1 00:00:00,000 --> 00:00:02,770 Deadly landslides can happen in the space of minutes, 2 00:00:02,790 --> 00:00:06,310 but factors that cause landslides can be detected ahead of time 3 00:00:06,330 --> 00:00:08,410 and from space. 4 00:00:08,430 --> 00:00:11,560 With satellites, NASA scientists have developed a new model 5 00:00:11,580 --> 00:00:17,670 to estimate where and when landslides may strike around the world using real-time information. 6 00:00:17,690 --> 00:00:21,940 The model, known as Landslide Hazard Assessment for Situational Awareness, 7 00:00:21,960 --> 00:00:26,840 estimates which regions have a moderate or high chance of landslides every 30 minutes. 8 00:00:26,860 --> 00:00:30,920 For the first time, potential landslide activity can be seen globally. 9 00:00:30,940 --> 00:00:34,580 These regions are identified by several factors. 10 00:00:34,600 --> 00:00:39,100 First, the model uses the Global Precipitation Measurement Mission to track rainfall 11 00:00:39,120 --> 00:00:43,000 - the most widespread and frequent trigger of landslides worldwide. 12 00:00:43,020 --> 00:00:46,330 Then the model evaluates which areas with high rainfall 13 00:00:46,350 --> 00:00:49,860 are also prone to landslides using a susceptibility map. 14 00:00:49,880 --> 00:00:53,370 The regions highlighted in this map may have a combination of 15 00:00:53,390 --> 00:00:57,230 steep slopes, deforestation, a weak bedrock, road construction 16 00:00:57,250 --> 00:00:59,680 or are near Earthquake fault zones 17 00:00:59,700 --> 00:01:03,530 - factors that make land more prone to landslides in heavy rains. 18 00:01:03,550 --> 00:01:06,250 Scientists ran the model looking back 15 years 19 00:01:06,270 --> 00:01:11,120 to determine when and where potential landslide activity tends to happen around the world, 20 00:01:11,140 --> 00:01:14,750 or in essence when landslide season exists in different regions. 21 00:01:14,770 --> 00:01:18,760 When this model is compared to NASA’s database of landslide reports 22 00:01:18,780 --> 00:01:20,740 dating back to 2007, 23 00:01:20,760 --> 00:01:23,370 similar patterns emerge. 24 00:01:23,390 --> 00:01:28,530 For example, potential landslide activity peaks from February to April in Peru. 25 00:01:28,550 --> 00:01:33,340 Whereas in Taiwan the peak occurs in May and June. 26 00:01:33,360 --> 00:01:36,280 But not every landslide is seen or reported. 27 00:01:36,300 --> 00:01:43,110 The model also reveals landslide-prone regions that currently don’t have any reported fatalities in the database. 28 00:01:43,130 --> 00:01:46,650 Scientists will use the NASA model in combination with landslide reports 29 00:01:46,670 --> 00:01:50,490 to improve our understanding of where and when landslides may occur. 30 00:01:50,510 --> 00:01:55,940 Creating a global picture on this pervasive hazard will not only help vulnerable populations, 31 00:01:55,960 --> 00:02:00,540 but better inform disaster response and mitigation. 32 00:02:00,560 --> 00:02:07,641