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Technical University of Munich

Data Preparation, Pre- and post-processing in Hydrology ()

Vortragende/r (Mitwirkende/r)
Faizan Anwar [L]
Faizan Anwar [L]
Nummer 0000001357
Art Vorlesung mit integrierten Übungen
Umfang 2 SWS
Semester Sommersemester 2025
Unterrichtssprache Englisch
Stellung in Studienplänen Siehe TUMonline
Termine Siehe TUMonline

Teilnahmekriterien

Siehe TUMonline

Lernziele

1. understand the importance of well-prepared model input data
2. understand the problem of incompatibility of observed data and model input data forms
3. convert observed data into a form that is suitable for a given model
4. interpolate data in space-time
5. identify bad quality data by comparing them among themselves
6. transform data in any form to a required one
7. learn how to convert model output data in to a more human readable form
8. compile model inputs and outputs together
9. understand model input and output relationship(s)
10. for any case of data pre- and post-processing, are able to recommend the best transformation method that yields a minimal loss of information
11. present and defend their calculations and findings against the questions of an expert audience

Beschreibung

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

Inhaltliche Voraussetzungen

• ED130088 Computer Programming in Hydrology

Lehr- und Lernmethoden

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.

Studien-, Prüfungsleistung

The examination is a project done in groups of up to three students. Through a project report (80% of the grade, ca. 25 pages), the students prove their ability to properly identify the form of given observed data, and the form that a given model needs its input to be in. They are able to able to recommend a proper transformation method that yields the best results using what that they learned in this course. In the project report, it should be possible to clearly identify the specific contribution of each group member. Through a final presentation (20% of the grade, individual group member evaluation, up to 60 minutes) they demonstrate their ability to present and defend their findings based on critical questions by an expert audience. The final grade depends on how detailed their analysis and solution is and the quality of its presentation.

Empfohlene Literatur

A practical primer on geostatistics, Olea 2018
Geostatistics for engineers and earth scientists, Olea 1999
Multivariate Geostatistics - An Introduction with Applications, Wackernagel 1998

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