NASA ARSET Overview and Applications of GLDAS Groundwater Data Products at Regional Scale Part 2

0:02 Speaker 1: Hello everyone, Welcome back to this training on monitoring groundwater changes for water resources management. This is part two of the training and the topic today is overview and applications of Global Lend Data Assimilation System groundwater data products at regional scale. This is a meta meta again and we're fortunate to have doctor Matheurodeal once again as our guest speaker today. Last week we had an overview analysis of NASA terrestrial water storage data from Grace and Grace follow on missions.

0:36 Speaker 1: Today we will focus on Gilda's groundwater data and next session will be on opera disc to monitor groundwater changes. As I mentioned last week, there will be one homework that will be posted on the training website on thirtieth of April and it will be due on fifteenth of May. A certificate of completion will be awarded to those who attend all live sessions and complete the homeburg assignment before the two date. Just a brief review of what we saw last week. We had an overview of GRACE missions and data products by doctor Rottel and he also talked about data applications to flood and rout We had a demonstration of Grace and GRACE fall on data Analysis tool or GRACE Interactive browser.

1:27 Speaker 1: Using that, we examine in terrestrial water storage for river basin Colorado River Basin, and we also looked at area of Ogallala aquifer and time series for that region and we saw that groundwater has strong multi year variability and there is a decreasing trend as well in this region. We went through an exercise that you must have worked on also. Now will start with today's session its overview and applications of Gilda's two point two its version two point two groundwater data products at regional scale.

2:10 Speaker 1: Our objectives for today are that by the end of this part, participants will be able to identify characteristics of groundwater data product from GRACE assimilated gildas system monitor inter annual to ticital changes in groundwater at regional scales using Gilda's data product. Our outline is that doctor Rottel is going to provide a review of key aspects of GRACE and GRACE. Follow on then overview of land data assimilation system about GRACE data assimilation in Elda's and groundwater data and then we will have a demonstration of how to analyze Gilda's based groundwater data, how to access it analyze it, visualize it using Giovanni.

3:00 Speaker 1: It's a web twol and we will also use jis to do some post analysis. Just a note about asking questions. Please put your questions in the questions box and we will address them at the end of the webinar. Feel free to enter your questions as we go and we will try to get to all the questions during the question and answer session. After the webinar, the remainder of the questions will be answered in the Q and a document which will be posted on the training website about a week after the training. With that, I want to introduce our speaker for today, doctor Matthew Roddel.

3:40 Speaker 1: Doctor Matthew Roddell is the Deputy Director of Earth Sciences for Hydrosphere, Biosphere and Geophysics, or HBG at NASA Gorder Space Blight Center. HBG comprises more than three hundred and fifty scientists and engineers within five laboratories involved in remote sensing and numerical modeling of land and ocean processes and related applications. Doctor Rodel has also served as Associate Deputy Director for HBG and as Chief of the Hydrological Sciences Lab. He is a member of the science teams for NASA's Grace follow On Mission and future Grace Continuity or Gray Sea Mission.

4:23 Speaker 1: He leads the Global Land Data Assimilation System and projects focused on monitoring groundwater storage changes, mapping and forecasting drought wetness, and detecting climate related variations in the watcycle. Doctor rodel is a past associate editor for the Journal of Hydrology and a current editor for the Journal of Hydrometology. He has also chaired the Hydrology program for the American Geophysical Union or AGU Fall meeting from twenty nine to twenty eleven, and led various national and international scientific working groups.

5:05 Speaker 1: He received a Presidential Early Career Award for Scientists and Engineers in two thousand and six, a NASAJSFC Earth Science Achievement Award in two thousand and seven, a Robert H. Goddard Award for Exceptional Achievement in Science in twenty eleven, and an Arthur S. Fleming Award for Outstanding Federal Service in the area of Basic Science in twenty fifteen. He was elected to the rank of AGU Fellow in twenty twenty two. Dotor Rottel has more than one hundred and sixty peer reviewed publications and appears on clariveet Web of Sciences twenty eighteen to twenty three lists of highly cited researchers.

5:48 Speaker 1: He holds a BS in Environmental science from the College of William and Mary and a PhD in Geological sciences from the University of Texas and Boston. With that wind night talk mate.

6:05 Speaker 2: Okay, thank you, Amita. So I'm Matt Rodell, and I'm going to be presenting on the GLEDS two point two groundwater product in which we assimilate data from the GRACE and GRACE follow on satellite missions. So in a nutshell, GLS two point two is one of the versions of the Global Land Data Simulation System. In this case, it's the land Information system running the Catchment Land Service Model. It's a global simulation excluding Antarctica and Greenland where there's permanent ice cover. The resolution is a quarter degree by quarter degree and it's about twenty five by twenty five kilometers.

6:46 Speaker 2: The time period of GAILEDS two point two is two thousand and three to present. For the input meteorological forcing, it uses the European Center for Media Weather Forecasts analysis data, which is actually proprietary. We get that through A through a special agreement with them, and in this case, JAILS two point two assimilates data from the Grace and Grace follow On satellite missions, which are trust your water storage anomalies.

7:18 Speaker 2: So I'm just going to go back, and you've heard from a meta about Grace and Grace follow On, but I just want to remind you of a few key aspects before we get into Jailed's two point two. So the big thing is that it's Grace and Grace follow On are unlike any other Earth observing satellite observations that we have because they're not looking downward at the Earth. It's actually two satellites, one following the other about two hundred kilometers apart, about five hundred kilometers initial altitude, and the distance between the satellites is continuously measured by a k Ban microwave ranging system and also a laser system on Grace follow On.

7:58 Speaker 2: It's measured down to a very high precision about the size of a red blood cell. This and we're talking about two and a chlometers, so it's a huge distance and a very fine measurement. And these measurements made every five seconds or so as the satellites orbit the Earth, and over the course of the month, there's enough information in this inner satellite range, along with the precise positioning of the satellites, for it to be put into a super computer model and come up with a new map of Earth's gravity field based on the information on the satellite orbits.

8:36 Speaker 2: And basically what's happening is if there's a mass anomaly at the land surface or the ocean. Over the ocean, that mass anomily will cause a difference in the gravity field which will affect the satellites as they pass over. So the example here shows a mountain range. There's more mass in the mountain range. That means as the two satellites approach, the first satellite will be pulled for a little bit, and then distance between the two satellites will increase as they sort of pass over. The second satellite will catch up and even get a little closer as the first satellite is kind of held back, and then things will even out afterward.

9:14 Speaker 2: So that's how they would detect a mountain range. But because the measurements are so precise, they can actually detect changes in the gravity field from month to month that are caused by changes in terrestrial water storage. What is trust water storage? You ask, well, treastor water storage is the sum of all the groundwater, so moisture, snow, surface water, vegetation of water storage, et cetera. And to visualize this, we have time series from the state of Illinois where they actually have measurements of all these quantities on the ground.

9:48 Speaker 2: So they will to measure groundwater and the somisture measurements and snow measurements, and we created a time series. Show to the top here where the blue is the groundwater, and then superposed on top of that is saw moisture and red, and then the white you can just barely see on top is the or snowater equivalent water storage. In fact, surface water in Illinois is small enough you wouldn't even see it on this time series on a relative based relative to the other components. So Grace is measuring the total treasher water storage.

10:22 Speaker 2: So that's sort of the top contour here. It does not measure the individual components. It can't break it down into these components the way we have with the time series that's shown here. And I also want to point out that you can see there's a seasonal cycle each year treasure water storage goes gets higher and lower just based on how much precipitation is falling and evaporation and runoff. But you can also see there's interannual variability. And the two yellow oval show the first one shows a major drought in the Midwest during nineteen eighty eight eighty nine, and the second one shows nineteen ninety three when there was ascid flooding in the upper Mississippi River basin.

11:05 Speaker 2: And the Grace observations look like the animation at the lower rate. And so there are these sort of large blobs of red where it's there's less water than than normal and blue where there's more water than normal. And if you sort of follow along with the months there, you can see that you can sort of see the seasonal cycle there where for example, in South America it's it's wetter in the boreal summer and and and drier around November, December, January. So these are the the monthly anomalies, but there's also this sort of inter annual variability on top of that.

11:47 Speaker 2: And I want to reiterate that Grace and Grace follow and measure all the trust water storage components. And we've we've taken a look at you know, where those different components have a greater or lesser impact. It turns out that on a monthly basis, so I'm moisture, it tends to dominate in temperate regions. Snow not surprisingly dominates in the high latitude and in alpied regions. Surface water is actually dominant in certain areas like the Amazon, and then groundwater storage is relatively important everywhere, but becomes even more important on longer time skills, particularly annual and multi annual time skills.

12:31 Speaker 2: But GRACE cannot separate these components, and that's one of the reasons why we have gel US two point two, which let me get into that. First, I want to talk about land data simulation systems in general. So obviously geld AS is a global land data simulation system. Land data simulation systems have land surface models as there as their main foundation. And these models are are complex computer models that simulate the processes at and below the land surface. Or they take as input the meteorological what we call forcings, so things like precipitation, downward, short wave, belong wave radiation, wind speed and that sort of thing, humidity.

13:19 Speaker 2: Those are the inputs to the model, and then they The models basically simulate what happens to that water and energy effort hits the land surface using systems of equations that describe our understanding of these these physical processes. They break down the Earth into into a grid, and then within each grid they often have subgrid heterogeneity where they model separately the different vegetation types. And then they also are have a vertical stratification, so they might model the surface so moisture and roots on saw moisture and the case.

13:59 Speaker 2: So the cash mad Landservice model, which we're gonna be talking about here the groundwater storage. The reason we have these data assimilation systems is largely for integration of data. So if you think about it, NASA has you know, something twenty something earth observing satellites in orbit making various types of measurements, but it's not always the measurement that we want. For example, if we were interested in snowater equivalent, which is basically the amount of water stored in the snow on the ground, we don't currently have a satellite observation for that.

14:39 Speaker 2: We can we can observe snow cover, but not but not the more important snowater equivalent. So what a landed is assimilation system would do. Is it uses inputs like precipitation, and we might do some intercomparison optical merging of the various precipitation products that are available. That's one of the inputs to the ld as. They are also I didn't mention it here, but there are also some static inputs things like soil types and vegetation types and that sort of thing. And then there are other forcings like short radiation.

15:17 Speaker 2: And then we can do data assimilation, which is where you take the model estimate of the the quantity you know, in this case snowater equivalent, and then you made have an observation of things like the snow cover and you use that observation to constrain the model. And neither of them are perfect. The model has its own imperfections because you know their errors and the forcing and errors and the way we simplify complex processes in the model. But you know, and the observation is not perfect either.

15:51 Speaker 2: But when you combine the two, taking into account their their error characteristics and their advantages this is vantages, you can come up with a best estimate and that's the point of data similation. And then finally we might have on the ground estimates we can use to evaluate the output of the ld ASS and use that to improve it and and tweak it to get better results.

16:21 Speaker 2: So let's get onto grace and GRACE follow on data a similation. And this is really what makes GILDS two point two different from the other geled as products. So again, the JAILS two point two uses the land information system with the cash Bent landsurface model and it runs a three dimensional ensemble and ensemble common smoother. And I'm not going to get into all the mathematics of this, but just to be simple about it, it's a way that you run an ensemble of of model runs over the course of a month where you perturb the forcing we need, adjusted the forcing a little bit to account for errors, and you come up with twenty different runs and then those are used to estimate the uncertainty on the model side.

17:17 Speaker 2: We also have an estimate of the uncertainty in our observation and we use those too uncertainty piece of uncertain information to sort of weight the model versus the observed estimate. And the smoother allows us to take a monthly Grace trust water storage anomaly observation and use it to produce results on a daily or sub daily basis. So if you're interested in learning more about data simulation, I encourage you to look at their reference here, and there are various other resources on data similation.

17:55 Speaker 2: So here are some results comparing in the up or left the trust to order Storge anomalies from the what we call the open loop, which is the model running without data similation. The upper right shows the Grace observations and you can see the difference between the two. There's a lot more of fine scale variability on the left and the model output, whereas the grace observations are pretty coarse. There they look very smooth out. The lower right shows the results when we assimilate the grace data into the model, and what we like to see here is sort of the best of both worlds.

18:32 Speaker 2: It looks a lot like the grace observations at the larger scale, but we have the finer scale variability from the model. And the reason the model is able to have that is because it has inputs like precipitation that are available with much finer scale. In fact, all the inputs are much finer scale than the grace observations, so we hope that the bottom right, which is what you would get with the Gail does two point two, is again sort of the best of both worlds. So we've done quite a bit of evaluation of the results.

19:08 Speaker 2: I'm going to show you a few different examples, this one from Kumar at All twenty sixteen, showing results if you look on the right of comparisons of the surface so moisture and roots and sol moisture output from the Grace data assimilating model with on the ground observations, and in this case we're looking at the correlation with the ground observations. And when you see warmer colors, that means the data similation results are better than at least in terms of correlation, they're better than the open loop or no data similation results from the model.

19:47 Speaker 2: So in most cases we're getting either grays which means no difference, or warmer colors. So that's that's what we want to see.

19:57 Speaker 2: Looking at the groundwater output in this paper by biling Lee at All in twenty nineteen, they actually looked at about four thousand wells and twenty two regions and basins around the world. You can see in the map here that they're not evenly distributed around the world. There are some areas where like southern India, where there are a lot of observations available. Similarly, the US is pretty well covered, but then large areas of the world where there's really no groundwater data available for comparison in these In these regions, you know, with the with these data would be very careful about which observations we used.

20:39 Speaker 2: We had some selection criteria that were very important in order for a proper evaluation. One is that we only used wells that were installed in unconfined okfurs, meaning in a water table open to the basically open to atmospheric pressure, as opposed to a confined oc forur, which has a confine layer, and it has much different sort of reasons for water storage changes observed in the well we want to have. We want to have wells that were not directly influenced by pumping or injections. And then we also looked for places where there was at least a five year data record and typically wanted something that was monthly or bi monthly in order to get a recentably good time series for comparison.

21:31 Speaker 2: So that actually cut out quite quite a number of wells that we would have otherwise been able to use. Further. In order to convert a depth to water and a well to a change in water storage that we can compare with the model output or with grace, we need to know the specific yield, and so we carefully went through and looked at the aqua types and an estimated specific yield for each of those to do that conversion.

21:58 Speaker 2: So here are some of the results for a few different regions. And so the upper left is the Toilkentans River basin, upper right is the Upper Mississippi River basin, and then the Uganda and the lower left. In each of these plots, they're red is the catchment landsforst model open loop output meaning no data similation. The blue line is the is the data similation output, and then the black dots are the mean observed groundwater storage anomalies. And in these three cases at least, you can see that that when we do the data simulation, the blue is much closer to the black dots, and that means that the data sublation is doing what it's supposed to be doing.

22:46 Speaker 2: On average around the world, the Great Data Simlation reduced the root means square error by thirty six on a regional and basin, river basins and regions around the world and increased the correlation by sixteen percent on average. However, I will point out that as you've seen all three cases here, the amplitude of the open loop is higher, so it was overestimating the amplitude of the groundwater variability. And and this is these are the types of cases where the grace data simlation performed very well.

23:24 Speaker 2: It didn't do as well in the opposite direction, where you know, for example, if the if the red line had been had a low too low amplitude, the grace data simlation didn't necessarily make it larger to meet the observations. Looking at this a different way, here are twenty two regions around the world shown in the in the table in the middle. Here for each of these regions, and the top we're showing the data simulation Rootming square error in blue and the open loop Rootming square error in orange, and then the same for the correlation and the bottom plot here and you can see the average statistics again on the upper right.

24:12 Speaker 2: So looking at this a little bit differently, we can look at these regional scale means and we can see that the comparing the DA and the open loop Routman square error and correlation changes, where the rootmy square error decreases on average by thirty six percent, the correlation increases by sixteen percent. If we look at point scale means, meaning all the groundwater wells individually, the rootmate square or decreases by ten percent correlation increases by twenty two percent. So either way we can see that around the world on average, the data simulation is doing what it's supposed to be doing and making the simulated output groundwater storage better match the observations.

25:01 Speaker 2: This is from another another publication by riger at All in twenty fifteen, looking at the GILDS two point two output prior to a major flood event in the Missouri River basin in twenty eleven, and what we see is again the open loop from the cash bt lansfers model. It underperforms the data similation results. If you look at the right the right most column in the table at the bottom. In comparison with the USGS groundwater anomalies for the Missouri Roof basin, the open loop had a correlation coficient at point five eight, whereas the data similation output at two point eighty six, so pretty significant increase in correlation and likewise a decrease Inman square error switching gears a little bit.

25:58 Speaker 2: I just wanted to talk about sort of how this this GLS two point two got started. Originally, we were looking at just the continental the United States to do the Great Data Similation, and we had a project where one of the goals was to create drought and wetness indicator maps that could then be used by the authors of the US Drought Monitor. So it was a similar sort of a configuration of the lands Information System running the catchment landsurface model. In this case, we used NLS two meteorological forcing and and we produced these maps that are basically the percentiles of wetness for each location.

26:45 Speaker 2: So what that means, and I'm looking at the on the lower right of the surface so moisture, rugelans, soysure and groundwater maps. What these percentiles mean is is, for example, if you're the fifth percentile, that means it's only been drier at that location and the same time of year five percent of the time in the long term record. Similarly, if it's at the ninety eighth percentile, it's only been wetter at that location in time of year two percent of the time. So we produce these maps in the same way we have in this example, the lower right is showing the grace dress or water storage anomalies in May twenty fourteen.

27:26 Speaker 2: It's a pretty coarse, pretty coarse scale. It's actually even coarser than you get the feeling for with this gridded product. We assimilate that into the model, and then the model is able to give us the nice looking maps on the lower right, and then compare that with the twentieth of May twenty fourteen US Drought Monitor in the upper right. You can see the comparison's pretty good in terms of the overall patterns. There's more information in the grace data simlation maps because they have different levels, whereas the drought monitors really supposed to represent sort of all levels uh and and also the drought monitor does not indicate, you know, wet periods.

28:14 Speaker 2: So since since about twenty eleven, we've been running running this and it's actually used as one of the inputs to the US Drought Monitor. The Drought monitor authors look at look at our products as well as many other things when they're drawing their weekly maps. And if you go to the National Drought Mitigation Center website if you have the If you have this this presentation, you can hover over Drought Mitigation Center in the bottom and you'll get actually great NASA GRACE dot U n L dot E d U. If you go there, you can you can choose some different options that the radio buttons to the top and so the left is the the US Shallow groundwater a drought indicator, and you can flip between that roottone so moisture and surface solar moisture.

29:06 Speaker 2: The middle is basically drought indicators Global drought indicators based on Gildas two point two. And the right is is an experimental forecast product for one, two and three months into the future. If we compare the US Drought monitor with with our basically GRACE d A drought and wetness indicators, we can look at the different drought categories and if you look at the lower right there you can see wetness percentile ranges for each drought category. And once plotted here is is in red the US drought monitor and in blue the the GRACE uh DA based drought indicators, and in the black dash line shows you where you want the average to be.

30:04 Speaker 2: This is This is the accumulation over the entire U S So, so it basically shows the percentage of area in the US versus time in each of these plots. And so where where you see a higher number, that means there's more drought you know around the US, and lower number means there's less drout And you can see that in general, especially if you look at the lower right all drought categories. You can see that the the grace based drought indicators are pretty well correlated with the US Drought Monitor.

30:38 Speaker 2: The other thing I'll notice is the US drought monitor numbers or or percentage areas tend to be a lot higher. That's actually a sort of a flaw in the US Drought Monitor. The dash black line is supposed to show the long term mean for each of these. So if you looked at again at the lower right the percentile ranges, for example Z zero percent, that implies that about ten percent of the US should be in drought at any given time. But look at the top left the top left plot. There you can see that the d zero drought category for the red line, which is the US drought monitors, is almost always above ten percent.

31:20 Speaker 2: So this is just sort of shows the tendency of the US Drought Monitor authors to sort of overestimate drought and they may have a little bit of I won't call it bias, but they've may able to feel a little bit of pressure to put different, you know, counties in drought because it affects the affects the distribution of disaster funding. So but overall, the you know, the important message here is that that our drought maps tend to line up pretty well with the the US Drought Monitor. This is one of my last slides and it's an animation of the US drought or or sorry, of the the Grace based drought indicators over the US.

32:08 Speaker 2: The left is the Rootston so moisture wetness percentile. On the right is the shallow brownwater wetness percentile. And uh, what's cool here, I think is is you can sort of see the the evolution of of of drought and pluvial or wet events and the routland so moisure which is in you know, basically direct communication with the atmosphere changes much more quickly. So if you have a you know, you might be in drought, but any a bit have big rainstorm come through, boom you go straight from red to blue.

32:40 Speaker 2: But on the right there the shallow groundwater. You can see that that because it's not in direct communication, because groundwater is a bigger store of water and it and it reacts and and evolves more and more slowly. You can see that these these droughts sort of take a while to evolve and and uh and take you longer to to dissipate than they do with their roots and so and much. Sure, so I think this is this is sort of fun to watch. And we've done this for the global scale as well, but it's kind of nice to see the defined scale granularity for the US.

33:15 Speaker 2: So let me summarize what I've talked to you about with the GRACE Data Similation and the GLEDUS two point two product. Grace and GRACE follow on are unique in that they observe changes in trust your water storage, including all forms of trust or water storage, which is not something that any other satellite system can do. We use land data similation systems to integrate data from various sources, which allows us to fill spatial and temporal gaps using algorithms that represent our knowledge of the physical processes in jailed US two point two.

33:56 Speaker 2: We assimilate the GRACE and GRACE follow on observation of trust water storage, and it allows us to basically downscale those trust water storage observations in space and in time, and also perform vertical disaggregation so we can separate the groundwater from the sow moisture and snow, et cetera. We've used the gilled ass two point two sow moisture and groundwater output and we evaluate those extensively and use them for producing drought products. But they do have some key limitations versus that the model does not simulate water management, so especially in areas where there might be intensive irrigate irrigation that's it's fed by groundwater pumping and you might have a long term decline in groundwater storage, or maybe there's a seasonal aspect to it.

34:49 Speaker 2: You know, when that's the growing season, you might have a big decline in groundwater storage because there's pumping in an agricultural region that's not simulated by the cash maint LANDSERCE model and and therefore well it is detected by the GRACE observations. If it's something that's happening with some fine scale variability, we're not able to effectively downscale the Grace and GRACE follow On observations over those sorts of those sorts of regions and that sort of case. The second issue is that the models not stimulate confined AWKWAD storage changes and those those could be an important component of the trust or orders trade sort of changes observed by Grace and Grace follow On in certain regions.

35:38 Speaker 2: And I'll just end by saying that these output are available on the Goddared Earth Sciences Data Information Services Center, and we can also get the drought and wedness indicator maps and forecasts from the National Drought Medication Center website. Thank you so much for listening, and I'm going to pass the mic back over to a meet and I'll be here for the question answer session.

36:05 Speaker 1: Thank you so much, Matt for your presentation and information about GRACE assimilated giled as groundwater storage data. Next, we are going to have a brief demonstration of access, analysis and visualization of giled as groundwater storage data using a web tool geomanny. We will also do some processing using qgis. First, we'll learn to create maps and time series of groundwater storage data.

36:35 Speaker 3: Using geovanny.

36:38 Speaker 1: We will look at groundwater storage differences over inter annual to interticketal time scale. The case study I've chosen is a small river basin, Plemat River Basin, and I'm going to share my screen with you to talk about the river.

36:59 Speaker 1: So Clemat River basin. It straddles California and Oregons or northern California southern Oregon, and it's a small river basin. If you recall last week we saw increase interactive browser. Major river basins are covered in there. If you're interested in looking at region of your own interests, you can use childas to do that, and that's what we are trying to see here. So this river there is groundwater issues, but also there are multiple dams on this river. It's used for water, is used for agriculture, flood controlled hydropower, recreation, fish and wildlife habitat, and also has tribal treaty rights.

37:47 Speaker 1: Just to see the basin, this is the basin that we will be focusing on, and for that, I'm going to start with Giovanni. To access Gilda's groundwater storage data. This is the Giovanni web page and you have this link in the presentation slide that you can access. One note is that you will have to register to NASA Earth Data to be able to download images or data using Giovanni. Just go through this page quickly. If you know what data you're interested in, which parameter, then you can just enter here and search by keyword.

38:26 Speaker 1: If you are just searching in general, you can go to observations, different disciplines, measurements platforms, so there are multiple options that you can explore. If you are just looking for different data sets. If you already know, you can just enter here and I'm going to do that.

38:46 Speaker 4: Gilled As sorry, and you can search.

39:04 Speaker 1: You will see two options. We want this data assimilated. Grease Assimilated goes from two thousand and three to January twenty twenty six. The daily data units are in millimeter and resolution is a quarter degree. Once you choose the parameter, you can pick analysis and visualization option, so select plot here. You have multiple options as well. You can make maps. You can make time series, difference of map and difference of time series. There are other you can compare and make correlation maps, scatter plots.

39:45 Speaker 1: There are hermular diagrams. You can make instagram zono mean and vertical cross sections from different data sets. If there are three D data sets. We are going to stick with time averaged map for now, and here's the time selection window from and two. So let's do one thing. We're going to first look at two different decades and average groundwater storage data for that. So I'm going to start with February first, two thousand and three when the data starts, and go to two thousand and thirteen December thirty first, so we look at first period, and then we'll go from twenty fourteen to twenty twenty five to see the difference between groundwater storage over Klamath River basin.

40:48 Speaker 1: This is the window that allows you to select region of your interest, and there are multiple ways to do that. You can enter longitude and altitude if you know exact box that you are interested in, or you can go to the map by clicking here and make a box here or any region that you're interested in and those code in its will appear here. Another way to do is to click here. And this has different shape files already available in Giovanni, so it's different countries, lakes and reservoirs, land only see only tribal lands, are there, US states, are there, World regions and so ezy world regions, and most importantly what we are going to look at is watersheds.

41:44 Speaker 1: Different river basins are given here. For Klemath River, which is a smaller river and not included in here, I have already looked at code in its and then I'm going to just enter those here. So these are longitudes. So it's west one twenty five to one twenty box and this is forty two forty three point five north. And if you click here, this will show you where the box is. So here between Oregon and California. Here's where Clemat River basin is. So that's what we're picking. And once you have picked all the options, all you have to do is just say plot data and it launches a workflow.

42:38 Speaker 1: So here I have already done that to save time. It takes depending on the data resolution, it may take a few seconds to maybe a minute or two. But I have already done that. And so this is the map over the region that we chose. And this particular map is from two thousand and three to twenty thirteen December, and you can see this is the color bar here there is red is lower groundwater storage and in the upper part of the basin there is more groundwater storage.

43:21 Speaker 3: As you can see.

43:23 Speaker 1: Once you have this map, what you can do is you can go back to data selection and I'm going to go to this window. It's the same map we got here. You can go back to data selection and keep the same. But now look at the next decade. And here I'm going to pick two thy fourteen January first, sorry first, two thousand and twenty five December thirty first. So this is next ticket from what we saw previously. And again you can say plot data and you will be you will see the map again. This map is given here.

44:17 Speaker 1: It starts in twenty fourteen, and you can see that. Of course, the pattern's pretty much the same magnitude. We will compare in QGIS in a couple of minutes. Next, what we want to do is look at time series of groundwater storage data. And for that, I'm going to pick the entire time period that data is available, so February first, and let's just go to.

44:47 Speaker 3: Twenty twenty five December.

44:50 Speaker 1: So here I'm going to go to select plot options and say time.

44:57 Speaker 3: Series area averaged.

44:59 Speaker 1: So this going to average groundwater storage data over that entire box, and it's going to give a time series and you can plot it and the workflow launches again to save time.

45:12 Speaker 3: I've already.

45:15 Speaker 1: Turn the time series analysis and then if you see this is the time series here, it's in millimeter groundwater storage from two thousand and three all the way to two thousand and twenty five December. Clearly you do see annual variations. These are daily data as we saw, so you can see annual variation, but you can also see that there is inter annual variation in groundwater storage. And what you can do is if you're interested in any particular year, you can go back to data selection and just picks it two thousand and five to two thousand and six and make a map or make a time series for that particular year, so you can do it year by year, and then you can have different maps for different years.

46:14 Speaker 1: So can go to results, and this is a quick way to see the time series. Next thing that we are going to see is how to download this. You can just go to download. You can save this file as net CDF for PNG image, or you can have a CSV file so you can then either go to Excel and plot it the way you like it, or you can then parse it by time different temporal domain. You can do all that with CSV as well for map. Also you can download data in various formats, either net, CDF data file or KMZ for Google Earth PNG just image as we saw on screen, and geotif something that you can work with either r GIS or QGIS.

47:06 Speaker 1: And I have already saved these files in geot format so we can look at them in QGIS, and have also downloaded CSV file for time series. So you will be following these steps for another river basin.

47:27 Speaker 3: So just keep note.

47:28 Speaker 1: That you will be downloading some of these data. And so now what I'm going to do is let's go back to data selection. And just we looked at map, we looked at time series. We know how to change time and how to change special domain. So now I'm going to close Giovanni and now go to qgis to see how groundwater storage changed between those two periods that we looked at. So let me share QGIS project with you.

48:02 Speaker 1: So this is the QGIS project. You need to install QGIS on your computer. There is a link in your presentation for that as well. You can follow steps and have QGIS on your computer.

48:17 Speaker 4: Once you have.

48:18 Speaker 1: Qgs you can click open and this window will open. You can go to project and open a new project. That is what I've done, and this is the new project. Now you can go to web here first and add quick map services.

48:37 Speaker 3: I'm going to use Google Roadmap.

48:43 Speaker 1: You can choose other options as well. And now you can follow the instruction in your exercise. If you do not have this plug in quick Map services, and you can go to plug in and install that particular plug in, and there's an instruction given to you for that, but you can follow that and you will be able to have a background map or base map on qgis like this. Now we can start adding the data layer we saved from geoanny. So for that, go to layer and first we're going to add those map or raster layers.

49:29 Speaker 1: So add layer and add raster layer. And here I have saved them in a Folderoda's geomanny and if you look at the names, this is twenty thirteen, so two thousand and three to twenty thirteen.

49:52 Speaker 3: And then this is the other ticket.

49:54 Speaker 1: You can highlight and shift and both and add both at the same time. Open and add, and now you can close this window and you can you can just use your computer mouse to zoom in, or you can use this to zoom in here and use this hand symbol to move your map. So, now these rasters are added.

50:31 Speaker 1: So once the rasters are added, we can change color symbology here.

50:37 Speaker 3: So go to.

50:40 Speaker 1: Control click and go to properties and in the properties select symbology. Here we're going to pick single band pseudo color. And these are the mean and max values for the groundwork storage for this particular raster. We can make it round number.

51:03 Speaker 3: If you like.

51:04 Speaker 1: Sorry about that. And then this is the color ramp. You can change different colors we are going to keep the same and here, instead of continuous, let's have equal interval and you can decide how many intervals or classes you want. I'm going to pick twenty one. One more thing is that you can select label precision. I'm going to say two, so that number of digits after the decimal point. And these are the groundwater storage intervals for different colors that we see. Once you do that, you can say okay, okay.

51:53 Speaker 1: One more thing is you can do legend setting here and instead of continuous, just turn that off so that we can see different intervals here. And now you can say okay, So now you can see that this raster has colors showing different groundwater storage range from four fifty to twelve fifty. Next, what you can do is click again and go to styles and copy style. And now go to the next raster layer, go to styles and paste the same style. So you have the same groundwater storage scale for both both the maps for you to compare.

52:44 Speaker 1: And now you can click and compare. We're also going to see the differences soon, but this is the way to do symbology and at the colors that you like. One more thing is that I have downloaded vector shape file for Klemath River basin and I'm going to display that. So this is the shape file. I'm going to pick through a layer and add vector layer and then open and add and so you can move the layers around. This is the Clemat River basin. You can again use properties and symbology to make it transparent transparent fiel and you can change.

53:38 Speaker 3: Line colors if you like.

53:39 Speaker 1: I'm just going to keep it the same for now. And here now you can see the boundary of the river. So here in the upper part of the basin you see more groundwater storage compared to the lower part, which is in California. You can also clip your rast or crop your raster so that it is within the river basin. And for that you can go to raster extraction and then say clip raster by mask layer. You can choose input layer. This is the twenty two thousand and three to thirteen and this is the mask layer, which is the shapefile of Clamath River basin.

54:26 Speaker 1: And you can keep

54:31 Speaker 1: everything else as default and then run and you will see that there is a raster that is cropped here. This is just within this of course, that is because of the resolution. There are some areas which are not there. You can interpot it and feel that up.

54:52 Speaker 3: But for now, this.

54:54 Speaker 1: Is good approximation for the river basin. You can you name this clipped us. You can control click and go to rename layer and click here again to add the.

55:08 Speaker 3: Name you like.

55:09 Speaker 1: I'm going to pick gildas groundwater Storage two thousand and three to thirteen. And you can use the same color style for this layer. Suppose a copy style and then it's the same style. So now you can see that. Let's turn these two layers off so that we can see the groundwater storage within this river basin. I have done the same processing with raster layer from twenty fourteen to twenty twenty five. So just to extract and here I picked twenty fourteen to twenty five raster river basin as mask. One more thing I've done is I set the file name here in clicked mask and then run and I have this raster here.

56:21 Speaker 1: Also I have added symbology to it the same using styles for copy and paste. So we have these two layers. Now you can see how groundwater is changing. And to do this calculation. To see the differences between them, go to raster and raster calculator. Here window will open with raster bands and these are the operators for calculations. I'm going to add a temporary name for difference file as g astiff and then now you can go here. When you click on the raster you're interested in. So this is twenty fourteen to twenty five.

57:10 Speaker 1: Double click and it appears here minus two thousand and three to thirteen. And now you can say, okay, now this raster has been calculated, and we want to make sure that it has the same CRS or cod reference system, so that is EPSG four three two six, And now you can see the raster here as you can see everywhere groundwater storage is negative.

57:42 Speaker 3: That means groundwater has decreased.

57:44 Speaker 1: Between two thousand and three to thirteen decades and then two thousand and fourteen to twenty five. You can symbolize it just as we did before. To see numbers better, you can go to symbology single band pseudo color. You can keep the same range and same here we can go to equal interval and maybe.

58:10 Speaker 3: Say tan for now and.

58:16 Speaker 1: Have proper numbers displayed in the color table and say okay, and now you can see that the differences are shown here. As you can see, there are some regions where it is yellow and red. These are the much larger differences. So in this area, in this area, and this so you can look at how ground or changed in different parts of the basin. So this concludes our demonstration of how to get data from geovanny and analyze the data in QGIS.

59:00 Speaker 3: You will be doing.

59:01 Speaker 1: Similar exercise with another river basin. I'm going to go back to the presentation window.

59:08 Speaker 3: Thank you.

59:11 Speaker 1: This brings us to the end of our demonstration of how to access and visualize child dust groundwater. You also have an exercise on the training website that you can download. You will be working on this exercise for your homework, so you can download and follow the steps. Next session will be on overview of observational products for end users from remote sensing, analysis or opera surface to displacement products or dispase. The product we'll be focusing on. The homework will be posted on the training web page on thirtieth of April and it will be due on fifteenth of May.

59:54 Speaker 1: Homework answers must be submitted via Google forms. A certificate of com will be awarded to those who attend all three live webinars and complete the homework assignment by the deadline.

1:00:07 Speaker 3: You will receive a.

1:00:08 Speaker 1: Certificate where email approximately two months after completion of the course.

1:00:15 Speaker 1: We thank doctor Rodel for his presentation on Gilda's Groundwater and also for his presentation on Grace missions last week. Yes the contact information that you have any questions, please contact us. Our set, website and YouTube links are given here for questions, comments, or to share how you have applied our trainings to your work or studies. Email NASA Arset at gmail dot com and join our quarterly newsletter. To stay up to date on our latest trainings. You can join our list serve at this email address and follow the steps, and thank you very much for attending this session.

1:00:57 Speaker 1: You have time to start with the exercise and then we will have question and answer session.

1:01:04 Speaker 3: Thank you WELLO welcome doctor Rutell.

1:01:20 Speaker 1: We are here in the question and answer session. I hope you all had some time to work on the exercise and have made some progress working with Giovanni downloading Gilda's data and you can process that in QGIS and save your results so that Homer questions can be answered based on that. And now we will start with question and answer session. We'll go through a few questions till the end of this will be in our time. So we're starting from question one, how does the assimilation work daily when Grace and Ways follow on data monthly?

1:02:04 Speaker 2: So first of all, hello everyone, can everyone hear me? I just want to make sure yes, yes, okay, great. So this is actually not an easy question to answer. We use something called an ensemble common smoother and and you can read about it in some of the some of the publications that we've that we've done on on Grace and Grace follow on data similation. But essentially it sort of mimics the observation type and and does a correction to the model, typically three times per month, and uh, you know, sort of based on the trajectory of the changes.

1:02:50 Speaker 2: So it looks at the difference between a the similation system looks a difference between the monthly average from the model and the monthly average from race really the month and month changes of each and then it makes a makes an adjustment to the model based on based on the difference and applying information on the uncertainty in both the model and the observation to do sort of a waiting between the two. So that's that's in a nutshell, but it's it's really quite a bit more complicated.

1:03:28 Speaker 3: Than that, Thanks matt Our.

1:03:32 Speaker 1: Next question is how well does roots and soil marsha from gild as represent seasonal soil motion dynamics in agricultural regions, considering differences between summer and winter crops and the impact of irrigation.

1:03:47 Speaker 2: So let me take the last part first. So, first of all, we do have an irrigation simulation routine in the Land Information System you know as the software behind giald ASK, but it hasn't been earned on in any of the the current jailed as versions, so JAILS two point two does not stimulate irrigation and therefore in irrigated regions, Yeah, I would definitely say that that there are going to be significant differences between you know, on the ground soil moisture where there's irrigation and what the model is is simulating.

1:04:24 Speaker 1: And uh.

1:04:27 Speaker 2: Yeah, summer in winter crops, so so yeah, there's multiple crop types, then then that's not going to be simulated in jailed as either. So that could have an effect, you know, but overall we are making use of precipitation data. They're basically observation based and and so the precipitation is going to have a large impact on the roots, on sol moisture, and so where there's where there's not irrigation, I think, you know, we do a region reasonable job of simulating the soil moisture, but but where there's irrigation and that that's it's going to be less than perfect by a lot.

1:05:10 Speaker 4: Yeah.

1:05:11 Speaker 1: Question four, is there a reference to look up the formula to convert well data using specific yields to compare with gield us.

1:05:23 Speaker 2: Well, the formula is very simple, but finding this specific yield data is that is the difficult part. So specific yield is going to be a number that ranges between about point oh two and point five, and it depends on on the type of the type of box for you know, basically the poorest media that the water is in. So something like a sand or gravel is going to have a high specific yield. It could be point you know, from put to five to point four. You know, it's a If it's a limestone ocher with huge you know, caverns in it, it could be closer to point five or even more.

1:06:00 Speaker 2: But uh, you know, if it's something like a clay or a sandstone and it's it's you know, not fractured or anything like that, then serpeific yield could be very low. So the formula that you ask about is is simply the the well, the water level, and the well multiplied by this specific yield, it converts to to to a storage value. So if you had a change of of ten centimeters of water and you're well, and you would a specific yield of point two five, then you multiply two point point two five by ten and you would have a two point five centimeter change in water storage.

1:06:46 Speaker 2: You directly compare with with the grace observation. But again spif gield is going to vary from place to place, so finding those stiff field values is difficult. You basically need to know something about the opera and make an assumption about this skild or you know there's actually been a well log and they've removed you know, when they tugg the well. You might be able to get more accurate number, but those are again hard to come by.

1:07:18 Speaker 3: Thanks Matt.

1:07:19 Speaker 1: Question five is does terrestrial water storage data also include information and water pollutants and does it vary based on differences in geographic location?

1:07:31 Speaker 2: Has no information on pollutants.

1:07:36 Speaker 1: Question six is when groundwater elevation drops, eventually stream based flow drops, then disappears and the stream runs dry. This has been observed in western and central Kansas. Has anyone looked at time series of stream flow data looking for gauges transitioning from perennial to intermittent flow regimes with base flow as an indicator of drought, climate change, or human impacts.

1:08:07 Speaker 2: I don't know the answer, and I don't mean to sound glib, but I would you know, to find the answer, I'd probably go to Google, scholar and and and look and see if if anyone's done anything like that, I don't know of it off the top of my head, but certainly people could have done that type of research. I'm not sure, thank you.

1:08:26 Speaker 1: When we calculate animal is in Jaileda's two point two, what years do we used for normalization?

1:08:37 Speaker 4: So?

1:08:37 Speaker 2: Uh, I think we've we've tried to align with the with the Grace years. Uh, you know the Grace baseline. However, so what we do is JELLS two point two is when we assimilate the Grace data, we have to basically align the model with the Grace and make an adjustment. So so for the we might take an averaging period of two thousand and two to twenty twenty. I don't remember the exact averaging period that we use exactly, to be honest. And we take the average and look at the anomalies relative to that baseline and then do the same thing with Grace.

1:09:22 Speaker 2: Use it, you know it could it would be the same baseline two to twenty twenty if that's what we chose. And then when we do the data simulation, we make an adjustment if the anomaly is off by one centimeter. Let's say that you know, we need to increase water storage by one centimeter or the model to better align with the Grace observation. We then add that one centimeter to whatever water storage value what was in the model, so the model doesn't have an anomaly. The model has an actual amount of water store in each soil layer and in its representation of the OKFUR and those numbers are also sort of arbitrary.

1:10:08 Speaker 2: I mean, they're not going to align with with the real world. But but that's how we make the adjustment is with the data simulation, as we look at the difference between the anomalies Grace and and and and the model, and then we add whatever adjustment we're making. We actually call that an increment. We add that increment to to the model. States. Yeah, so I'm not I didn't. I sort of danced around your question there. What years use for normalization? You could? You could basically use whatever years you wanted to if you wanted to, you know, average over a certain time period and and calculate the baseline you know that mean as a baseline and then subtract it to get to get anomalies.

1:10:55 Speaker 4: Mhmm, thanks Matt.

1:10:58 Speaker 1: Next question eight is if we more monthly estimates of soil moisture changes from the for the conus that include conditioning on remote sense soil moisture should be used gelled as two point two at a version of nal das.

1:11:17 Speaker 2: So I'm trying to read the question and it keeps moving around here.

1:11:21 Speaker 1: Question eight.

1:11:22 Speaker 2: Yeah, uh, well, you know, if you're if you're interested in the conus and and remotely sense so moisture. Uh yeah, this is diff difficult to answer. It depends on what remote sensing you're talking about. If you're you know, you're talking about the Grace remote sensing, then you'd probably want to use the Gilds two point two. But if you're talking about you know, uh snap so moisture, you know, the n old ASS has a higher resolution and somewhat better input data. So general, and people are interested in in the conus and they're not interested in the groundwater more in the so much or I'd probably point them to analtass.

1:12:19 Speaker 2: But they both have their own advantages and disadvantages.

1:12:26 Speaker 1: Great, thank you. Question nine. I'm looking to analyze drout patterns across southern Alberta and would like to compare grace based drout indicators with existing roundwater well records in the province. Are there any guidelines you can suggest for the best types of wells to use for such an exercise? Do I need to select between active and inactive wells for such analysis?

1:12:55 Speaker 2: Yeah, the the things that we look for in wells to here with GRACE data or number one, it should be a well that's in an unconfined oct FORER. So a lot of wells go deep and they're they're installed in confined octfers and those are not appropriate for comparison with GRACE observations. Secondly, you want to have a long you know, the longer the data record, the better you know. If it's you mentioned inactive wells, I guess you mean a well that's not observations aren't being made anymore, and if you know, if there were, you know, if there's a long time series of observations that happens to end in twenty nineteen or something like that, you could still compare with the older GRACE data that would be that would be fine, But you know, you'd really like to have, you know, if you're interested in in current conditions, and clearly you want to have a well that's still actively monitored.

1:13:56 Speaker 2: And you also, you know, depending on whether you're interested in, uh, you know, the seasonal cycle and short term variability versus you know, just long term trends. Then that sort of helps to determine whether we need you know, is one observation per year enough or do you want to have you know, at least seasonal observations and that sort of thing. But and then the final thing I'll mention is you don't want to use measurements for well that's actively pumped or right next to you know, a municipal water supply where they're doing a lot of pumping, or agricultural area where they're pumping a lot of water for irrigation, that's going to have you know, variability that's determined you know, by the pumping and not so much represent the larger scale ambient ambient you know, groundwater conditions.

1:14:51 Speaker 3: Thank you.

1:14:52 Speaker 1: On the next question is it's about exercise, and I think we've gone through that. So I'm going to question to I am unsure if I understood the reach of gildas Is it a model that incorporates different data sets? Are the outputs only for the US territory? If that is the case, can we modify code? So no, I think we answered that question Matt, if you want to look at it, that GILL does is a global landed assimilation system, incorporate different data sets and so data are available globally. You want to add anything, we can.

1:15:31 Speaker 2: No, that's you're correct, it was already. Yeah, GILS runs globally already so that you don't need to modify the code for other areas. And it does incorporate multiple different.

1:15:45 Speaker 1: Question eleven will skip that's about Gianni and q jis. The next one is question twelve. As leakes don't really flow, is it easy to capture its decrease in volume?

1:16:01 Speaker 2: Well, uh, if you're referring to based on a Grace observation, you know, first of all, most lakes, unless you're talking about the Great Lakes, are are too small to have a discernible effect on on the Grace observations. If it's a group of lakes, you know, like in parts of Africa where there you know, you know, several lakes close to each other in southeastern Africa, those those effects can be seen in the GRACE data for GLS two point two. We don't actually model lakes explicitly. Uh. And you know, as regards to your question, lakes don't really flow, they don't, you know, somebody doesn't have to flow for it to have a change in water storage.

1:16:48 Speaker 2: So if the lake level is going up and down, you know, like a like a reservoir level going up and down, that you know, that translate to a change in mass that is detectable grace theoretically, if it's a large enough change in mass, great, thank you.

1:17:06 Speaker 1: We're going to question fourteen. What is the latency of chilled US two point two can it be used for early morning.

1:17:16 Speaker 2: Jailled US two point two is typically made available within about a month of real time. I think although no, we have we have an early product. I should have answered this. It's either a month or it's either within a week or a month of real time that it's it's updated. So it might actually be weekly updates at this point. So it's it is more it's updated more frequently than than the data available from from grace because grace is has a latency of typically two to four months. Uh, and we can run up to neural time with chielled us two point two, but then it it takes a little longer for us to if we only run once a week, and then we have to upload it to the guest discs, so it might be a little longer than that.

1:18:08 Speaker 2: So for some things that could be useful for early warning for some applications, but you know, others might need you know, data within twenty four hours of real time for to be useful.

1:18:23 Speaker 1: Thanks Matt. I just wanted to clarify that in geomaney or gs disc there may be one or two months of latency for data, but if you go to NASA Earth Data Search, you find weekly data there. Question sixteen do we need know that that's also for exercise? We answered that similarly. Question okay, so question eighteen for the demonstration, I'm a little with that also answered. I'm sorry, she questioned nineteen or also has been answered. Question twenty. Are there correction for developments such as urban built out, mining, et cetera?

1:19:10 Speaker 2: This question twenty, Yeah, so we when we've looked at things like uh, you know, oil extraction or mining, even though those might be you know, large amounts of mass at a small scale, because it's such a small scale, it does not have an effect on on the grace observations. So so no correction is really necessary. I mean even in a place like Saudi Arabia where they pump huge amounts of oil that the resulting change in mass is less than than you would see from a water storage change, So it's below the aeror level urban built out, you know, I don't.

1:19:54 Speaker 2: I don't know if any cities have built you know, they've brought in construction material fast enough to have an effect on you know, to be detected by grace. I tend to doubt it.

1:20:11 Speaker 1: Thank you. We're on question twenty two. We've answered several questions for exercise, I'm just keeping some questions. Question twenty two is can we use the groundwater data to calculate the velocity or rate of change of groundwater of any area?

1:20:32 Speaker 2: Yeah? I mean you could, you know, if you you could plot the time series and then look at, you know, how the the change, you know, how fast the change is happening. You know, it's probably more accurate on an inter annual basis than it would be, you know, on a sub monthly basis or something like that. But but yeah, theoretically you can. You can take the changes in groundwater versus time and look at the rate of change.

1:21:04 Speaker 3: Thank you.

1:21:06 Speaker 1: The next question is about availability of Gilda's two point two in Google Earth Engine. Is it available in there. I believe some Gelda's data are available, but I'm not sure about Gilda's two point two met if you know, if it's there in Google Earth Engine.

1:21:27 Speaker 2: I don't think GLS two point two is on Google Earth Engine. But then again, you know Google tends to take things they find online and put them up without asking us, So it's possible that I don't know that it's there.

1:21:39 Speaker 1: Yeah, I was trying to check, and I don't see exactly two point two. But I'll come from that question twenty four. Is it better to correlate change in ground with sentinel one inside of the same location to determine the reason for uplift or subsidence? I'm asked king this while considering injection, welse.

1:22:09 Speaker 2: I I I think that. So, first of all, we're not you know, gailed US wouldn't model an injection well, so it's not going to be very good at downscaling the GRACE data in in areas where there's you know, a direct human impact, like like groundwater pumping for irrigation and direct injection. So I wouldn't necessarily trust, say, you know, one degree pixel from grace in a region where where there's an anthrogetic effect like that, that's that's significant, and comparing with within sur data to look for upflip, uplip or subsidence.

1:22:53 Speaker 2: Yeah, I think I think it would be you we look and maybe I'm wrong, may maybe find something interesting there, but I wouldn't necessarily expect to see a good correlation between the two.

1:23:13 Speaker 3: Thank you.

1:23:13 Speaker 1: The next question is question twenty seven, referring to the key limitations on slight thirty one. It is noted that Hilda's two point two does not simulate groundwater pumping in the context of Indonesia, particularly in the regions dominated by massive localized pumping for large scale mining operations. How severely does this limitations kew the GRACE fall on downscale data? Does the model misinterpret this localized anthropogenic depletion as broader climate driven animally.

1:23:53 Speaker 2: Yeah, there are a few a few issues here, so yes, First of all, it is a significant limitation. If there's localized pumping, you know, that's not going to be captured by the model, and it's so it's not going to be effectively downscaled from GRACE. So theoretically GRACE would detect the effects of pumping, but typically only if it's over a significantly large region. So if there's a you know, for example, the high planes off for this Southern high and Central high planes offer, there's skipping an amount of pumping.

1:24:26 Speaker 2: There's been a long term decline in groundwater storage there, and that's that's detected by GRACE. But if you're talking about a smaller scale operation, then that that's going to be sort of smoothed out in in the GRACE data, just because the Great Grace has a course resolution. But the other issue here is that, you know, because Indonesia's islands, GRACE does does not do well resolving Thrusser water store changes over islands because the much larger oceanic mass changes are not you know, are not perfectly modeled and removed from the GRACE data.

1:25:05 Speaker 2: So there's a lot of you know a lot of errors of our islands like Indonesian, So we're really lying relying on in Indonesia. In the case of GLS two point two is the model itself, and again the model doesn't simulate the groundwater pumping, so it's it's not really going to be any good at Similarly, the sort of anthrogenic effects.

1:25:29 Speaker 1: Thank you for your own question thirty one now. So this is again based on exercise, so I'll skip that and maybe come back later to thirty one.

1:25:46 Speaker 2: Yeah, I mean, if I can interrupt for a second, I found the answer on the latency question for GLS two point two, and it's it's actually available right now. The g LS two point two EP, which is the extended product where runs there's no more GRACE data to assimilate, but the model continues to run using other inputs. That's available right now up through April twenty second, which is six days ago. So I think it typically is it's on the order of seven to fourteen days latency, but you apparently it's only six days today.

1:26:21 Speaker 2: I think the data might have just been posted.

1:26:24 Speaker 3: That's great, that's great to know. Thank you.

1:26:31 Speaker 1: Yeah, so question thirty three, we're going to go to question thirty three. When you say groundwater storage in the model, are we actually measuring round water or we are just calculating whatever is left over after subprect soil snow plants.

1:26:49 Speaker 2: Yeah, this is I would encourage you to look at the publications that we have on graces. In ways, it's even more complicated than what you said. The cashment landsforce model simulates groundwater as a basically what's left after you you know, if you have a certain amount of water storage, it actually stimulates this uh this this water uh sort of loss from from a fully saturated state and uh and then you know a sokhomofore about Hey, how GRACE data simulation works. Are basically comparing the anomaly in the model to the anomaly and Grace and then making adjustment to the model's anomaly.

1:27:33 Speaker 2: So, uh so when you you know, we're not measuring groundwater using a well observation, We're we're really calculating it, uh you using a model simulation and then and then correcting it using an information from GRACE. I hope that's helpful, but I really would encourage you to look at the the publications if you're interested in in the de tells.

1:28:04 Speaker 1: I'll go to question forty and that's the last question, and then we can go back to a couple of more questions later on because we're almost at the end of the time. So question forty is I'm looking for a groundwater animal is on the east coast of James Bay. Unfortunately, there is no observation or any kind of Well, what is your suggestion for the validation and the comparison. What can I do for the downscaling.

1:28:36 Speaker 2: Okay, so I'm not familiar exactly where where you're looking for data. I can say that, you know, most of the world there is not there's not good groundwater well observations available. And what we've done is, uh, you know, we've done our evaluation of the GLS two point two model output using you know, areas in the world where there is you know, a substantial number of well observations to use for validation.

1:29:07 Speaker 4: Uh.

1:29:07 Speaker 2: And then you know, really I always like to say, you know, we've we've sort of proven that this is working reasonably well already. I don't know that there's how much science stuff you've done in terms of evaluation, and maybe just think about what's your real scientific question and then and then take a look and see if the data are useful for that.

1:29:29 Speaker 3: Thank you so much, Mett.

1:29:31 Speaker 1: So we are at the end of our webinar time, and thank you doctor Rudle for your presentation and your answering all the questions. We also want to thank all of you for attending today's session. There are some questions about Gioannie not responding. I think it's the same data being accessed by many people at the same time might do that. You can try it at a different time and it will work. So with that we thank you again and hope to see you on Thursday on thirtieth of April at the same time. And want to thank our set team here for their help, especially our coordinator Natasha Johnson Griffin and Maria Maravito, Cherry Morris brock Levin say our editors Mariam Arabito, Sarahkshell, Jonathan O'Brien and my colleagues for instructional design so monthly and Sean McCartney and Erica Bordas we all work together on these webinars, So thanks our set team and thank you all for atturning to this session once again.

1:30:46 Speaker 1: Matt, great to have you here with our set trainings and we've got a lot of information about both Grace last week and Jill does today, so we really thank you for your time and your help with this.

1:31:01 Speaker 2: Yeah, thanks to everyone who attended and helped with the with the training and much appreciated.

1:31:11 Speaker 4: H