Data Preparation, Pre- and post-processing in Hydrology
| Lecturer (assistant) |
Faizan Anwar [L]
|
|---|---|
| Number | 0000001357 |
| Type | lecture with integrated exercises |
| Duration | 2 SWS |
| Term | Summer semester 2026 |
| Language of instruction | English |
| Position within curricula | See TUMonline |
| Dates | See TUMonline |
- 13.10.2026 09:45-11:30 0670ZG, Hör-/Lehrsaal eben o.Exp.Bühne
Admission information
Description
1. Introduction to data preparation
2. Various inputs and formats used in practice
3. Data collection and validation
4. Processing text and binary data
5. Coordinate Systems and Transformations
6. Plotting tools for data analysis
7. The Nearest Neighbors (Thiessen Polygons) method
8. The Kriging Method and its variants
9. From point to gridded data
10. From gridded to point data
11. Bringing data in to a suitable form for a model
12. Storing and analyzing model outputs
13. Presenting combined inputs and outputs for further analysis
2. Various inputs and formats used in practice
3. Data collection and validation
4. Processing text and binary data
5. Coordinate Systems and Transformations
6. Plotting tools for data analysis
7. The Nearest Neighbors (Thiessen Polygons) method
8. The Kriging Method and its variants
9. From point to gridded data
10. From gridded to point data
11. Bringing data in to a suitable form for a model
12. Storing and analyzing model outputs
13. Presenting combined inputs and outputs for further analysis
Prerequisites
• ED130088 Computer Programming in Hydrology
Teaching and learning methods
The module consists of lectures with integrated exercises. The students are required to bring their own laptops. Portable open-source software will be made available to all. The students should be able to code in the Python programming language. After the introduction and definition of essential terms, the problem of model input data preparation is broken in to small chunks that the students are supposed to complete in each exercise. The aim is that the students are able to transform observed data in any form that is suitable for a model as input. Finally, the students write a report (as a group) where they demonstrate their ability to understand a given situation and provide the solution in the form of a running computer code that takes raw observed data, validate it, pass it to a model and finally present the outputs of the model along with the inputs in an understandable manner using the methods learned during this course.