WATER FOOTPRINT
- Course
- ENVE313 - WATER FOOTPRINT
- Department
- Environmental Engineering - English - Undergraduate
- Course Type
- Course
- Status
- Required
- Language
- English
- Credit
- 3
- ECTS
- 4
- T+P+L
- 3 + 0 + 0
- Course Coordinator(s)
- Assoc. Prof. Dr. Şifa DOĞAN
- Prerequisite
- -
Course Description
The aim of this course is to discuss personal, industrial and countrywide water consumption behavior and factors affecting it as well as strategies to achieve goals set for sustainable water consumption by United Nations. Some of the themes that will be covered in this course are water footprint, blue, green and grey water footprints, water footprint evaluation steps, personal water footprints, agricultural and industrial footprints, Country’s footprints, availability and dependency on water, status of country’s water footprints. Students will be able to calculate their own footprints. They will evaluate the water footprint values for various countries and discuss the differences. Students will observe the uneven distribution of the world’s water resources and also the differences in worldwide consumption habits. In this course the United Nations Sustainable Development Goals particularly the ones involving water footprint themes will be investigated.
WATER FOOTPRINT
Evaluation Tools (Active Term)
No evaluation items have been defined.
Course outcomes
No course outcomes have been defined yet.
Course Syllabus
| Week | Topic |
|---|---|
| Week 1 | Introduction to Water Footprints: Concept, metrics, history |
| Week 2 | Introduction to Water Footprints: Concept, metrics, history |
| Week 3 | The Water Footprint Assessment (WFA) Framework |
| Week 4 | Agricultural Water Footprints |
| Week 5 | Industrial and Corporate Water Accounting |
| Week 6 | Review |
| Week 7 | Midterm exam |
| Week 8 | Midterm exam |
| Week 9 | Homework, project topic assignments and discussions |
| Week 10 | National and Consumer Water Footprints |
| Week 11 | Water Footprint Sustainability Assessment |
| Week 12 | Water Footprint Sustainability Assessment |
| Week 13 | Homework discussions (Project discussions, presentations) |
| Week 14 | Homework discussions (Project discussions, presentations) |
| Week 15 | Final exam |
Reference Books & Course Materials
No reference books have been listed.
Learning Outcomes
No learning outcomes have been defined.
Program Outcomes
- P01 Apply data science principles and techniques to challenges in real life situations, and effectively communicate their solutions.
- P02 Identify and implement data analysis methodologies based on theoretical ideas, ethical code, and in-depth knowledge of the underlying data.
- P03 Analyze the guiding concepts and assessment procedures for information analysis in real-life applications.
- P04 Design and apply relevant data analysis models to find obscure solutions to business-related problems.
- P05 Utilize modern computing techniques to handle real-world problems characterized by massive amounts of data, such as parallel and distributed computing and machine learning.
- P06 Configure and administer the software tools required to efficiently produce usable information from any size of structured and unstructured datasets.
- P07 Administer or manage data science tools and techniques to organize and complete projects aimed at gaining useful insight from complex data.
- P08 Think critically and imaginatively, conceiving real-world issues from several angles, and work well in a variety of teams to solve issues cooperatively.
- P09 Be able to effectively integrate data‐based solutions into the user environment and help non-technical professionals in exploring, visualizing, and using these solutions
- P10 Understand their obligations under professional and ethical standards in relation to matters like data ownership and citation, data security and sensitivity and the privacy implications of data analysis.
Po-Lo Matrix
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