New NASA Model Finds Landslide Threats in Near Real-Time During Heavy Rains

NASA scientists have developed a model to predict landslides globally using real-time data. The Landslide Hazard Assessment for Situational Awareness model estimates landslide risks every 30 minutes by analyzing rainfall and susceptibility factors like steep slopes and deforestation. Historical data reveals patterns, such as peak landslide activity in Peru and Taiwan. The model also identifies unreported landslide-prone regions, aiding in disaster response and mitigation efforts.

What is the primary purpose of the Landslide Hazard Assessment for Situational Awareness model developed by NASA?

• To predict weather patterns globally

• To estimate landslide risks in real-time

• To track deforestation rates

• To monitor volcanic activity

Which factor is most commonly used by the model to trigger landslide predictions?

• Road construction

• Global precipitation data

• Deforestation rates

• Earthquake activity

What does the susceptibility map highlight in the context of landslide predictions?

• Regions with abundant wildlife

• Zones with high volcanic activity

• Areas with steep slopes and weak bedrock

• Regions with high population density

In which months does landslide activity peak in Peru according to the model?

• July to September

• February to April

• October to December

• May to June

How does the model contribute to disaster response and mitigation?

• By tracking urban development

• By monitoring wildlife migration

• By creating a global picture of landslide hazards

• By predicting volcanic eruptions