This is a bedload transport model that was created to:
- Simulate many different sediment transport scenarios in parallel (Monte-Carlo style)
- Simulate extreme sediment supply conditions (large, episodic supply events)
My thesis gives an overview of the topic and the model:
Müller, J. T. (2019). Modelling fluvial responses to episodic sediment supply regimes in mountain streams (T). University of British Columbia. Retrieved from https://open.library.ubc.ca/collections/ubctheses/24/items/1.0377728
The two main applications were
-
Simulating episodic sediment in the flume lab at UBC. Paper:
Müller, T., & Hassan, M. A. (2018). Fluvial response to changes in the magnitude and frequency of sediment supply in a 1-D model. Earth Surface Dynamics, 6(4), 1041-1057. -
Simulate different options in management of a potential San Clemente Dam removal Paper: in preperation AGU 2020 talk: https://agu.confex.com/agu/fm20/meetingapp.cgi/Paper/743304
Matlab versions used were 2015b and 2018b
Garry Parker's ebook is most helpful. Many of the solutions are based on that.
- Only Wilcock and Crow works. Ashida Michue is not tested thoroughly, but files are there..
- Reaches are not implemented in this version, but sediment feed can be implemented at any node.
- There are a bunch of unused/old scripts in the repo. Best follow the execution, halt the program if you want to implement something. Set breakpoints etc.
- I refactored some of the variable names a while back which might be a bit confusing (some unused old variable names might still be active).
I deleted a bunch of configurations for the 508 projects of Alex and Conor. The current configuration executes a version of the permuted pulse sequence from Maria's experiments.
Generate a .mat file (binary workspace snapshot) using a configuration script, e.g.: genconf_VX_MCPulse.m. The run_SimPulseV87_MC.m file does this automatically.
Result is a runconfig_*.mat file that has all variables stored needed for execution.
If there are multiple scenarios of for example grain size distribution or feedrate,
one runconfig_*.mat file is generated per scenario. Each scenario can additionally be
executed for a number of runs each. For example I iterate through grain size distributions
per scenario and then iterate through event frequencies per run.
Using scenario-iterations is recommended over using run-iterations.
In all run_*.m scripts the configuration is referred to by name (Set at the top of genconf_*.m).
If there are multiple scenarios they have to be iterated through by their file endings.
The iteration should be copied from the runconfig_*.mat file generation at the end of the genconf_*.m script
Starting an analysis is done similarly to executing a simulation. Iterate through
runconfig_*.mat, generate folders and do the plotting.
This is a bit rough as of writing (2020-11-25). The analysis scripts are a bit messy, sorry for that.
I'll add some more scripts with different run configurations as examples.