GeoClaw Example
Alaska 1964 - Tsunami simulation
Goals:
- Runs a real-event tsunami simulation.
- Create a deformation file from a *.csv file using the geometries from the NOAA SIFT database, and configure a time-dependent fault rupture.
- Use the 0-360 convention for the geographic coordinate system
- Plot the maximum tsunami amplitude in an area of interest with high-resolution topographic data.
First, download the data:
We have created a shared folder with the data needed to simulate the Alaska 1964 event and its impact on Crescent City. This is the link: https://drive.google.com/drive/folders/1yTSbAibZvip63PDltkambUiR7xZ21wzZ?usp=sharing
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After downloading the shared folder, you can just move the folder to your working directory(in the Linux system), as it is shown in the image:
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Now, copy the topographic files ‘Pacific_domain.asc’ and ‘crescent_domain.asc’ to your “scratch” directory. Important: Both files follow the 0-360 convention for the longitude:
- Your directory should look like this(don't worry about the *.zone.Identifier file, you can remove them):
![][image3]
Second, check the maketopo.py file:
This section has been adapted from: https://www.clawpack.org/geoclaw/dtopotools_examples.html
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Check the lines that indicate the filenames.
‘Alaska1964_timedep.csv’ contains the geometry of 12 sub_faults rupturing during 13 seconds. The geometry of each sub_fault is the same as the NOAA SIFT database. After the creation, the deformation file ‘Alaska1964.tt3’ will be saved at the ‘scratch_dir’.
![][image4] -
Check the fault specification using the *.csv file containing the geometry of the fault plane. The statement “ fault.read” read the fault plane geometries from the file ‘ alaska1964_timedep.csv’.
![][image5] -
Check spatial domain coordinates ( x , y ) to create a meshgrid for the seafloor deformation → sea level perturbation using Okada. The rupture time is created by the linspace ‘times’. Note that we are using the 0-360 convention for the meshgrid coordinates.
![][image6] -
Check the plot of the seafloor deformation/sea level perturbation is created using just the time 13. Note you can create an animation (read: https://www.clawpack.org/geoclaw/dtopotools_examples.html#Read-in-an-existing-dtopo-file:
![][image7]
Third, check the setrun.py file:
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Check the spatial domain specification at the coarsest grid. The coordinates for the longitude are 0-360. The cell size will be 30 minutes.
![][image8] -
Check the simulation time. In this case, 6 hours (6*3600)
![][image9] -
Check the AMR parameters
The list of level refinement ratios is related with your hardware and your specific problem. You should try another combination to reach a high resolution, but considering the associated computational cost. In general the cost will depend on the available RAM memory and the number of processors. You should try a combination suitable to your problem and your hardware. The problem constrain is because the AMR algorithm not only depends on the defined regions, but also in the value of the tsunami amplitude to decide where refine or not the grids, and this information is not known a priori the simulation. You can also use a good nesting of the grids refined as regions in the “regiondata” section. You must know that there is not aunique solution to define the best level of ratios and to get the best time and high resolution.
![][image10]
This combination of ratios of refinement, let the simulation go from the 30’ of grid size to 11” at the level number 5. -
Check the AMR regions and the DART/Tide gauges configuration.
Some virtual gauges to register the tsunami propagation
![][image11] -
Check the setup for the topography files and the deformation file:
In this section, the path of the topography files is defined.
![][image12] -
Check the configuration for the maximum tsunami amplitude at Crescent City, the area of interest. This section is used to define where is the area of interest, in order to get tha maximum value that can be used to plot the maximum inundation area.
![][image13]
Fourth, check the setplot.py file:
To plot the output simulations, we must check the longitude limits according to the 0-360 convention. Remember to ‘plotitem.pcolor_cmin = -0.5’ in the base of the results of your simulation.
![][image14]
Fifth, check the plot_fgmax.py file:
Here, you don’t need to change anything; just check how this script works:
![][image15]
Sixth, RUN the model:
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Generate the Executable file Geoclaw. Write:
>>make new -
Generate the deformation file. Write:
>>make topoCheck the generation of this plot: Alaska1964.png
![][image16] -
Generate the *.data input files. Write:
>>make .data -
Run the model and generate the _output folder. Write:
>>make .output -
Plot the outputs and generate the _plots folder. Write:
>>make .plots -
Plot maximum tsunami amplitude at the area of interest. Write:
>>make maxCheck the generation of this plot: amplitude_times.png
![][image17]
[image3]: 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