Search result: Catalogue data in Spring Semester 2024
MAS in Sustainable Water Resources  The Master of Advanced Studies in Sustainable Water Resources is a 12 month full time postgraduate diploma programme. The focus of the programme is on issues of sustainability and water resources in Latin America, with special attention given to the impacts of development and climate change on water resources. The programme combines multidisciplinary coursework with high level research. Sample research topics include: water quality, water quantity, water for agriculture, water for the environment, adaptation to climate change, and integrated water resource management. Language: English. Credit hours: 66 ECTS. For further information please visit: http://www.mas-swr.ethz.ch/ | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
 Elective CoursesElectives: 6 credits has to be achieved. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | ||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 701-1232-00L | Radiation and Climate Change | W | 3 credits | 2G | M. Wild | ||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This lecture focuses on the prominent role of radiation in the energy balance of the Earth and in the context of past and future climate change. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The aim of this course is to develop a thorough understanding of the fundamental role of radiation in the context of Earth's energy balance and climate change. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The course will cover the following topics: Basic radiation laws; sun-earth relations; the sun as driver of climate change (faint sun paradox, Milankovic ice age theory, solar cycles); radiative forcings in the atmosphere: aerosol, water vapour, clouds; radiation balance of the Earth (satellite and surface observations, modeling approaches); anthropogenic perturbation of the Earth radiation balance: greenhouse gases and enhanced greenhouse effect, air pollution and global dimming; radiation-induced feedbacks in the climate system (water vapour feedback, snow albedo feedback); climate model scenarios under various radiative forcings. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Slides will be made available | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | As announced in the course | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 701-1252-00L | Climate Change Uncertainty and Risk: From Probabilistic Forecasts to Economics of Climate Adaptation  | W | 3 credits | 2V + 1U | D. N. Bresch, R. Knutti | ||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The course introduces the concepts of predictability, probability, uncertainty and probabilistic risk modelling and their application to climate modeling and the economics of climate adaptation. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students will acquire knowledge in uncertainty and risk quantification (probabilistic modelling) and an understanding of the economics of climate adaptation. They will become able to construct their own uncertainty and risk assessment models (in Python), hence basic understanding of scientific programming forms a prerequisite of the course. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The first part of the course covers methods to quantify uncertainty in detecting and attributing human influence on climate change and to generate probabilistic climate change projections on global to regional scales. Model evaluation, calibration and structural error are discussed. In the second part, quantification of risks associated with local climate impacts and the economics of different baskets of climate adaptation options are assessed – leading to informed decisions to optimally allocate resources. Such pre-emptive risk management allows evaluating a mix of prevention, preparation, response, recovery, and (financial) risk transfer actions, resulting in an optimal balance of public and private contributions to risk management, aiming at a more resilient society. The course provides an introduction to the following themes: 1) basics of probabilistic modelling and quantification of uncertainty from global climate change to local impacts of extreme events 2) methods to optimize and constrain model parameters using observations 3) risk management from identification (perception) and understanding (assessment, modelling) to actions (prevention, preparation, response, recovery, risk transfer) 4) basics of economic evaluation, economic decision making in the presence of climate risks and pre-emptive risk management to optimally allocate resources | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Powerpoint slides will be made available. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Many papers for in-depth study will be referred to during the lecture. For the exercises the CLIMADA platform- https://wcr.ethz.ch/research/climada.html - will be (extensively) used. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Hands-on experience with probabilistic climate models and risk models will be acquired in the tutorials; hence good understanding of scientific programming forms a prerequisite of the course, in Python (teaching language, object oriented) or similar. Basic understanding of the climate system, e.g. as covered in the course 'Klimasysteme' is required. Examination: graded tutorials during the semester (benotete Semesterleistung) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 701-1260-00L | Climatological and Hydrological Field Work | W | 2.5 credits | 3P | M. Hirschi, M. Rösch, S. I. Seneviratne | ||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Practical work using selected measurement techniques in meteorology and hydrology. The course consists of field work with different measuring systems to determine turbulence, radiation, soil moisture, evapotranspiration, discharge and the atmospheric state as well as of data analysis. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Learning of elementary concepts and practical experience with meteorological and hydrological measuring systems as well as data analysis. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Practical work using selected measurement techniques in meteorology and hydrology. The course consists of field work with different measuring systems to determine turbulence, radiation, soil moisture, evapotranspiration, discharge and the atmospheric state as well as of data analysis. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | The course takes place in the hydrological research catchment Rietholzbach (field work) and at ETH (data analysis) as a block course. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 701-1342-00L | Agriculture and Water Quality | W | 3 credits | 3G | C. H. Stamm, E. Frossard, H. Singer | ||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Linking scientific basics of different disciplines (agronomy, soil science, aquatic chemistry) with practical questions in the context of real-world problems of diffuse pollution due to agricultural production. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | This course discusses the application of scientific understanding in the context of real-world situations of diffuse pollution caused by agricultural production. It aims at understanding the relevant processes, analysing diffuse pollution and developing mitigation strategies starting from legal requirements regarding water quality. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | - Diversity of diffuse agrochemical pollution - Agronomic background on the use of agrochemicals - Transport of agrochemicals from soils to water bodies - Development of legal requirements for water quality - Monitoring strategies in water bodies - Mitigation strategies - Relevant spatial and temporal scales - Exercises including all major topics - 1 field excursion - 2 - 3 external guest speakers | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Handouts will be provided for each topic. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Some exercises require R (http://www.r-project.org/) and a laptop during the class. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 701-1522-00L | Multi-Criteria Decision Analysis | W | 3 credits | 2G | J. Lienert | ||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This introduction to "Multi-Criteria Decision Analysis" combines prescriptive Decision Theory (Multi-Attribute Value and Utility Theory) with practical application and computer-based decision support systems. Aspects of descriptive (behavioral) Decision Theory (psychology) are introduced. Participants apply the theory to an environmental decision problem (group work). | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The main objective is to learn "Multi-Attribute Value Theory" (MAVT) and apply it step-by-step to an environmental decision problem. Multi-Attribute Utility Theory" (MAUT) is shortly introduced. At the end, participants should be able to carry out MCDA on their own, in research projects and in practice (e.g., working as consultant). The participants learn how to structure complex decision problems and break them down into manageable parts. An important aim is to integrate the objectives and preferences of different decision-makers or stakeholders. The participants will practice how to elicit subjective (personal) preferences from stakeholders with structured interviews. They will learn to include uncertainty in decision models and test assumptions with sensitivity analyses. Participants should have an understanding of people's limitations to decision-making, based on insights from descriptive Decision Theory. They will use formal computer-based tools to integrate "objective / scientific" data with "subjective / personal" preferences to find consensus solutions that are acceptable to different stakeholders. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | GENERAL DESCRIPTION Multi-Criteria Decision Analysis is an umbrella term for a set of methods to structure, formalize, and analyze complex decision problems involving multiple objectives (aims, criteria), many different alternatives (options, choices), and different stakeholders which may have conflicting preferences. Uncertainty (e.g., of environmental data) adds to the complexity of environmental decisions. MCDA helps to make decision problems more transparent and guides stakeholders into making rational choices. Today, MCDA-methods are being applied to many complex decision situations. This class is designed for participants interested in transdisciplinary approaches that help to better understand real-world decision problems and that contribute to finding sustainable solutions. The course focuses on "Multi-Attribute Value Theory" (MAVT). It gives a short introduction to "Multi-Attribute Utility Theory" (MAUT) and behavioral Decision Theory, the psychological field of decision-making. STRUCTURE The course consists of a combination of lectures, exercises and discussion in the class, exercises in small groups, and reading. Some exercises are computer assisted, applying the ValueDecisions app, a browser-based MCDA software in a user-friendly R-shiny interface. For the analyses, participants need a laptop. The participants will choose an environmental case study to work on in small groups throughout the semester. They will summarize this work in a graded report. Additional reading of selected sections in the textbook Eisenführ et al. (2010) is required to understand the theory. Participants’ individual learning of MCDA will be tested in one mandatory quiz. GRADING The grade for the course is determined by one mandatory quiz at a fixed date that is individually completed during class (30%) and a semester-long group project with a final written group report to be delivered at the end of the semester (70%). There is no possibility to repeat the quiz! If participants miss the mandatory quiz, it is graded 1. Last cancellation / deregistration date for this graded semester performance: second Tuesday in March! Please note that after that date no deregistration will be accepted and the course will be considered as “fail” / unsatisfactory grade. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | No script (see below) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Theory is supported by reading selected sections in: Eisenführ, Franz; Weber, Martin; and Langer, Thomas (2010) Rational Decision Making. 1st edition, 447 p., Springer Verlag, ISBN 978-3-642-02850-2. Additional reading material will be recommended during the course. Lecture slides will be made available for download. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | The course requires some understanding of (basic) mathematics. The "formal" parts are not too complicated and we will guide students through the mathematical applications and use of the ValueDecisions app (software). Participants should bring their own laptop (let us know if this is not possible). The course is limited to 30 participants (first come, first served). | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Master's Thesis | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | ||||||||||||||||||||||||||||||||||||||||||||||||||||
| 118-0121-00L | Master's Thesis | O | 24 credits | 51D | Lecturers | ||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Students propose relevant research topics from their home countries, or from Latin American research projects, around which individual study programmes are devised, and on which they write their thesis. The Master thesis is supervised by scientific staff at ETH and collaborating institutions, and is based on the student's academic or professional experience. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The Master Thesis research takes place throughout the duration of the MAS Programme (12 months), complimented by Master level coursework and Seminars focusing on Water Resources and Sustainability. Students become familiar with new research techniques, and receive guidance from experts. The topic of the research should address a relevant water resources problem in the student's home country, and is aimed at enhancing collaboration between academics and professionals in Latin America and in Switzerland. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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