Earth System Science
A 200-level or 300-level course in the Earth, Energy & Sustainability (EES) Major
Quantative Research Methods, or
Geographic Information Systems
Numerical models are an essential part of geoscience research and an important tool for engineers, natural resource managers, and policymakers. The most famous examples are (of course) the General Circulation Models (GCM): the climate models employed construct future climate scenarios for, among others, the IPCC. Yet, we can find many more models in Earth system science that are used in research or to design policy and infrastructure. For example, we can determine the properties and location of riverine and coastal flood infrastructure through a hydrologic model , and statistical models can inform us about flood magnitudes. Likewise, ice sheet models are used to predict future sea-level change, and biogeochemical models can help us to locate potential harmful algal blooms. For an environmental scientist it is essential to know the basics of Earth system modeling so they can access and use their outputs.
However, the Earth is also a complex system that cannot easily be described by a set of linear relationships. For models to be informative, one requires a thorough understanding of these complex relations and the knowledge to translate the complexities into an Earth system models.
This course offers an introduction to the workings, pros, and cons of several Earth system models. Students will build simple Earth models themselves and work with model output in commonly used datasets. In the course, we will work through three models to study a hydroclimatic extreme event and its aftermath. First, student will build statical models based on Extreme Value Theory, next they will work with climate model output, and finally they will build an hydrologic model in the HEC-RAS software.
At the end of the course students can:
Comprehensively characterize Earth system models and assess their quality.
Describe the basics of Extreme Value Theory, General Circulation Models, reanalysis data, and hydrologic models.
Describe how different types of models can be calibrated, validated and used for prediction.
Describe possible problems with capturing complex natural processes in a model, and the potential solutions.
At the end of the course students can:
Analyses different types of Earth system data
Build simple statical models of extreme events in R
Build simple hydrological models in HEC-RAS
Employ Earth system models to solve sustainability challenges
Timetables for courses offered at Leiden University College in 2022-2023 will be published on this page of the e-Prospectus.
Mode of instruction
This course focusses on both the theory on which the discussed Earth system models are based and the practical aspects of computer modeling. Classes will start with lectures presenting the necessary background to study Earth system models. Next students will work (together) on computer exercises in which they apply the concepts from the lectures. Students will learn to apply Extreme Value Theory, reanalysis data and the HEC-RAS software to study an extreme event.
Throughout the course we will use the program language R. Student should be able to at least open an R script and run it.
Week 1: What is an Earth model?
Week 2: Statistical models in Earth science.
Week 3: Extreme Value Theory
Week 4: General Circulation Models
Week 5: Reanalysis Data
Week 6: Hydrologic models and HEC-RAS
Week 7: Coupled Earth system models
Lab I: Project description (10%)
Lab II: Extreme Value Theory (15%)
Lab III: Reanalysis Data (15%)
Lab IIII: HEC-RAS (15%)
Group project: Coupled Earth system models (35%)
Readings and learning materials will be provided throughout the course.
Courses offered at Leiden University College (LUC) are usually only open to LUC students and LUC exchange students. Leiden University students who participate in one of the university’s Honours tracks or programmes may register for one LUC course, if availability permits. Registration is coordinated by the Education Coordinator, firstname.lastname@example.org.
Dr. Joeri Reinders, email@example.com