Teaching information and download centre

Teaching information and download centre

# Modelling in Physical Geography

UNIVIE 290.044 PS3 WS2016 2015

Martin Mergili

In geomorphology, computer models are often employed to analyze or to predict spatial patterns, to improve process understanding, or even to anticipate the occurrence and characteristics of possible future processes. This course introduces to the key aspects of computer modelling. It builds on examples related to mass movement processes and fluvial geomorphology. The modelling techniques are strongly related to the application of Geographic Information Systems (GIS).

The course largely follows a concept of learning by doing. Specific real-world problems are taken as examples. The various aspects of modelling are elaborated by introducing step-by-step solutions of those problems. Even though the course is coupled to specific software packages, it introduces general skills which can be applied with various tools. It also attempts to convey a feeling for which methods are appropriate for which types of tasks. At the end of the course the participants will

- be able to critically evaluate model results;
- be able to apply computer models in an independent and responsible way;
- know the most important software packages for modelling of mass movements and their scope of application;
- know about the possibilities to develop their own model applications;
- be able to design simple model applications by themselves.

Some basic knowledge of GIS (in particular, raster analyses with ArcGIS) is expected from the participants.

The course takes place in the GIS laboratory on the 1st floor of the NIG Building (Universitätsstr. 7). It builds on eight lessons of introductory lectures and code development. Seven more lessons are dedicated to practice and to the final exam. Each lesson has a duration of three teaching units.

Further information on the course on u:find

Teaching method

Presentation

Guided training

### All models are wrong, but some are useful

Computer modelling has become an extensive field with a variety of aspects. Some of the key concepts of modelling will be exemplified on the example of GIS-based landslide susceptibility modelling. The purpose of this type of modelling is to identify those areas in a certain region (ranging from the small catchment scale to the global scale) more likely to be directly affected by landslides than others. In the first lesson you will- learn about the key aspects of modelling
- be introduced to modelling of landslide susceptibility
- implement a simple slope stability model with standard GIS functions
- write your own script for executing the infinite slope stability model

Guided training

### Learn how to program your own slope stability modelling tool

Whilst ready-to-use software packages are available for complex tasks, it may be useful for simple applications or for very specialized tasks to design your own model code. Building on the code developed in the first lesson, you will use the Python programming language to develop a software tool for running the infinite slope stability model, making use of GIS functions. You will- learn how to efficiently combine ArcGIS with the Python programming language
- learn how to automatically run an R script from your python code to produce a map

Presentation

Guided training

### Parameters are uncertain

Models have to be fed with input parameters. However, these parameters ar sometimes hard to determine and are most often highly variable in space. This means that they are uncertain, and these uncertainties reflect themselves in the model results. Appropriate strategies are needed to deal with uncertain parameters. In this lesson you will- be introduced to the general aspects of parameter uncertainty
- extend your python script to account for the uncertainties of the key parameters of the infinite slope stability model

Presentation

Guided training

### Model results have to be validated

Using GIS, one may produce nice colourful maps with any type of model. However, in order to explore to which extent the model results are useful to make predictions they have to be validated against observations. In the case of landslide susceptibility models, these observations most commonly consist in a landslide inventory. You will- learn about the key concepts and issues of model validation, back calculation and forward calculation
- apply ROC plots to validate your model results

Guided training

### Modelling needs practice

In this lesson you will have the chance to finish up those calculations which have caused troubles in the previous lessons. You are further encouraged to practice everything learned until now. For example, you might try to further extend your python scripts in order to add more functionalities to your program codes.Presentation

Guided training

### Mass movements are more complex

In the previous lessons you have become familiar with the infinite slope stability model. However, this type of model - as all models - is suitable for a limited range of applications. More complex models are needed (i) to anticipate where deep-seated rotational slides are likely to happen; (ii) for modelling the propagation of mass movements; or (iii) for coupling slope stability to the hydraulic conditions of the soil or rock. Whilst (i) and (ii) will be introduced on a theoretical basis, you will use the open source software TRIGRS to couple the infinite slope stability model to a hydraulic model. In this lesson you will:- learn about sliding surface models
- see what can be done for anticipating the propagation of mass movements
- gain an overview on GIS-based coupled hydraulic-slope stability models
- use the software TRIGRS to perform your own coupled hydraulic-slope stability model calculations

Guided training

### Rainfall and landslides

The software TRIGRS offers a broad range of functionalities to explore the susceptibility of landslides as a response to rainfall events. Building on the previous lesson, you will- learn more about the functionalities of TRIGRS
- explore these functionalities on your own
- use the Python programming language to automatize the execution of TRIGRS as well as the validation and visualization of the results

Guided training

### More practice

In this lesson you will have the chance to finish up those calculations which have caused troubles in the previous lessons. You are further encouraged to practice everything learned until now. For example, you may try to find out how different rainfall scenarios and parameter combinations influence the model results.Guided training

Mid-term exam

### More practice ... and a little test

You are encouraged to practice everything learned until now. There will further be a small mid-term exam (duration: 60 minutes) concerning the content of the presentations and the general understanding of the possibilities and limitations of modelling gained up to this point. There will be no modelling tasks to be performed, the outcome of the exam will contribute 35 per cent to the final mark.Presentation

Guided training

### Evolution of river profiles

Rivers shape their valleys in various ways. Whilst they tend to equilibrate the longitudinal profile by retrogressive erosion and sedimentation, the transverse profile is influenced by factors such as the rate of incision or sedimentation and by the local geology. In this lesson you will- hear about some general aspects of fluvial valley evolution
- write your own script for predicting the future evolution of river longitudinal profiles by incision into the bedrock, using Python
- create profile plots with R

Guided training

### The exciting life of a fluvial valley

The development of river longitudinal profiles is influenced by a variety of factors such as the supply of sediment in the headwaters, blocking of the valley by natural or artifical dams, changes in the sea level, tectonic uplift or coastal erosion. Focusing on the effects of the latter two processes you will- use and extend the scripts designed in the previous lesson to explore the response of the river longitudinal profile to changes in the key governing parameters
- produce profile plots illustrating the outcomes of certain scenarios

Recapitulation

### Recapitulation: programming with Python

We will repeat the key aspects of Python programming, putting the pieces of code produced in the previous lessons into a broader context.Recapitulation

### Recapitulation: spatial modelling

We will repeat the key aspects of spatial modelling, putting the exercises performed in the previous lessons into a broader context.Guided training

### Prepare for the final exercise

In this lesson you are encouraged to practice everything learned until now. A particular focus is put on preparation for the final exam.Final exercise