Data and Models in Earth Surface and Water Systems

Course goals

By the end of the course, the students will have acquired:

• Competence in Python programming and data wrangling for geoscientific applications, including proficiency in working with diverse (geospatial) data formats and implementing reproducible workflows;
• Knowledge and understanding of modelling approaches used across the different tracks of Earth Surface and Water, and the ability to select appropriate approaches for specific research questions;
• Competence in applying statistical modelling, machine learning, and numerical simulation to analyze environmental data and processes;
• Critical perspective on models in scientific and societal contexts, including the ability to assess model credibility, validity, and limitations, and to understand any ethical implications.

The course focuses on development of the following transferable skills:

• Ability to work in a team: Collaboratively solving research problems
• Analytical skills: Applying statistical and numerical methods
• Critical thinking: Evaluating model validity, uncertainty, and societal implications
• Problem-solving: Identifying suitable solutions
• Technical proficiency: Python programming and data wrangling
• Verbal communication: Engaging in scientific discussions

Content

Models are essential tools for research across the Earth Surface and Water (ESW) domain, from predicting floods and tracking glacier loss to understanding coastal erosion and managing water resources. This course gives you the skills to use and critically evaluate the relevant analytical methods that underpin scientific research as well as practical decision-making.

You will explore the full spectrum of approaches used in ESW, learning when to apply statistical methods, machine learning, or process-based simulations. The course covers modelling terminology and helps you compare different techniques, so you can confidently choose appropriate tools for your research questions.

Python programming forms the practical core of the course. Through computer labs, you will develop data wrangling skills with common data formats used in geoscience, apply statistical inference and regression, implement machine learning algorithms like Random Forest, and build and apply process-based numerical simulations. For an optimal workflow and dissemination of results, the course also teaches the basics of professional research practice including FAIR data principles and version control.

Beyond technical skills, the course addresses important questions: How reliable are our models? How do we communicate uncertainty? What ethical responsibilities come with using models in society? Through structured discussion, you will develop the critical thinking needed for responsible model use.

You will leave with practical programming capabilities, theoretical understanding, and the analytical skills needed to tackle quantitative problems throughout your master's programme and future career.