Water Quality Assessment and Monitoring
For whom?
Young and mid-career professionals (scientists, consultants, decision makers) with a background in Water management or Environmental science.
Prerequisites
Required: Basic knowledge in chemistry and statistics || Basic knowledge in computer operations (MS-Windows, Office) || Basic knowledge in QGIS (ES programme module 2 or IHE Open CourseWare on Open Source Software for Preprocessing GIS Data for Hydrological Models, exercises 1, 2 and 7).
Recommended: basic knowledge of R statistical software (ES programme modules 2-3).
Dates, Fee, ECTS
Start: 30 March 2020
End: 17 April 2020
ECTS credit points: 5
Deadline IHE application: 28 February 2020 - 23.59 (CET)
Course fee: € 2910
Start: 29 March 2021
End: 16 April 2021
Deadline IHE application: 27 February 2021 - 23.59 (CET)
Course fee: € 2910
VAT is not included in the course fee
Learning objectives
- Select and apply appropriate methods to assess water quality in natural waters in relation to their anticipated use.
- Design and evaluate water quality monitoring networks for different types of surface water in relation to set objectives.
- Report the results of water quality assessment and monitoring programmes using appropriate statistical tools for interpretation and presentation of large data sets.
Course content
The fundamental question of why we monitor will be discussed, along with what water quality variables to monitor in relation to different objectives. Participants will design a monitoring network, and open source tools to gather information for monitoring will be explored. Finally, participants will work on ensuring data quality, and on statistical analyses of water quality data.
Prerequisites
Required: Basic knowledge in chemistry and statistics || Good command of English || Basic knowledge in computer operations (MS-Windows, Office) || Basic knowledge in QGIS (ES programme module 2 or IHE Open CourseWare on Open Source Software for Preprocessing GIS Data for Hydrological Models, exercises 1, 2, en 7).
Recommended: basic knowledge of R statistical software (ES programme modules 2-3).