Climate Change and Water in Mountains: A Global Concern
What is climate change? How are mountain regions affected by the evolution of water resources and their uses? What kind of risks need to be considered?
Mountains are recognized as particularly sensitive physical environments where intense and rapid changes have in the past, and may increasingly in the future, place pressure on their resource base.
About this Course
In this context, a team of roughly 100 experts worked from 2008 to 2013 for the European ACQWA project (www.acqwa.ch) which was coordinated by the University of Geneva. The primary objectives of the project were to assess the impacts of a changing climate on the quantity and quality of water originating in mountain regions, particularly where snow- and ice melt represent a large, sometimes the largest, streamflow component. A further objective of the project was to determine the potential disruptions to water-dependent economic activities related to the climate impacts on hydrological systems and to propose a portfolio of possible adaptation strategies.
Read more
This particular MOOC is inspired by the ACQWA Project and offers a better understanding of climate change, its impacts on the quality and quantity of water in mountain regions and the risks related to changing water resources. From an interdisciplinary perspective, the participation of twenty-five instructors from five different countries (Switzerland, England, South Korea, India and Nepal) and fourteen institutions (UNIGE, RTS, UNIFR, UZH, ETHZ, Meteodat GmbH, WGMS, Imperial College London, Agroscope, République et Canton de Genève, Yonsei University, IHCAP, ICIMOD, SDC, FOEN) highlights the diversity of both theoretical and practical viewpoints related to these issues.
By the end of this course, you will be able :
- to define the general concept of climate change in mountain regions
- to understand the concepts associated with climate change such as adaptation and water governance strategies
- to consider the impacts of climate change on water resources in mountain regions
- to identify the impacts of climate change on hydropower, agriculture, aquatic ecosystems and health
- to enumerate risks that can occur in mountain areas and lead to disruptions in water availability and use.
Your acquired knowledge will be evaluated through multiple-choice quizzes at the end of each unit of the course.
This MOOC on “Climate Change and Water in Mountain Regions : A Global Concern” was initiated and financed by the University of Geneva, through its Institute for Environmental Sciences.
We look forward to you joining us !
Instructors
Mine Çetinkaya-Rundel
Associate Professor of the Practice Institute for Environmental Sciences
Ann Maisner
Associate Professor of the Practice Institute for Environmental Sciences
Tomas Abbar
Associate Professor of the Practice Institute for Environmental Sciences
Agenda & Syllabus - What you will learn from this course
10.10.2021, 12:00 PM - 14:00 PM
Models: How to See More by Looking at Less
This course introduces you to sampling and exploring data, as well as basic probability theory. You will examine various types of sampling methods and discuss how such methods can impact the utility of data analysis. The concepts in this module will serve as building blocks for our later courses. Each lesson comes with a set of learning objectives that will be covered in a series of short videos. Supplementary readings and practice problems will also be suggested from OpenIntro Statistics, 3rd Edition, https://leanpub.com/openintro-statistics/, (a free online introductory statistics textbook, that I co-authored). There will be weekly quizzes designed to assess your learning and mastery of the material covered that week in the videos. In addition, each week will also feature a lab assignment, in which you will use R to apply what you are learning to real data. There will also be a data analysis project designed to enable you to answer research questions of your own choosing. Since this is a Coursera course, you are welcome to participate as much or as little as you’d like, though I hope that you will begin by participating fully. One of the most rewarding aspects of a Coursera course is participation in forum discussions about the course materials. Please take advantage of other students' feedback and insight and contribute your own perspective where you see fit to do so. You can also check out the resource page (https://www.coursera.org/learn/probability-intro/resources/crMc4) listing useful resources for this course. Thank you for joining the Introduction to Probability and Data community! Say hello in the Discussion Forums. We are looking forward to your participation in the course.
10.10.2021, 14:00 PM - 15:00 PM
Introduction to Data
This course introduces you to sampling and exploring data, as well as basic probability theory. You will examine various types of sampling methods and discuss how such methods can impact the utility of a data analysis. The concepts in this module will serve as building blocks for our later courses.Each lesson comes with a set of learning objectives that will be covered in a series of short videos. Supplementary readings and practice problems will also be suggested from OpenIntro Statistics, 3rd Edition, https://leanpub.com/openintro-statistics/, (a free online introductory statistics textbook, that I co-authored). There will be weekly quizzes designed to assess your learning and mastery of the material covered that week in the videos. In addition, each week will also feature a lab assignment, in which you will use R to apply what you are learning to real data. There will also be a data analysis project designed to enable you to answer research questions of your own choosing. Since this is a Coursera course, you are welcome to participate as much or as little as you’d like, though I hope that you will begin by participating fully. One of the most rewarding aspects of a Coursera course is participation in forum discussions about the course materials. Please take advantage of other students' feedback and insight and contribute your own perspective where you see fit to do so. You can also check out the resource page (https://www.coursera.org/learn/probability-intro/resources/crMc4) listing useful resources for this course. Thank you for joining the Introduction to Probability and Data community! Say hello in the Discussion Forums. We are looking forward to your participation in the course.
10.10.2021, 15:00 PM - 18:00 PM
Exploratory Data Analysis and Introduction to Inference
This course introduces you to sampling and exploring data, as well as basic probability theory. You will examine various types of sampling methods and discuss how such methods can impact the utility of a data analysis. The concepts in this module will serve as building blocks for our later courses.Each lesson comes with a set of learning objectives that will be covered in a series of short videos. Supplementary readings and practice problems will also be suggested from OpenIntro Statistics, 3rd Edition, https://leanpub.com/openintro-statistics/, (a free online introductory statistics textbook, that I co-authored). There will be weekly quizzes designed to assess your learning and mastery of the material covered that week in the videos. In addition, each week will also feature a lab assignment, in which you will use R to apply what you are learning to real data. There will also be a data analysis project designed to enable you to answer research questions of your own choosing. Since this is a Coursera course, you are welcome to participate as much or as little as you’d like, though I hope that you will begin by participating fully. One of the most rewarding aspects of a Coursera course is participation in forum discussions about the course materials. Please take advantage of other students' feedback and insight and contribute your own perspective where you see fit to do so. You can also check out the resource page (https://www.coursera.org/learn/probability-intro/resources/crMc4) listing useful resources for this course. Thank you for joining the Introduction to Probability and Data community! Say hello in the Discussion Forums. We are looking forward to your participation in the course.
FAQ
When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
Will I receive a transcript from Duke University for completing this course?
No. Completion of a Coursera course does not earn you academic credit from Duke; therefore, Duke is not able to provide you with a university transcript. However, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.