1. Course information

1.1. General information

Name of course: Land Surface Process Modelling

Course Code: GEO4-4406

ECTS: 7.5

Category / Level: M (Master)

Teaching period: 3

Contact hours: appr. 8 h / week

Language of instruction: English

1.2. Lecturers

Prof Dr Derek Karssenberg (coordinator)

Department of Physical Geography

Faculty of Geosciences

Utrecht University

Vening Meineszgebouw A

Princetonlaan 8a

3584 CB Utrecht

room 5.20

Phone: (+31) 30 2532768

E-mail: d.karssenberg@uu.nl

Mastodon: https://scicomm.xyz/@derekkarssenberg

https://www.uu.nl/medewerkers/DJKarssenberg

GitHub: https://github.com/DerekKarssenberg

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Oriol Pomarol Moya

Dr Oliver Schmitz

Dr Kor de Jong

Dr Edwin Sutanudjaja

Dr Saeb Faraji Gargari

Department of Physical Geography

Faculty of Geosciences

Utrecht University

1.3. Place in curriculum

The course provides a theoretical and practical basis of spatio-temporal (process-based) modelling of land surface processes, and is relevant for all disciplines related to land surface processes (hydrology, land degradation, geomorphology, natural hazards, ecology). In addition it provides a background in geoinformatics which is relevant for appropriate use of modelling tools and GIS.

1.4. Required background

Knowledge of geography, geosciences, or a related field at an MSc level.

1.5. Entry requirements

You must have study entrance permit.

1.6. Aims and content

1.6.1. Domain related

Numerical simulation models of processes on the earth surface are essential tools in fundamental and applied research in the geosciences. They are used in almost all disciplines in the geosciences, for instance hydrology, geomorphology, land degradation, sedimentology, and most fields in ecology. They are important instruments in research for a number of reasons. First, they provide understanding of how systems work, in particular how system components interact, how systems react to changes in drivers, and how non-linear responses emerge. Also, simulation models can be used to forecast systems, which is essential in planning and decision making. Finally, land surface process models provide a means to evaluate theory of simulated processes against observational data.

In this course we will focus on generic principles of land surface modelling. You will study a number of different approaches to represent land surface processes in a simulation model, including differential equations, rule based modelling, cellular automata, individual (agent) based approaches, and probabilistic models. We will discuss how local interactions in large systems can lead to complexity and the impliction of this for forecasting. Also, you will learn how to combine information from observational data and simulation models using error propagation and calibration.

You will learn how these principles can be applied in a number of different disciplines, in particular in the field of hydrology, geomorphology, sedimentology, and ecology. You will also learn how very similar approaches are used in other fields, for instance in environmental epidemiology and urban geography.

In addition to principles of land surface modelling, you will learn how to use software tools for land surface modelling. You will study theoretical concepts of software environments for land surface modelling, and you will learn how to program land surface models. In this part of the course we will use the Python programming language and modules running in Python, in particular PCRaster and Campo. These tools provide standard frameworks for model construction and techniques to combine a model with observational data. Other tools for model construction use similar concepts, so you will be able to apply your knowledge from this course to other software environments.

The course aims are:

• To retrieve a theoretical basis of land surface modelling, including approaches to represent processes and approaches to combine data and models.

• To retrieve an understanding of how various systems in the geosciences are represented with land surface models.

• To learn principles of software environments for modelling and how to use these software environments.

1.6.2. Transferable skills related

• Ability to work in a team: A number of activities are done in teams of students. Students will learn how to distribute the work over team members and how to cooperate efficiently.

• Written communication skills: A paper is written on which students get extensive feedback from the tutor. In addition, a longer case study report is written structured like a scientific article.

• Problem-solving skills: Students learn to execute all phases of numerical model construction. This requires to solve problems related to concepts of process-based models, the implementation of these models using a programming environment, and the use of various empirical data linked to models. Students are challenged considerably regarding this aspect in the case study project at the end of the course which is done largely without support from the tutor.

• Verbal communication skills: Students present their work in a number of sessions. This teaches them mainly to prepare a well-structured talk in the time span of a few days; in addition they get feedback on the quality of the presentation.

• Strong work ethic: The course is taught as a blended learning course (on-campus and on-line) which means that students need to properly plan their own work.

• Initiative: Students are trained to take initiative, particularly in the case study project.

• Analytical/quantitative skills: A large part of the course relates to various analytical approaches used in forward process-based modelling. Students have to apply these approaches in their own modelling work.

• Technical skills: The course teaches computational thinking in particular during the computer labs on Python programming and PCRaster and Campo programming.

1.7. Course outline (time table)

Course outline and schedule are provided in this document. You can also use https://mytimetable.uu.nl to get access to the schedule (like with all Utrecht University courses), but note that not all scheduled events are actually used.

As shown in the outline of the course in the table below, the course consists of two components, Model Theory and Model Tools. These are run parallel in time. Model theory contains eLectures, a working group meeting, and a paper assignment. Model Tools is taught mainly using computer practicals. At the end of the course you will do a personal project, consisting of case study modelling work, a written report, and oral presentations. The detailed course schedule below gives the date and location of lectures, working groups, and computer practicals. It also provides due dates for the computer practicals and other assignments.

1.8. Calculation of final mark & rubrics

For passing the course students need to:

• Submit answers to the questions of all computer labs, preferably before the deadline of each lab as indicated in the timetable.

• Active participation in working groups.

• Hand-in short paper assignment and report on the personal project before the deadline.

• Get a final mark of 5.5 or higher.

The final mark M is calculated as:

M = 0.1A + 0.6B + 0.1C + 0.2D

with A, the mark for the short paper assignment; B, the mark for the exam; C, the mark for the oral presentation on the case study (final presentation); and D, the mark for the written report on the personal project. A, B, C, and D are not rounded.

Absence (for instance as a result of illness or family circumstances) during the exam must be agreed with the coordinator of the course in advance by phone or email.

The course has been passed if the final grade is >=5.5 and all obligations have been fulfilled. If not, and only if 1) the final grade is 4.00 or higher and 2) all obligations have been fulfilled, a supplementary test (or repeat exam) can be offered. If the supplementary test has been successfully passed, the final grade of the course will be 6.0.

For details on the above and further information, see the OER (Education and Examination Regulations).

A rubric is used for marking the personal project (report and presenation). It can be downloaded here.

1.9. Individual and group work

For some parts of the course you will work in groups:

• Computer Labs: group of 2 students

• Short Paper Assignment: group of 3 students

• Working Group: group of 4 students

• Case Study: group of 4 students

Please self-subscribe for each of these four groups in Blackboard (Course Content section).

1.10. Question based lectures

Slides of the question based lectures are at https://surfdrive.surf.nl/files/index.php/s/cxyStl6DEHCcy04 (as soon as they are available).

1.11. Study material

For the exam, you need to study:

• All material indicated in this document as ‘Literature for exam’. You can download this as one PDF (or order it as a printed syllabus, recommended) from Blackboard.

• Think Python, An introduction to software design, 2nd Edition (!), A. Downey, 2015, Green Tea Press, Needham, 222 pp. Chapters 1, 2, 3, 5, 6, 7, 8, 10, and 14. Online at http://greenteapress.com/wp/think-python-2e/ or order a print from Blackboard.

• All computer practicals to the extend that you need to understand the core concepts of the tools.

• All eLectures.

Note that material indicated in this document as ‘Reading material’ is background material only. It is recommended to read through this but it is not a requirement for the exam.

1.12. Exam

The written exam (individual test) will take place on campus (on paper):

• The exam will be open book. During the exam, you are allowed to use any source of (online or offline) information except information that you get from other persons. It is not allowed to use Large Language Models (e.g., ChatGPT) to generate answers. If you would like to use digital or online information please bring your laptop.

• Study the study material as described in the studyguide.

• Two days before the exam you will receive a research paper. The paper will be on the design or use of a simulation model. Read through the paper 2 or 3 times to be sure you understand it. See below on how the paper is used in the exam.

• The exam will contain a few questions that you need to answer in the context of the paper that you received (see above). For instance: Is the model used in the paper you read a physically-based model or a conceptual model? (note that I will not ask this particular question). So the questions will be general, but in the context of the paper. In addition the exam will contain normal (and open, not multiple choice questions, for instance) on the study materials.

• The following document will give you an impression of the type of questions included in the exam.

1.13. OSIRIS information on the course

Additional information is available at https://www.osiris.universiteitutrecht.nl/osistu_ospr/StartPagina.do

1.14. Form of teaching

The course is run largely following the flipped-teaching model. The idea of flipped teaching is that contact time between student and tutor is not used for explaining theory but mainly for discussion and questions related to the theory. To make this work, it is important you study the learning materials before attending a Question Based Lecture. The time schedule of our course supports this approach to learning. Each week, you will study you particular topic. The first days of the week, or if possible even the week before, it is strongly recommended to study the learning materials related to this topic. This includes pre-recorded eLectures as well as literature. On Wednesday (not all weeks, refer to course time table in this course guide), then, we have the Question Based Lecture during which you can ask questions or particular concepts can be discussed. Your are asked to prepare for this Question Based Lecture by compiling a list of questions.

The components of the course are:

• Recorded eLectures, you can listen to these anytime

• Literature, sections from books or scientific articles

• Question Based Lectures, weekly meeting for questions on study materials, theory or anything important to the course

• Computer Labs, scheduled computer labs with supervision, on campus

• Lecture, introductionary lecture at the start of the course

• Working group session

• Short paper assignment

• Personal project including a final presentation and written report

Communication is through:

• Questions and discussion during Lab hours on campus

• E-mail, for other questions, e-mail the course coordinator

• Blackboard, used for uploading your answers to questions in the labs and other assignments

1.15. Disclaimer

In case of unforeseen circumstances, it may be necessary to make last minute changes to the course compared to what has been described in this manual. E.g. lectures or exams may be held online or an exam may be replaced by an assignment. The course coordinator will keep students up to date with the latest information.