Rainfall Induced Landslide Susceptibility Maps using GIRI model
Creators
- 1. Norwegian Geotechnical Institute
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2.
Universitat Politècnica de Catalunya
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3.
OsloMet – Oslo Metropolitan University
Description
The dataset provides global rainfall-induced landslide susceptibility maps developed using Global Infrastructure Resilience Index (GIRI) model. The maps categorize different terrains into five susceptibility classes, considering factors such as slope (derived from Yamazaki et al., (2019)), vegetation (land cover) (Copernicus Climate Change Service, Climate Data Store, 2019; Defourny et al., 2021), lithology (Hartmann and Moosdorf, 2012), and mean of the year average monthly rainfall (Lange, 2019;
Frieler et al., 2017), using global datasets. Rainfall information is gathered from the W5E5 dataset for the timespan of 1979-2016 and the IPSL-CM6A-LR climate model from the ISIMIP3b dataset, covering the SSP126 scenario for 2061-2100.
The dataset is organized as below:
Two compressed folders corresponding to two different climatic scenarios (W5E5 and SSP126)
Each folder is then organized geographically, following a grid-based system based on latitude and longitude intervals. Each folder contains 57 subfolders with names ending with n00e00, n00e30, up to s60w180, where n stands for north, s for south, denoting latitudes, and e stands for east, w stands for west, denoting longitudes. Both latitudes and longitudes have 30° increments. Each subfolder then contains multiple .tif files corresponding to smaller 5° × 5° tiles (e.g., n00e005, n00e010).
Files
Landslide_Susceptibility_GIRI_SSP126.zip
Files
(3.9 GB)
| Name | Size | Download all |
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md5:de62b8a0073c4cc0b15cc73012b8e88b
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1.9 GB | Preview Download |
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md5:5a0284894b6098d7510950049987e53c
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1.9 GB | Preview Download |
Additional details
Related works
- Is derived from
- Software: 10.5281/zenodo.11073061 (DOI)
Funding
Software
- Repository URL
- https://github.com/norwegian-geotechnical-institute/geoinquire-va2-34-2
- Programming language
- Python
- Development Status
- Active
References
- Copernicus Climate Change Service, Climate Data Store. (2019). Land cover classification gridded maps from 1992 to present derived from satellite observation [Dataset]. Copernicus Climate Change Service (C3S). https://doi.org/10.24381/cds.006f2c9a
- Defourny, P., Lamarche, C., Marissiaux, Q., Brockmann, C., Martin, B., & Kirches, G. (2021). Product User Guide and Specification: ICDR Land Cover 2016–2020 [Dataset]. Copernicus Climate Change Service (C3S).
- Frieler, K., Lange, S., Piontek, F., Reyer, C. P. O., Schewe, J., Warszawski, L., Zhao, F., Chini, L., Denvil, S., Emanuel, K., Geiger, T., Halladay, K., Hurtt, G., Mengel, M., Murakami, D., Ostberg, S., Popp, A., Riva, R., Stevanovic, M., ... Yamagata, Y. (2017). Assessing the impacts of 1.5 °C global warming: Simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b). Geoscientific Model Development, 10(12), 4321–4345. https://doi.org/10.5194/gmd-10-4321-2017
- Hartmann, J., & Moosdorf, N. (2012). The new global lithological map database GLiM: A representation of rock properties at the Earth surface. Geochemistry, Geophysics, Geosystems, 13. https://doi.org/10.1029/2012GC004370
- Lange, S. (2019). WFDE5 over land merged with ERA5 over the ocean (W5E5) (Version 1.0) [Dataset]. GFZ Data Services. https://doi.org/10.5880/pik.2019.023
- Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G. H., & Pavelsky, T. M. (2019). MERIT Hydro: A high-resolution global hydrography map based on latest topography datasets. Water Resources Research, 55, 5053–5073. https://doi.org/10.1029/2019WR024873