Simulations Data for Sublacustrine Landslide-Induced Paleotsunami in NW Alpine Lake
<p>This repository contains the simulations data used in our manuscript, titled "<strong>Numerical Reconstruction of Landslide Paleotsunami Using Geological Records in Alpine Lake Aiguebelette</strong>' by Muhammad Naveed Zafar, Denys Dutykh, Pierre Sabatier, Mathilde Banjan, and Jihwan Kim.<br><a title="https://doi.org/10.1029/2023JB028629" href="https://doi.org/10.1029/2023JB028629">https://doi.org/10.1029/2023JB028629</a></p> <p><br><strong># README</strong> to reproduce the results for mass movement and related tsunami models.</p> <p><strong>## Folders</strong><br>- `Mass_Movement_Model`: Data related to the mass movement model.<br>- `NSWE`: Data for Nonlinear Shallow Water Equations (NSWE) simulations.<br>- `BOUSSINESQ`: Data for Boussinesq equation simulations.<br>- `Amplitude_time_series`: Time series data for amplitude.<br>- `Runup_time_series`: Time series data for runup height.</p> <p><strong>## Contents</strong><br>- `_output` directory: Contains output data files generated by the simulations.<br>- `Makefile`: Script to automate the compilation and execution of the model.<br>- `setplot.py`: Python script for setting up the plot configurations.</p> <p><strong>## Installation</strong><br>To use the data and run the models, you need to install Clawpack 5.8.2. Installation instructions can be found here: <a href="https://www.clawpack.org/v5.8.x/installing_pip.html#install-quick-all">Clawpack5.8.2 Installation Guide</a>.</p> <p><strong>## Running the Models</strong><br>After installing Clawpack, you can generate plots by navigating to any of the model directories (Mass_Movement_Model, NSWE, BOUSSINESQ, amplitude_time_series, Runup_time_series) and running the following command:<br>```<br><em><strong>make plots</strong></em><br>```<br>This command will compile and execute the models, and the plots will be saved in the same directory.</p> <p><strong>## Support</strong><br>For any questions or issues related to this data, please contact [syednaveed1421@gmail.com].</p>
ShareScore
32/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 4
- Harmonization
- 4
- Access
- 16
- Reuse readiness
- 8
- Engagement
- 0