Land surface process modelling
To retrieve a theoretical basis of land surface modelling, including approaches to represent processes and approaches to combine observations and models.
To learn how to execute all steps in the model development cycle: development of the conceptual model, programming the model, model calibration, validation, and error propagation modelling.
To learn principles of software environments for modelling and how to use these software environments.
Content
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 can lead to complexity at a larger scale and the implications of this for forecasting. Also, you will learn how to combine information from observational data and simulation models using error propagation, calibration, and data assimilation techniques.
During the course 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 urban geography and social sciences.
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 PCRaster. 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. This course can also be interesting for MSc students in ecology, environmental science, sustainability science, or energy science.
Development of Transferable Skills
Ability to work in a team: Oral presentations and the case study report are written in teams of 2-3 students. Students will learn how to distribute the work over team members and how to cooperate efficiently.
Written communication skills: Three two-page papers are 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 two working group sessions. This teaches them mainly to prepare a well-structured talk in the time span of a few days; in addition they get limited feedback on the quality of the presentation.
Strong work ethic: The course is taught as a blended learning course which means that students need to properly plan their own work.
Initiative: Students are trained to take initiative, particularly in the case study projects.
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 programming.
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Land surface process modelling
Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or HaDEA. Neither the European Union nor the granting authority can be held responsible for them. The statements made herein do not necessarily have the consent or agreement of the ASTRAIOS Consortium. These represent the opinion and findings of the author(s).
