1 00:00:00,020 --> 00:00:04,070 [Music, rain] 2 00:00:04,090 --> 00:00:08,100 [rain] Dalia: GPM will help us to understand 3 00:00:08,120 --> 00:00:12,260 precipitation extremes. And this is everything from too much rainfall, such as 4 00:00:12,280 --> 00:00:16,430 flooding in India or Southeast Asia, to too little rainfall 5 00:00:16,450 --> 00:00:20,520 such as drought in the U.S. Southwest. 6 00:00:20,540 --> 00:00:24,670 [music] 7 00:00:24,690 --> 00:00:28,810 Eric: There's about one major flood a day 8 00:00:28,830 --> 00:00:32,920 someplace in the world, so it's not as if it's a rare event. 9 00:00:32,940 --> 00:00:33,040 [rain falling, thunder] 10 00:00:33,060 --> 00:00:37,100 [rain falling, thunder] Big problem is that over much of the world 11 00:00:37,120 --> 00:00:41,190 the in situ data, the gauges, and the measured 12 00:00:41,210 --> 00:00:45,250 precipitation just isn't available. 13 00:00:45,270 --> 00:00:49,300 To predict floods you need to have the data in near real-time. 14 00:00:49,320 --> 00:00:53,320 And so the satellites are 15 00:00:53,340 --> 00:00:57,510 about the only way--GPM is about the only way-- 16 00:00:57,530 --> 00:01:01,570 that this is going to happen. And so we're going to use GPM 17 00:01:01,590 --> 00:01:05,720 rainfall retrievals to go do analyses, do flood forecasting, 18 00:01:05,740 --> 00:01:09,890 and bring climate services, 19 00:01:09,910 --> 00:01:14,050 bring information, to users in these areas. 20 00:01:14,070 --> 00:01:18,240 [music] Dalia: Landslides happen all over the world 21 00:01:18,260 --> 00:01:22,340 in nearly every country, and they cause more economic damage and more fatalities than 22 00:01:22,360 --> 00:01:26,490 people generally think. [rocks falling] The large 23 00:01:26,510 --> 00:01:30,590 majority of landslides around the world are triggered by intense or prolonged rainfall. 24 00:01:30,610 --> 00:01:34,640 [rain falling] A landslide is a general 25 00:01:34,660 --> 00:01:38,690 term, often used for mudslides, debris flows, rock falls, 26 00:01:38,710 --> 00:01:42,720 and usually it's just a mass of rock, earth, and dirt 27 00:01:42,740 --> 00:01:46,770 basically moving down a hillslope. Typical 28 00:01:46,790 --> 00:01:50,870 landslide studies are done at the local scale and they use gauge data. Now this is a problem 29 00:01:50,890 --> 00:01:55,050 in areas of topography where we don't have gauges or radar, in particular 30 00:01:55,070 --> 00:01:59,220 in developing areas where we don't have any information. So satellite data 31 00:01:59,240 --> 00:02:03,410 is really important for understanding where and when this intense rainfall might happen 32 00:02:03,430 --> 00:02:07,570 that could trigger landslides. 33 00:02:07,590 --> 00:02:11,660 [music] 34 00:02:11,680 --> 00:02:15,790 [music] 35 00:02:15,810 --> 00:02:19,940 [music] Tom: In the western U.S. 36 00:02:19,960 --> 00:02:23,960 we deal with drought on a regular basis. It tends to be cyclic. 37 00:02:23,980 --> 00:02:28,050 We'll get two or three dry years and we'll get a few wet years. If somebody 38 00:02:28,070 --> 00:02:32,140 could predict when the dry ones are coming, we'd be a lot better off. 39 00:02:32,160 --> 00:02:36,190 A lot of our water here comes in snow 40 00:02:36,210 --> 00:02:40,240 It accumulates up in the mountains in the wintertime, runs off 41 00:02:40,260 --> 00:02:44,410 in the spring, and that's we use for irrigation in the western U.S. 42 00:02:44,430 --> 00:02:48,600 Wade: Agricultural drought is defined as a lack of 43 00:02:48,620 --> 00:02:52,770 water within the top meter of soil 44 00:02:52,790 --> 00:02:56,940 for adequate crop functionalities, adequate 45 00:02:56,960 --> 00:03:01,120 crop productivity. 46 00:03:01,140 --> 00:03:05,280 And if you're talking about agricultural drought, 47 00:03:05,300 --> 00:03:09,470 probably the biggest error source is the quality of 48 00:03:09,490 --> 00:03:13,650 the precipitation information that you have available. If you have good precipitation 49 00:03:13,670 --> 00:03:17,810 information, you can do a very good job of characterizing drought and often its 50 00:03:17,830 --> 00:03:21,940 subsequent impact on agricultural productivity. 51 00:03:21,960 --> 00:03:26,080 Tom: We do work in research in 52 00:03:26,100 --> 00:03:30,200 determining the water needs of crops and what the impacts on crops are 53 00:03:30,220 --> 00:03:34,290 if you don't have enough water. We're doing this because we 54 00:03:34,310 --> 00:03:38,380 realize that in the western U.S. there will likely be less water available 55 00:03:38,400 --> 00:03:42,440 in the future than there has been in the past, and the farmers need to know how 56 00:03:42,460 --> 00:03:46,490 to respond to that decreasing water supply. 57 00:03:46,510 --> 00:03:50,530 Certainly when we're looking nationwide, the better prediction we have of how much 58 00:03:50,550 --> 00:03:54,600 rain we've been getting and how much is likely to come in the near future 59 00:03:54,620 --> 00:03:58,770 is very, very important. To the extent that we can predict that with satellites, 60 00:03:58,790 --> 00:04:02,800 it's really beneficial. 61 00:04:02,820 --> 00:04:06,930 This isn't just a U.S. problem; it's a global problem. 62 00:04:06,950 --> 00:04:11,010 Many countries of the world are facing the same kind of issues 63 00:04:11,030 --> 00:04:15,070 that we are. And so we expect this information to be able to be used 64 00:04:15,090 --> 00:04:19,190 in the east and the western U.S. and globally. Dalia: We 65 00:04:19,210 --> 00:04:23,260 need accurate and timely rainfall information to understand disasters like 66 00:04:23,280 --> 00:04:27,330 floods, droughts, and landslides. GPM's global 67 00:04:27,350 --> 00:04:31,380 rainfall data will help us to better understand and model these types of disasters 68 00:04:31,400 --> 00:04:35,400 around the world. [Music, whoosh] 69 00:04:35,420 --> 00:04:39,550 [Music] 70 00:04:39,570 --> 00:04:43,580 [Music] 71 00:04:43,600 --> 00:04:43,997