Articles | Volume 20, issue 12
https://doi.org/10.5194/nhess-20-3413-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-20-3413-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
New global characterisation of landslide exposure
Robert Emberson
CORRESPONDING AUTHOR
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD 20771, USA
Universities Space Research Association, Columbia, MD 21046, USA
Goddard Earth Sciences Technology and Research, Columbia, MD 21046, USA
Dalia Kirschbaum
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD 20771, USA
Thomas Stanley
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD 20771, USA
Universities Space Research Association, Columbia, MD 21046, USA
Goddard Earth Sciences Technology and Research, Columbia, MD 21046, USA
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- Constraining landslide timing in a data-scarce context: from recent to very old processes in the tropical environment of the North Tanganyika-Kivu Rift region O. Dewitte et al. 10.1007/s10346-020-01452-0
- Landslide vulnerability mapping using multi-criteria decision-making approaches: in Gacho Babba District, Gamo Highlands Southern Ethiopia L. Tadesse et al. 10.1007/s42452-024-05693-9
51 citations as recorded by crossref.
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- Historical dynamics of landslide risk from population and forest-cover changes in the Kivu Rift A. Depicker et al. 10.1038/s41893-021-00757-9
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- Assessing Global Landslide Casualty Risk Under Moderate Climate Change Based on Multiple GCM Projections X. Wang et al. 10.1007/s13753-023-00514-w
- Convolutional neural networks prediction of the factor of safety of random layered slopes by the strength reduction method E. Soranzo et al. 10.1007/s11440-022-01783-3
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- Investigation of Model Uncertainty in Rainfall-Induced Landslide Prediction under Changing Climate Conditions Y. Chen et al. 10.3390/land12091732
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- Risk Assessment of Landslide Casualty under Incomplete Information——Tienshan and Kunlun Mountainous Regions of Central Asia X. Chen et al. 10.1016/j.ijdrr.2024.105057
- Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine A. Handwerger et al. 10.5194/nhess-22-753-2022
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- Mass Movement Hazard and Exposure in the Himalaya S. Dubey et al. 10.1029/2022EF003253
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- Characteristics and causes of natural and human-induced landslides in a tropical mountainous region: the rift flank west of Lake Kivu (Democratic Republic of the Congo) J. Maki Mateso et al. 10.5194/nhess-23-643-2023
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2 citations as recorded by crossref.
- Constraining landslide timing in a data-scarce context: from recent to very old processes in the tropical environment of the North Tanganyika-Kivu Rift region O. Dewitte et al. 10.1007/s10346-020-01452-0
- Landslide vulnerability mapping using multi-criteria decision-making approaches: in Gacho Babba District, Gamo Highlands Southern Ethiopia L. Tadesse et al. 10.1007/s42452-024-05693-9
Latest update: 13 Dec 2024
Short summary
Landslides cause thousands of fatalities and cost billions of dollars of damage worldwide every year, but different inventories of landslide events can have widely diverging completeness. This can lead to spatial biases in our understanding of the impacts. Here we use a globally homogeneous model of landslide hazard and exposure to provide consistent estimates of where landslides are most likely to cause damage to people, roads and other critical infrastructure at 1 km resolution.
Landslides cause thousands of fatalities and cost billions of dollars of damage worldwide every...
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