Course Objectives: - Develop an understanding of the physical principles governing the Earth’s water cycle and its variability across spatial and temporal scales. - Explore how human activities and climate change alter hydrological processes and water availability. - Introduce students to remote sensing technologies and Earth observation systems used to monitor water resources. - Provide hands-on experience in analyzing and interpreting satellite-derived hydrological data. - Prepare students to apply remote sensing methods for real-world challenges such as water management, disaster response, and sustainable development.
Course Prerequisites
This class has no formal requirements and students will be given the opportunity to learn basic python coding skills for the course in the context of the practical learning activities
Teaching Methods
Lectures and guided problem-solving sessions in class, integrated with two practical exercises carried out in the laboratory in small groups.
Assessment Methods
Written exam
Texts
Introduction to Hydrology (e-book), Steven Margulis (https://margulis-group.github.io/textbook/) Chuvieco, Fundamentals of Satellite Remote Sensing, An Environmental Approach. Campbell, Introduction to Remote Sensing 5th ed.
Contents
The water cycle I, fundamentals: - Water budgets - Storages/fluxes and their global spatial and temporal variabilities Basic laws of physics that describes water variability The water cycle II, the human influence: - Effects of Climate Change to the water cycle - Extremes (floods and droughts) Water management (e.g., Irrigation) Earth Observations I: - Fundamentals (Electromagnetic spectrum, properties) - Satellite observations of terrestrial water storage and groundwater. First Practical Learning Activity Earth Observations II: - Remote Sensing of soil moisture and evapotranspiration - Remote Sensing of seasonal snow - Observing changes from space: the human vs. natural drivers Second Practical Learning Activity Navigating in a data rich world: - Satellite, Data Assimilation, and Artificial Intelligence: opportunities for the water cycle Third Practical Learning Activity Exam
