Articles | Volume 12, issue 4
https://doi.org/10.5194/essd-12-2899-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-12-2899-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Constructing a complete landslide inventory dataset for the 2018 monsoon disaster in Kerala, India, for land use change analysis
Lina Hao
CORRESPONDING AUTHOR
State Key Laboratory of Geohazard Prevention and Geoenvironment
Protection, Faculty of Earth Sciences, Chengdu University of Technology,
Chengdu, China
Faculty of Geoinformation Science and Earth Observation (ITC),
University of Twente, Enschede, the Netherlands
Rajaneesh A.
Department of Geology, University of Kerala, Thiruvananthapuram
695581, Kerala, India
Faculty of Geoinformation Science and Earth Observation (ITC),
University of Twente, Enschede, the Netherlands
Sajinkumar K. S.
Department of Geology, University of Kerala, Thiruvananthapuram
695581, Kerala, India
Department of Geological & Mining Engineering & Sciences,
Michigan Technological University, Houghton, MI, USA
Tapas Ranjan Martha
National Remote Sensing Centre, Indian Space Research Organisation,
Hyderabad, India
Pankaj Jaiswal
Geological Survey of India (GSI), Ranchi, India
Brian G. McAdoo
Environmental Studies, Yale-NUS College, Singapore
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We propose a modeling approach capable of recognizing slopes that may generate landslides, as well as how large these mass movements may be. This protocol is implemented, tested, and validated with data that change in both space and time via an Ensemble Neural Network architecture.
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Rainfall intensity–duration (ID) thresholds can aid in the prediction of natural hazards. Large-scale sediment disasters like landslides, debris flows, and flash floods happen frequently in the Himalayas because of their propensity for intense precipitation events. We provide a new framework that combines the Weather Research and Forecasting (WRF) model with a regionally distributed numerical model for debris flows to analyse and predict intense rainfall-induced landslides in the Himalayas.
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Natural hazards such as earthquakes, landslides, and flooding do not always occur as stand-alone events. After the 2008 Wenchuan earthquake, a co-seismic landslide blocked a stream in Hongchun. Two years later, a debris flow breached the material, blocked the Min River, and resulted in flooding of a small town. We developed a multi-process model that captures the full cascade. Despite input and process uncertainties, probability of flooding was high due to topography and trigger intensities.
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Landslides, debris flows and other types of dense gravity-driven flows threaten livelihoods around the globe. Understanding the mechanics of these flows can be crucial for predicting their behaviour and reducing disaster risk. Numerical models assume that the solids and fluids of the flow are unstructured. The newly presented model captures the internal structure during movement. This important step can lead to more accurate predictions of landslide movement.
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Short summary
Kerala in India was subjected to an extreme rainfall event in the monsoon season of 2018 which triggered extensive floods and landslides. In order to study whether the landslides were related to recent land use changes, we generated an accurate and almost complete landslide inventory based on two existing datasets and the detailed interpretation of images from the Google Earth platform. The final dataset contains 4728 landslides with attributes of land use in 2010 and land use in 2018.
Kerala in India was subjected to an extreme rainfall event in the monsoon season of 2018 which...
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