. Environmental modelling using PCRaster Python.

Methods

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

Lecturer