Valentin Gebhart: Catalogue data in Spring Semester 2025 |
| Name | Dr. Valentin Gebhart |
| Address | Professur Wetter- und Klimarisiken ETH Zürich, CHN H 73.2 Universitätstrasse 16 8092 Zürich SWITZERLAND |
| valentin.gebhart@usys.ethz.ch | |
| Department | Environmental Systems Science |
| Relationship | Lecturer |
| Number | Title | ECTS | Hours | Lecturers | ||||||||||||||||||||||||||||||||||||||||||||
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| 701-0034-22L | Integrated Practical: Python in Geosciences   | 2 credits | 3P | M. Hauser, V. Gebhart, R. Noyelle | ||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Python is a widely used language for data analysis and visualisation, making it an indispensable tool for science and industry. Students will learn to use, adapt and write clean and reusable Python code from a variety of geoscience examples, working in small teams of two or three. | |||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | In this course students will learn to - handle and visualise geoscientific data (geoscientific data formats, maps in python) - work with python on their laptops (installing and managing python, using the web to help themselves) - write quality code (clean coding, documentation, reusable functions) - collaborate on code (joint projects, adapting existing code) - use scripting for reliable and efficient data processing (python) | |||||||||||||||||||||||||||||||||||||||||||||||
| Content | The first half of the course is dedicated to setting up python on your personal laptop and for an introduction to important data processing and visualization libraries. For the second half, students work on one specific project. For each project, skeleton python code is provided, which the students complete and augment. Ideally, students work in teams of two or three. All projects include some aspects of data handling and visualization, especially geographical maps. At the end of the practica, all projects are presented, along with the challenges that occurred and how they were tackled. As our goal is that students learn to work with python on their own, using their own laptop and the internet for tackling problems is highly encouraged but not mandatory. | |||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Instructions and literature will be provided during the course (in English). | |||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Basic knowledge of python (e.g. ‘Einsatz von Informatikmitteln’, 252-0839-00 or equivalent). Bring your own laptop – or contact organizers if you do not own a laptop. | |||||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
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| 701-0660-00L | Practical Course in Human Environment Systems | 7 credits | 14P | J. W. McCaughey, D. N. Bresch, V. Gebhart, S. Hanger-Kopp, C. M. Kropf | ||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The students develop research projects on climate change adaptation in which they combine and apply quantitative and qualitative methods for data collection and analysis. The students identify political, economically and socially acceptable adaptation measures and thus also evaluate the social and ethical dimensions of climate risk analysis and adaptation. | |||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students are able to apply a qualitative and quantitative set of methods to answer an interdisciplinary research question at the intersection of human and environment systems. | |||||||||||||||||||||||||||||||||||||||||||||||
| Content | At the core of the Human-Environment Systems Practicum is the question of the technical, economic and social feasibility of measures to adapt to climate change. Strengthening weather and climate resilience in particular is already of eminent importance today - and will continue to increase in the coming decades. The climate of the past is no longer a solid basis for future-oriented decisions due to many changes. Never before have societies had such a broad knowledge of the processes that shape their future and such extensive data - including climate projections. However, we are still at the beginning when it comes to the extensive use of this information in planning and decision-making processes. Central questions for decision-makers and society are therefore: 1) What is the possible influence of weather and climate today and in the coming decades? 2) How can we deal with these opportunities / risks with foresight, and what measures are available? 3) What investments are necessary - do the benefits outweigh the costs? 4) Do the risks fall disproportionately on certain groups, especially those who may already be disadvantaged, and do the adaptation measures reduce this inequality? For an interdisciplinary understanding of these questions, relevant qualitative and quantitative methods, their connection and critical discussion are learned in the internship. These methods include the Economics of Climade Adaptation (ECA, https://wcr.ethz.ch/research/casestudies.html) a probabilistic risk model with open source code (CLIMADA, https://wcr.ethz.ch/forschung/climada .html), Multi-Criteria Analysis (MCA), and qualitative and quantitative methods to identify socially vulnerable groups. The students choose and combine qualitative and quantitative methods in such a way that they are best suited to their research questions. This methodology provides a framework for the assessment of climate risk and the assessment of adaptation options together with decision-makers and society, taking into account various possible socio-economic development paths and climate scenarios. Through this exercise and the comparison of the scenarios, the students get to know connections and interactions, and also to take into account social framework conditions in the modeling. The students critically reflect on the advantages and disadvantages of such assessments, especially with regard to values, ethics and social justice. The students will describe and critically discuss the entire process in a professional project report. | |||||||||||||||||||||||||||||||||||||||||||||||
| Literature | References will be given during the course. | |||||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
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