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| Type: | Resource | |
| Storage: | The size of this resource is 895.9 KB | |
| Created: | Dec 25, 2020 at 9:42 p.m. | |
| Last updated: | Dec 25, 2020 at 10:05 p.m. (Metadata update) | |
| Published date: | Dec 25, 2020 at 10:05 p.m. | |
| DOI: | 10.4211/hs.52e1acac40ba4ffa8ec2d1899bfc5dec | |
| Citation: | See how to cite this resource | |
| Content types: | Geographic Feature Content Geographic Raster Content |
| Sharing Status: | Published |
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| Views: | 1226 |
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Abstract
Accurate rainfall estimates are required to predict when and where rain-triggered landslides will occur. In regions with sparse region gauge networks, satellite rainfall products, owing to their easy availability, high temporal resolution, and improved spatial variability, could be used as an alternative. This study compares the utility of rain gauge and satellite rainfall data for assessing landslide distribution in a data-sparse region: Idukki, along the Western Ghats, India. The GPM IMERG-L (Global Precipitation Mission Integrated Multi-satellitE Retrievals for GPM – Late) daily rainfall product was compared with rain gauge measurements, and it was found that the satellite rainfall observations were underpredicting the rainfall. A conditional merging algorithm was applied to the GPM data to develop a product that combines rain gauge measures' accuracy and the satellite data's spatial variability. A comparison of the ability of the data products to capture the spatial spread of landslides was then carried out. The study area was divided into zones of influences corresponding to the rain gauge stations, and the landslides were classified according to their location within each zone. 5-day antecedent rainfall values were computed from both the rainfall products. Relying solely on the rain gauge derived values created many false positives and false negatives in landslide prediction. A total of 10.2% of the landslides fell in the true-positive category, while 51.3% was the overall false-negative rate. The study proposes using satellite products with improved spatial resolution and a denser rain gauge network to have reliable inputs for landslide prediction models.
Subject Keywords
Coverage
Spatial
Content
Data Services
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People or Organizations that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.
| Name | Organization | Address | Phone | Author Identifiers |
|---|---|---|---|---|
| Thomas Oommen | Michigan Technological University | |||
| Snehamoy Chatterjee | Michigan Technological University | |||
| Sajinkumar K.S | University of Kerala |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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