New NASA Model Finds Landslide Threats in Near Real-Time During Heavy Rains
Narration: Joy Ng
Deadly landslides can happen in the space of minutes, but factors that cause landslides can be detected ahead of time and from space.
With satellites, NASA scientists have developed a new model to estimate where and when landslides may strike around the world using real-time information.
The model, known as Landslide Hazard Assessment for Situational Awareness, estimates which regions have a moderate or high chance of landslides every 30 minutes.
For the first time, potential landslide activity can be seen globally. These regions are identified by several factors.
First, the model uses the Global Precipitation Measurement Mission to track rainfall - the most widespread and frequent trigger of landslides worldwide.
Then the model evaluates which areas with high rainfall are also prone to landslides using a susceptibility map.
The regions highlighted in this map may have a combination of steep slopes, deforestation, a weak bedrock, road construction or are near Earthquake fault zones - factors that make land more prone to landslides in heavy rains.
Scientists ran the model looking back 15 years to determine when and where potential landslide activity tends to happen around the world, or in essence when landslide season exists in different regions.
When this model is compared to NASA’s database of landslide reports dating back to 2007, similar patterns emerge.
For example, potential landslide activity peaks from February to April in Peru.
Whereas in Taiwan the peak occurs in May and June.
But not every landslide is seen or reported. The model also reveals landslide-prone regions that currently don’t have any reported fatalities in the database.
Scientists will use the NASA model in combination with landslide reports to improve our understanding of where and when landslides may occur.
Creating a global picture on this pervasive hazard will not only help vulnerable populations, but better inform disaster response and mitigation.