WEBVTT FILE 1 00:00:03.010 --> 00:00:06.850 You know when we talk about the water cycle, particularly when you teach the water cycle, 2 00:00:06.850 --> 00:00:11.830 say, in school, you think of that diagram and it has the clouds dropping snow onto the 3 00:00:11.830 --> 00:00:17.010 mountains and the water flowing off into the rivers and going into a lake maybe and then 4 00:00:17.010 --> 00:00:24.170 into the ocean. And in many depictions, humans are absent. We don't see that actually there's 5 00:00:24.170 --> 00:00:29.800 a giant dam next to that lake, and that dam actually controls how much water is flowing 6 00:00:29.800 --> 00:00:35.850 into the ocean. So in this study we used data from NASA's new ICESat-2 satellite, which launched 7 00:00:35.850 --> 00:00:41.149 in October of 2018, to try to better understand how surface water varies around the world. 8 00:00:41.149 --> 00:00:45.300 And so we intersect data from the satellite, which tells us the water levels. So how much 9 00:00:45.300 --> 00:00:50.350 water is in the lake essentially, with a global dataset of where surface water bodies are, 10 00:00:50.350 --> 00:00:54.949 and which ones are reservoirs and which ones are not. Try to better understand how natural water 11 00:00:54.949 --> 00:00:59.929 bodies and how reservoirs vary seasonally and to be able to understand the impact of 12 00:00:59.929 --> 00:01:06.180 human management on total seasonal variability of water storage. In my view this study represents 13 00:01:06.180 --> 00:01:13.430 kind of a first quantification of the impact that humans have on the surface water storage component 14 00:01:13.430 --> 00:01:17.851 of the water cycle. No one has ever been able to quantify this value before. And what we 15 00:01:17.851 --> 00:01:22.230 find is that reservoirs are significantly more variable. So the amount of water stored 16 00:01:22.230 --> 00:01:26.910 in them varies a lot more than natural water bodies. And in particular they're responsible 17 00:01:26.910 --> 00:01:32.580 for 57 percent of the total seasonal water storage variability on Earth. So in other 18 00:01:32.580 --> 00:01:36.700 words, human management. So you know, humans modulating the water levels in reservoirs 19 00:01:36.700 --> 00:01:42.780 is responsible for the majority of surface water variability on Earth. Even though reservoirs 20 00:01:42.780 --> 00:01:48.110 actually account for a very small percentage, only about 3.9% of the total water bodies 21 00:01:48.110 --> 00:01:54.410 that we observed. And the reason for this is that ICESat-2 is a laser altimeter, which 22 00:01:54.410 --> 00:02:00.050 means that it provides very high resolution, really highly accurate observations of surface 23 00:02:00.050 --> 00:02:05.700 water level. This means that we can now observe the height of very small water bodies, as 24 00:02:05.700 --> 00:02:11.180 small as several hundred or a thousand square meters. And so in doing this now we can look 25 00:02:11.180 --> 00:02:15.249 at hundreds of thousands of water bodies instead of a couple hundred water bodies, and also 26 00:02:15.249 --> 00:02:20.670 we know that those observations of water level are quite accurate. In the future, as we have, 27 00:02:20.670 --> 00:02:25.519 you know, increasing population, we have growing development around the world and growing industrialization, 28 00:02:25.519 --> 00:02:28.940 we have growing demands on water through agriculture and we also have climate change, which is 29 00:02:28.940 --> 00:02:32.950 changing the way water is moving around the world. So when you have all those factors, 30 00:02:32.950 --> 00:02:35.940 it's clear that there's going to be greater demands on water usage in the future. And 31 00:02:35.940 --> 00:02:40.111 so a study like this can tell us, okay, here is how water is currently being managed. Here 32 00:02:40.111 --> 00:02:42.509 is how humans are currently controlling the water cycle.