Data Acquisition, Preprocessing and Modelling using the PCRaster Python Framework

Training on Data Acquisition, Preprocessing and Modelling using the PCRaster Python Framework
18-29 September 2017

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.

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.

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.
Contact Course Coordinator:  h.vanderkwast@unesco-ihe.org
Contact Fellowship and Admission Officer:   shortcourse@unesco-ihe.org

UNESCO-IHE conducts a wide range of short, intensive and highly specialized courses which are aimed at upgrading and refreshing the knowledge and skills of mid-career professional and senior experts.

Short courses are meant for professionals – or groups of professionals – with a specific area of interest and a limited amount of time. The focus and content of short courses vary from specialised and technical matters to challenges and approaches in management. Didactical methods used in these short courses include lectures, individual or group exercises in the classroom, behind the computer, or in the laboratory.

Fieldwork, excursions and field visits to relevant sites often are a part of a short course, allowing the participants to come into contact with practical examples of the theory offered. Through case studies, role-plays and workshops, content is made more interactive, and experience that the participants already have is shared.

Admission Criteria
You should at least have a Bachelor Degree in a relevant field of study, some years of working experience and a good command of the English language.

NFP, MSP and SIDS Fellowships
Most of our short courses are eligible for NFP, MSP and SIDS Fellowships. Candidates from eligible countries are always encouraged to apply for one. 


You will need to have academic admission at UNESCO-IHE before you apply for an fellowship. Please be aware that the UNESCO-IHE application deadline and the fellowship application deadlines are different.


You may register by filling out the online application form available at the webpage of the Short Course of your preference. Within 15 working days you will be notified about the outcome of your application. You will receive more details about how to apply for the fellowship when you are academically admitted. In case you would like to be nominated for a NFP/MSP fellowship


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Cambodia Jobs: Data Acquisition, Preprocessing and Modelling using the PCRaster Python Framework
Data Acquisition, Preprocessing and Modelling using the PCRaster Python Framework
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