NASA ARSET Overview and Analysis of NASA Terrestrial Water Storage Data from GRACE_GRACE-FO Pt. 1

0:01 Speaker 1: Hello everyone, Welcome to this applied remote sensing training on

0:06 Speaker 1: monitoring groundwater changes from Water Resources Management. My name is

0:11 Speaker 1: Amita Meta from our SET, and today we will be

0:15 Speaker 1: talking about overview and analysis of NASA terrestrial water storage

0:19 Speaker 1: data from Gravity Recovery and Climate Experiment or GRACE and

0:24 Speaker 1: GRACE follow on missions. We have a guest speicker today,

0:29 Speaker 1: doctor Matthew Roddell from NASAGA Space Flag Center, and I

0:33 Speaker 1: will be introducing him shortly. We'll start with a brief

0:37 Speaker 1: introduction to our SET program. Our SET is part of

0:41 Speaker 1: NASA's Earth Action Capacity Building program and it provides accessible

0:46 Speaker 1: and relevant, cost free trainings on remote sensing satellites, sensors, methods,

0:51 Speaker 1: and tools. Trainings include a variety of applications of satellite

0:56 Speaker 1: data on these thematic areas including agriculture, disasters, ecological conservation,

1:03 Speaker 1: health and air quality, water resources, and wildline fires. Trainings

1:08 Speaker 1: are tailored to audiences with a variety of experienced levels.

1:13 Speaker 1: Our SET trainings are online and there are in person

1:16 Speaker 1: trainings offered as well. There live and instructor led like

1:21 Speaker 1: this one, or there are asynchronus and self based trainings

1:25 Speaker 1: available from our SET website. As I mentioned, they're cost free.

1:30 Speaker 1: Most of our training material is translated into Spanish and

1:35 Speaker 1: many trainings are bilingual and there are multilingual options available.

1:39 Speaker 1: Our set only uses open source software and data and

1:43 Speaker 1: accommodates differing levels of expertise. Again, please visit our set

1:47 Speaker 1: website for more information. We will start with this webinar

1:53 Speaker 1: series on monitoring groundwater changes for water resources management. Now.

1:59 Speaker 1: Groundwater is water from precipitation that percolates into the soil

2:04 Speaker 1: and moves downward. It fills up cracks and openings in

2:07 Speaker 1: the rocks and sand below the surface. Depending on the

2:10 Speaker 1: porousity of the soil, it percolates down and it can

2:14 Speaker 1: extend from just below the surface all the way down

2:16 Speaker 1: to several thousand feet. You can see from this diagram

2:21 Speaker 1: that from total global water, most of it is saline

2:25 Speaker 1: and it's in ocean or it's brickish. Just two point

2:28 Speaker 1: five percent of total water is fresh water, and out

2:31 Speaker 1: of that thirty percent is in groundwater. Groundwater ages can

2:38 Speaker 1: range from months to millions of years, depending on the

2:41 Speaker 1: source of the water and how deep it is. The

2:44 Speaker 1: total groundwater volume in the upper two kilometer of continental

2:49 Speaker 1: crust is approximately twenty two point six million cubic kilometer

2:54 Speaker 1: and out of which small amount abouzero point one to

2:57 Speaker 1: five million cubic kilometers is less than fifty years old.

3:02 Speaker 1: It is the most extracted resource worldwide and its renewable

3:06 Speaker 1: resource depending on regional and environmental conditions. Traditionally, water wells

3:16 Speaker 1: are used to monitor groundwater levels. Here you can see

3:20 Speaker 1: from National Groundwater Monitoring Network blue dots show wells in

3:25 Speaker 1: the United States. So water level in these wells are

3:29 Speaker 1: regularly monitored to estimate what the groundwater levels would be

3:33 Speaker 1: at that location, and there are no direct measurements of

3:37 Speaker 1: groundwater from remote sensing observations. Measurements from GRACE and Grace

3:42 Speaker 1: follow On. They are used to estimate or infer total

3:46 Speaker 1: terrestrial water depth, and these total water depths are then used,

3:51 Speaker 1: along with additional hydrological information to derive global large scale

3:57 Speaker 1: ground water distribution at their solution of about one hundred

4:01 Speaker 1: and fifty thousand kilometer square. Additionally, there are two more

4:08 Speaker 1: sources of groundwater data. One is Global Land Data Assimilation

4:13 Speaker 1: System or GILDAS that assimilates GRACE and Grace follow On

4:17 Speaker 1: data and data as global groundwater data, and the second

4:21 Speaker 1: one is an opera surface displacement product which is derived

4:26 Speaker 1: from synthetic aperture radar measurements from Sentinel one satellite and

4:31 Speaker 1: so these two are also groundwater products available from NASA.

4:39 Speaker 1: So the overall training learning objectives are that by the

4:42 Speaker 1: end of this training you will be able to identify

4:45 Speaker 1: characteristics of groundwater quantity data sets from GRACE missions, GILDAS

4:50 Speaker 1: and opera displacement product. Access analyze and visualize GRACE data

4:57 Speaker 1: to monitor seasonal to inter annual changes in large scale

5:01 Speaker 1: total terrestrial water storage. Access analyze and usualized GILDAS data

5:07 Speaker 1: to monitor interannual to interdictal changes in groundwater at regional scale,

5:12 Speaker 1: map surface subsidence related to groundwater extractions with OPERAS data,

5:18 Speaker 1: and recognize applications of these groundwater data sets available at

5:22 Speaker 1: multiple spatial and temporal scales for monitoring drought and flood

5:26 Speaker 1: conditions and for planning groundwater resources for a variety of

5:31 Speaker 1: uses such as for drinking water or for irrigation, etc.

5:38 Speaker 1: These are the prerequisites fundamentals of remote sensing. This is

5:41 Speaker 1: a self paced course available from our set website. It

5:45 Speaker 1: provides background about satellites, their orbits, different sensors, their special

5:50 Speaker 1: and temporal resolutions and other characteristics of remote sensing data.

5:55 Speaker 1: Then there is an introductory training are set provided in

5:58 Speaker 1: twenty twenty on groundwater monitoring using obervations from Grace Missions

6:04 Speaker 1: that also has some useful information. This training will have

6:10 Speaker 1: three parts. Today, as I mentioned, will be focusing on

6:14 Speaker 1: overview and analysis of terrestrial water storage data from GRACE

6:17 Speaker 1: and Grace Fallen. Next week on April twenty eighth, Part

6:22 Speaker 1: two will be on overview and applications of gildask groundwater

6:26 Speaker 1: data products at regional scale, and on thirtieth of April,

6:30 Speaker 1: Part three will be on overview and applications of opera

6:33 Speaker 1: disc to monitor groundwater changes. There will be one homework

6:38 Speaker 1: posted on last day of the training that is thirty

6:41 Speaker 1: eeth of April, and the homework will be due on

6:44 Speaker 1: fifteenth of May. The homework will be posted on the

6:47 Speaker 1: training web page and a certificate of completion will be

6:50 Speaker 1: awarded to those who attend all live sessions and complete

6:54 Speaker 1: the homework assignments before the given t day. Start with

7:00 Speaker 1: today's session and specific objectives for today are that by

7:04 Speaker 1: the end of part one you will be able to

7:06 Speaker 1: identify characteristics of terrestrial water storage data sets from Grace

7:10 Speaker 1: and Grace Swallow on monitor seasonal and interannual changes in

7:14 Speaker 1: terrestrial water storage using GRACE analysis tool. So the outline

7:21 Speaker 1: for today is that our speaker, Doctor Matthew Rodell, will

7:25 Speaker 1: provide an overview of GRACE missions, Race data products and applications.

7:30 Speaker 1: Then we will have a demonstration of race data analysis

7:34 Speaker 1: tool that allows analysis and visualization of terrestrial water storage data.

7:39 Speaker 1: There will also be a hands on exercise that you

7:42 Speaker 1: will be conducting using the same analysis tool and some

7:46 Speaker 1: of your homework questions will be based on that. At

7:52 Speaker 1: the end, we will have a question and answer session

7:55 Speaker 1: and just a note about asking questions. Please put your

7:59 Speaker 1: questions in the questions box and we will let us

8:01 Speaker 1: them at the end of the webinar. Feel free to

8:04 Speaker 1: enter your questions as we go and we will try

8:07 Speaker 1: to get to all the questions during the question and

8:09 Speaker 1: answer session. After the webinar, the remainder of the questions

8:13 Speaker 1: will be answered in the Q and a document which

8:16 Speaker 1: will be posted on the training website about a week

8:19 Speaker 1: after the training. With that, I want to introduce our

8:25 Speaker 1: speaker for today, doctor Matthew Rodel. Doctor Matthew Rodel is

8:31 Speaker 1: the Deputy Director of Earth Sciences for Hytrosphere, Biosphere and Geophysics,

8:35 Speaker 1: or hPG at NASA Corder Spaceplight Center. HBG comprises more

8:41 Speaker 1: than three hundred and fifty scientists and engineers within five

8:45 Speaker 1: laboratories involved in remote sensing and numerical modeling of land

8:50 Speaker 1: and ocean processes and related applications. Doctor rodel has also

8:55 Speaker 1: served as Associate Deputy Director for HBG and as Chief

8:59 Speaker 1: of the Hydrological Sciences Lab. He is a member of

9:04 Speaker 1: the science teams for NASA's Gray's follow On Mission and

9:07 Speaker 1: Future Grace Continuity or Grace Sea Mission. He leads the

9:12 Speaker 1: Global Land Data Assimilation System and projects focused on monitoring

9:16 Speaker 1: groundwater storage changes, mapping and forecasting drought wetness, and detecting

9:23 Speaker 1: climate related variations in the watorcycle. Doctor Roddell is a

9:27 Speaker 1: past associate editor for the Journal of Hydrology and a

9:32 Speaker 1: current editor for the Journal of Hydrometology. He has also

9:40 Speaker 1: chaired the Hydrology program for the American Geophysical Union or

9:44 Speaker 1: AGU Fall meeting from twenty nine to twenty eleven and

9:49 Speaker 1: led various national and international scientific working groups. He received

9:54 Speaker 1: a Presidential Early Career Award for Scientists and Engineers in

9:58 Speaker 1: two thousand and six, a NASAJSFC Earth Science Achievement Award

10:03 Speaker 1: in two thousand and seven, a Robert H. Goddard Award

10:07 Speaker 1: for Exceptional Achievement in Science in twenty eleven, and an

10:12 Speaker 1: Arthur S. Fleming Award for Outstanding Federal Service in the

10:16 Speaker 1: area of Basic Science in twenty fifteen. He was elected

10:21 Speaker 1: to the rank of AGU Fellow in twenty twenty two.

10:24 Speaker 1: Doctor Rodel has more than one hundred and sixty peer

10:27 Speaker 1: reviewed publications and appears on clariveet Web of Sciences twenty

10:32 Speaker 1: eighteen to twenty three lists of highly cited researchers. He

10:37 Speaker 1: holds a BS in Environmental science from the College of

10:40 Speaker 1: William and Mary and a PhD in Geological Sciences from

10:44 Speaker 1: the University of Texas and Austin. With that Midnight, doctor

10:48 Speaker 1: Rodell Matt take it.

10:50 Speaker 2: Away, Hey, thank you, Amita.

10:55 Speaker 3: I might to provide an overview of the Grace and

10:58 Speaker 3: Grace follow On missions. So, Grace and Grace follow On

11:03 Speaker 3: are two separate satellite missions. One launched in two thousand

11:08 Speaker 3: and two and ended in twenty seventeen, and Grace follow

11:10 Speaker 3: On picked up in twenty eighteen and is still active.

11:15 Speaker 3: They were both jointly developed by NASA and the Germans,

11:21 Speaker 3: and both of them are twin satellite systems meeting their

11:24 Speaker 3: two satellites operating at once. And the two satellites are

11:29 Speaker 3: critical because actually the key measurement is the distance between

11:33 Speaker 3: those satellites and how it changes in time. So one

11:36 Speaker 3: is following the other, or about two and twenty kilometers apart,

11:41 Speaker 3: about four hundred kilometers above the land surface or Earth surface,

11:46 Speaker 3: and as they orbit the Earth in a near polar orbit,

11:50 Speaker 3: they basically observe all different regions of the world pretty

11:55 Speaker 3: well over the course of about a thirty day period.

12:01 Speaker 3: So these satellites again provide global coverage. You see the

12:04 Speaker 3: dates of the two missions, and right, the key measurement

12:10 Speaker 3: is the distance between the two satellites, and that distance

12:13 Speaker 3: is measured by a micro rate K band ranging instrument

12:16 Speaker 3: and actually on grace follow on, there's also an experimental

12:19 Speaker 3: laser instrument. And so imagine at two hundred and twenty

12:22 Speaker 3: kilometer distance and you're able to measure that distance every

12:27 Speaker 3: five seconds with the precision down to the size of

12:31 Speaker 3: a red blood cell.

12:33 Speaker 2: That's how accurate these measurement measurements are.

12:37 Speaker 3: In addition, there each satellite has an accelerometer on board.

12:40 Speaker 3: When an accelerometer does is it measures non gravitational forces

12:46 Speaker 3: on the satellite, in particular things like atmospheric drag. So

12:50 Speaker 3: you don't want the atmospheric drag to leak into your

12:55 Speaker 3: estimates of the changes in the distance but then and

12:57 Speaker 3: then cause errors in your observations of the gravity field.

13:03 Speaker 3: There are also GPS receivers on board to provide the

13:06 Speaker 3: precise locations of the satellites. So what happens is, you know,

13:13 Speaker 3: as these two satellites are orbiting the Earth, imagine what

13:17 Speaker 3: we call a mass anomaly, like a mountain range. That's

13:20 Speaker 3: where there's a bit or actually a lot more mass

13:24 Speaker 3: at the surface than there is on average. So imagine

13:27 Speaker 3: these satellites floating along in space and they come closer

13:32 Speaker 3: and closer to a to a mountain range, and that

13:35 Speaker 3: mountain range has extra mass, which means there's extra gravitational potential.

13:39 Speaker 3: And what it does it pulls the first satellite forward,

13:43 Speaker 3: and that satellite is basically the separation between the two

13:48 Speaker 3: satellites increases because that first satellite is sort of speeding.

13:51 Speaker 2: Up as they pass over the mountain range.

13:53 Speaker 3: The first satellite sort of held back by that extra gravity,

13:56 Speaker 3: and the second satellite speeds up in the distance between

13:59 Speaker 3: the two of them become smaller, and then things sort

14:01 Speaker 3: of even out again once they pass the Mountain range.

14:06 Speaker 2: So that's sort of an extreme example.

14:07 Speaker 3: Actually, you know, Mountain range is actually a huge amount

14:10 Speaker 3: of mass, but these satellites in the measurements are so

14:14 Speaker 3: sensitive that they can actually detect changes caused by changes

14:19 Speaker 3: in mass over time over the same location from month

14:24 Speaker 3: to month. So for example, if there's a big snowstorm

14:27 Speaker 3: and there's a lot more mass on the ground because

14:30 Speaker 3: of all that snow, that's actually enough mass and enough

14:34 Speaker 3: gravitational change to affect how the satellites orbit in a

14:39 Speaker 3: way that we can detect and then infer the amount

14:42 Speaker 3: of snow on the ground. So again we use sort

14:48 Speaker 3: of fundamental physics, you know, or understanding of how mass

14:52 Speaker 3: and gravity are related to translate the grace follow on

14:56 Speaker 3: measurements these distance between the satellites into gravi and then

15:01 Speaker 3: from there into mass concentrations, meaning like where there's more

15:06 Speaker 3: or less mass on the surface of the Earth. So,

15:11 Speaker 3: as I mentioned before, there there are subtle shifts and

15:14 Speaker 3: it's gravity field that.

15:15 Speaker 2: Are caused by movements of mass around the Earth.

15:18 Speaker 3: If you think about what moves or how do you

15:22 Speaker 3: have large movements of mass on the Earth. The biggest

15:25 Speaker 3: one is the is the ocean, So you think about

15:27 Speaker 3: ocean tides and ocean sloshing around in the ocean basins.

15:31 Speaker 3: That's a huge amount of mass. There's also atmospheric mass.

15:36 Speaker 3: Think about surface pressure pressure changes of the surface.

15:40 Speaker 2: That's really a.

15:40 Speaker 3: Measurement of the amount of mass of atmosphere above given location.

15:45 Speaker 3: And then over land changes the mass are primarily associated

15:49 Speaker 3: with redistribution of water. So again you know something like

15:52 Speaker 3: a snowstorm or a drought, or or a flood, or

15:56 Speaker 3: even you know, just smaller changes in water storage over

16:01 Speaker 3: a large region. Those are actually huge quantities of mass

16:05 Speaker 3: that again are are big enough to cause changes in

16:09 Speaker 3: gravity that then affect the orbits of the Grace satellites.

16:14 Speaker 3: So what we do is, after accounting for the atmospheric

16:17 Speaker 3: oceanic changes using models and measurements of the Earth, you

16:22 Speaker 3: sort of strip those off from the Grace observations. There

16:26 Speaker 3: are also some solid earth mass redistributions.

16:30 Speaker 2: For example, if there's an earthquake, that.

16:31 Speaker 3: Could be a large amount of mass moving and we

16:34 Speaker 3: can estimate how much that is, and there are solid

16:37 Speaker 3: earth tides that are pretty well understood. So after you

16:40 Speaker 3: remove all those other components, what's left is the changes

16:44 Speaker 3: in mass associated with changes in interestraal water storage. So

16:54 Speaker 3: I'll get to what trustra water storage is in a moment,

16:57 Speaker 3: but spice to say, it's of the it's all the

17:01 Speaker 3: water on and in the land surface.

17:03 Speaker 1: Uh.

17:04 Speaker 3: And so the data that we get from GRACE are

17:06 Speaker 3: provided as an equivalent water thickness.

17:10 Speaker 2: So imagine if you.

17:11 Speaker 3: Took all the water on and in the land, all

17:13 Speaker 3: the groundwater, so is your surface water snow ice and

17:17 Speaker 3: just created, you know, just ponded it on the surface,

17:20 Speaker 3: and then you watched how that how the the depth

17:23 Speaker 3: of that pond changed over time, you know, sort of

17:26 Speaker 3: like the depth of water in your bathtub. And and

17:31 Speaker 3: that is provided those those an anomalies of trust water

17:36 Speaker 3: storage relative to some arbitrary data are provided in centimeters

17:40 Speaker 3: or millimeters equivalent water thickness.

17:44 Speaker 2: So the so you know, when we.

17:46 Speaker 3: Say level one, two, three for GRACE products, Level one

17:49 Speaker 3: is basically the the measurements of the distance between two satellites.

17:53 Speaker 3: Level two is is the gravity anomaly fields and level

17:57 Speaker 3: three or the trust water storage anomally data that we

18:00 Speaker 3: as hydrologists can use. The effect of spatial resolution of

18:06 Speaker 3: these observations from GRACE and GRACE follow under is very

18:10 Speaker 3: coarse relative to other measurement systems that you're probably familiar with.

18:16 Speaker 3: So for GRACE, the effect of spatial resolutions about one

18:19 Speaker 3: hundred thousand to one.

18:19 Speaker 2: Hundred and fifty thousand square kilometers.

18:22 Speaker 3: To give you an idea of how big that is,

18:23 Speaker 3: the state of Illinois about one hundred and forty five

18:26 Speaker 3: thousand square kilometers, So we're.

18:28 Speaker 2: Talking about very large regions.

18:29 Speaker 3: And actually, when GRACE first launched, a lot of hydrologists

18:33 Speaker 3: through up their hands said I can't use this, it's

18:35 Speaker 3: much too much To course, I was actually among the

18:39 Speaker 3: first first hydrologists to be involved in using GRACE data.

18:43 Speaker 2: And we figure out a lot of ways to use it.

18:44 Speaker 3: And the bottom line is we have no other global

18:47 Speaker 3: measurements of total trust or water storage in a particular

18:50 Speaker 3: ground whatter, which we'll talk about later. So if you're clever,

18:54 Speaker 3: you can find a lot of ways to use these

18:55 Speaker 3: data despite the coarseness. If you look for trust or

19:01 Speaker 3: water storage data, and we'll provide some links later. They're

19:05 Speaker 3: often provided on a one degree or a half degree

19:08 Speaker 3: resolution grid and I want you to it's an important takeometric,

19:14 Speaker 3: important take on point that's really just for convenience. In

19:19 Speaker 3: order for those data to be meaningful, you can't just

19:23 Speaker 3: take one grid point and say this is how the

19:25 Speaker 3: water storage is changing over that grid point. You have

19:27 Speaker 3: to average over a sufficiently large region and I'm talking

19:31 Speaker 3: about greater than one hundred thousand square kilometers in order

19:34 Speaker 3: for your your results. You know, the time series of

19:37 Speaker 3: trust to water stores you might get, for example, in

19:39 Speaker 3: order for that to be meaningful, and the errors or

19:46 Speaker 3: the uncertainty is on the order of one to two

19:49 Speaker 3: centimeters quite one height of water change each month for

19:55 Speaker 3: a three hundred thousand square kilometer or a larger region. Okay,

20:03 Speaker 3: so what can we do with these these trust water

20:06 Speaker 3: storage data? So again I mentioned before trust water storage

20:09 Speaker 3: is some of the groundwater, some I sure surface water,

20:13 Speaker 3: snow and ice.

20:14 Speaker 2: As you can see sort of the chart on the

20:19 Speaker 2: right there.

20:21 Speaker 3: So Grace cannot and Grace follow cannot differentiate among these

20:25 Speaker 3: different trust water storage components. They're only showing the bar

20:30 Speaker 3: on the left there this total trust water storage and

20:33 Speaker 3: and in fact, I can only tell us the the

20:36 Speaker 3: changes in trust water storage, not the absolute amount of water.

20:40 Speaker 3: So don't expect that grace is going to tell you

20:42 Speaker 3: how much water is left in the Ogalalla aqua, for example.

20:46 Speaker 3: It's only gonna tell us how that water storage is changing,

20:50 Speaker 3: usually relative to the long term mean, from the GRACE data.

20:56 Speaker 3: So one of the ways that we can we can

21:00 Speaker 3: sort of tease out what's happening with the individual components

21:04 Speaker 3: is to combine the GRACE data with a with a landserface.

21:07 Speaker 2: Model through data a similation.

21:10 Speaker 3: And because that model has other information going into it

21:14 Speaker 3: from other observations and and our understanding of the system,

21:19 Speaker 3: it's able to do vertical, horizontal and temporical temporal disaggregation

21:23 Speaker 3: to make the GRACE data more more useful. So you know,

21:31 Speaker 3: if you have trust your water storage data, you know

21:33 Speaker 3: a simple approach for disaggregating would be to simply do

21:38 Speaker 3: sort of a water balance approach.

21:39 Speaker 2: So you see the boldled equation at the.

21:41 Speaker 3: Top there, groundwater equals trust your water storage chain or

21:45 Speaker 3: ground road change equals stretcher water storage change minus soul

21:48 Speaker 3: moisture minus snowater equivalent minus surface water. And you know

21:54 Speaker 3: early on in the GRACE mission. That was one of

21:57 Speaker 3: the ways that we that we evaluate the GRACE data

22:00 Speaker 3: is is to do use you know, land based observational

22:06 Speaker 3: time series of these components and and sum them all up.

22:09 Speaker 3: And so do we actually see the trust water storage

22:12 Speaker 3: changes we expect to see based on grace answer is yes, again,

22:17 Speaker 3: you can.

22:17 Speaker 2: You can do a you can do the equation.

22:20 Speaker 3: You set the equation up so you find groundwater storage

22:24 Speaker 3: changes as a residual if you have estimates of the

22:27 Speaker 3: other components from either observations or models. A more sophisticated

22:35 Speaker 3: approach is data assimilation and and this involves again you have.

22:41 Speaker 2: A land service model.

22:42 Speaker 3: The LANDSERFCE model has various inputs going into it in

22:46 Speaker 3: addition to the GRACE data. The LANDSERCE model on its

22:49 Speaker 3: own can provide estimates of how the Trust water storage

22:53 Speaker 3: change components are changing in time. But then when you

22:56 Speaker 3: assimilate the GRACE data, you have an additional constraint on

22:59 Speaker 3: the model. You basically get better results. So we're gonna

23:03 Speaker 3: be talking about GLEDS version two point two. And in

23:07 Speaker 3: this version, we have a landsforce model called the cash

23:10 Speaker 3: Man Lanserce model that simulates groundwater storage in addition to

23:15 Speaker 3: the other components, and by using GRACE as a as

23:21 Speaker 3: a constraint on that model, we get a result that's

23:23 Speaker 3: better than either the model alone or the Grace observation alone.

23:31 Speaker 3: So we'll talk about some of the GRACE products that

23:33 Speaker 3: are out there. So here's some links.

23:38 Speaker 2: When you have a moment, you.

23:40 Speaker 3: Can get the slides and follow these links. There are

23:45 Speaker 3: several different Grace and Grace follow on trusher water storage

23:49 Speaker 3: data products available. Protter Spaceflight Center, Just Propulsion Laboratory gf

23:56 Speaker 3: SAID which is in Germany, and the University Texas all

24:00 Speaker 3: of their own products. So, for example, the GPL Grace

24:07 Speaker 3: Data Portal has a few options here. You can download

24:12 Speaker 3: the grids as global mass cons. A mascon is sort

24:16 Speaker 3: of an advanced way of deriving trust or water storage

24:20 Speaker 3: data from the GRACE observations, and all of the centers

24:23 Speaker 3: now use.

24:24 Speaker 2: Mass cons as sort of the preferred method.

24:29 Speaker 3: There's an interactive Grace data browser and you can download

24:35 Speaker 3: the monthly grids for the land or for the ocean.

24:41 Speaker 3: So there are you know, a lot of applications of

24:44 Speaker 3: Grace and Grace follow on data. One of the early

24:48 Speaker 3: ones or not so early Actually, this is publication from

24:52 Speaker 3: twenty eighteen where we looked at the changes in trust

24:56 Speaker 3: water storage over the peer twenty two to twenty sixteen. Basically,

25:03 Speaker 3: at each point on Earth, we determined the rate of

25:06 Speaker 3: change of trust your water storage by fitting a linear

25:09 Speaker 3: trend after removing the seasonal cycle. So if you had

25:14 Speaker 3: you see a blue here, that means trend was upward.

25:17 Speaker 3: It was an increase in trust or water storage on

25:21 Speaker 3: average over time. You see the bar at the bottom

25:24 Speaker 3: of the units are centimeters per year, and where it's

25:27 Speaker 3: a yellow, orange or red, that means that the trust

25:31 Speaker 3: water storage was decreasing over time. And so what we

25:35 Speaker 3: did with this this study was was try to understand

25:38 Speaker 3: why trustal water storage was changing. And in some cases

25:42 Speaker 3: it's just natural variability and those are that's indicated by green.

25:47 Speaker 3: And what I mean by that is maybe you had

25:48 Speaker 3: a drought in the beginning of the time period around

25:51 Speaker 3: two thousand and two, and maybe there was you know,

25:53 Speaker 3: just sort of a wet period towards the end, and

25:55 Speaker 3: so it looks like you have this increase in trust

25:57 Speaker 3: your water storage, but we don't. We don't expected to

26:00 Speaker 3: continue a long term. It's just sort of part of

26:03 Speaker 3: the natural fluctuations. On the other hand, there are regions

26:07 Speaker 3: where where we might be you know, there might be

26:11 Speaker 3: a human a direct human impact, like groundwater pumping. And

26:15 Speaker 3: one of the first ones that we saw and the

26:17 Speaker 3: gray data was in northern India where a huge amount

26:21 Speaker 3: of water is pumped out of the opera there to

26:25 Speaker 3: use for irrigating crops. When you pump water out of

26:28 Speaker 3: an aqua faster than it can recharge, and you pour

26:32 Speaker 3: it on the land surface derigate crops, and most of

26:34 Speaker 3: the water evaporates or transpires, then over time the operas.

26:38 Speaker 2: Can be depleted and and there's a there's a.

26:41 Speaker 3: Very fast rate of depletion in northern India that's clearly anthropogenic.

26:48 Speaker 3: And then the other thing is there might be climate

26:49 Speaker 3: change signals. And the most obvious place to look for

26:53 Speaker 3: this is is someplace like Greenland, where the ice sheets

26:57 Speaker 3: are blating or melting away into the ocean, and that's

27:01 Speaker 3: why most of Greenland is red. There there might be

27:04 Speaker 3: there's some debate over whether the blue in the middle

27:06 Speaker 3: is an accumulation of snow or just a just an

27:10 Speaker 3: artifact of the data processing that might be related to

27:14 Speaker 3: something called post glacial rebound. Another more operational application is

27:22 Speaker 3: is drought monitoring. And one thing we've done is we've

27:28 Speaker 3: developed these drought indicators that are based on the GRACE

27:33 Speaker 3: data assimilation into the LANDSERFCE models. So the data simulation

27:36 Speaker 3: allows us to do again the vertical, temporal and horizontal

27:41 Speaker 3: downscaling of the GRACE data. Remember, GRACE data are monthly,

27:46 Speaker 3: they're very coarse and and in fact they're only available

27:50 Speaker 3: a few months after a near real time after real time.

27:53 Speaker 3: So to make them really useful for an operational application

27:56 Speaker 3: like drought monitoring, the data.

27:58 Speaker 2: Similation is really essential.

28:00 Speaker 3: It allows us to run up to neural time, it

28:02 Speaker 3: allows much higher spatial and temporal resolution, and we can

28:07 Speaker 3: also do things like disaggregating vertically. So this is the

28:12 Speaker 3: top two examples are a drought indicator, which basically tells

28:16 Speaker 3: us the relative conditions or the conditions each location relative

28:22 Speaker 3: to the long term going back to nineteen forty eight,

28:26 Speaker 3: which is based on the model results. Obviously, GRACE doesn't

28:29 Speaker 3: go back to nineteen forty eight. And what we see

28:33 Speaker 3: right now is we're get into some pretty dry conditions

28:36 Speaker 3: over much of the US. If you look top right there,

28:39 Speaker 3: you can see that around the world. It's a mix

28:42 Speaker 3: of dry and wet conditions around the world, which.

28:46 Speaker 2: Is what you'd expect.

28:48 Speaker 3: And then there's an example at the bottom there showing

28:51 Speaker 3: drought conditions in Brazil, which matches we see in several

28:59 Speaker 3: news reports. Another thing we can use GRACE data for

29:05 Speaker 3: is looking at flood vulnerability or flood potential. And so

29:11 Speaker 3: we have enough data from GRACE and GRACE follow on

29:13 Speaker 3: going back to two thousand and two, we have a

29:15 Speaker 3: pretty good handle on the range of variability of trust

29:19 Speaker 3: your water storage that we would expect, and when when

29:24 Speaker 3: the trustre water storage is up near the top of

29:26 Speaker 3: that range, we would say a region is vulnerable to floods,

29:29 Speaker 3: meaning you know, additional water that comes down the Lancerfruce's

29:34 Speaker 3: rain or snowmelt is likely to overwhelm the river system

29:40 Speaker 3: and then you start to have you start to have flooding.

29:43 Speaker 3: And so it's a useful way of basically preparing for floods,

29:48 Speaker 3: you know, looking for regions like this, this.

29:50 Speaker 2: Region is very wet.

29:50 Speaker 3: Maybe it's not flooding yet, but you know, look out

29:52 Speaker 3: if there's a storm or something.

29:56 Speaker 2: And that's that's some work that J.

29:58 Speaker 3: T Rieger worked on a while ago, and we've also

30:01 Speaker 3: used our drought wet syndicator maps for this purpose. Another recent,

30:11 Speaker 3: more scientific result was looking at what is apparently an

30:15 Speaker 3: abrupt decline in trust your water storage around the world,

30:19 Speaker 3: which happened around twenty fifteen. The time series is not here,

30:24 Speaker 3: but basically, if you look at the time series of

30:26 Speaker 3: all the average trust water storage for all of the

30:29 Speaker 3: land excluding the ice sheets, where there's a long term

30:35 Speaker 3: persistent trend, excluding the ice sheets and the glaciers, i

30:38 Speaker 3: should say, and you looked at how the time series

30:42 Speaker 3: evolves from two thousand and two to present, you would

30:45 Speaker 3: see around twenty fifteen the time series suddenly has this

30:51 Speaker 3: decline of about one centimeter equivalent head of water average

30:55 Speaker 3: over the entire Earth. And what's really interesting about that

30:59 Speaker 3: is that, you know, twenty fifteen lines up with when

31:01 Speaker 3: we had a series of or in the midst of

31:04 Speaker 3: a series of the warmest years on record, so twenty

31:10 Speaker 3: fifteen through twenty twenty three, or the nine enormost years

31:16 Speaker 3: in the in the data you know, in the temperature

31:20 Speaker 3: record for the Earth's surface, and that of course continued

31:24 Speaker 3: in twenty twenty four and twenty twenty five, and this

31:29 Speaker 3: decline has also persisted, or this this lower level trust

31:33 Speaker 3: your water storage has has persisted.

31:37 Speaker 2: The map here is showing it's twenty fifteen.

31:41 Speaker 3: A significant portion of the landsurface has has hit its

31:45 Speaker 3: lowest on record trust your Water Storage anomaly, which is

31:49 Speaker 3: another indicator of what's happening. And just for fun, here's

31:54 Speaker 3: an animation of GRACE data starting from twenty two and

31:57 Speaker 3: running through twenty sixteen, and just to see how it

32:01 Speaker 3: sort of evolves.

32:03 Speaker 2: You see these.

32:03 Speaker 3: Blobs of wet and dry moving around over time, and

32:07 Speaker 3: then it's going to zoom in on the Acabango Delta

32:11 Speaker 3: in Southern Africa and for this region that's outlined here.

32:16 Speaker 3: This is how the trust your Water storage changes over time,

32:20 Speaker 3: and you can see during this particular period there's a

32:23 Speaker 3: very wet period and the delta, which is home to

32:26 Speaker 3: a lot of cool, big animals you'd see in the zoo,

32:29 Speaker 3: was wet. There was a lot of water there and

32:32 Speaker 3: the elephants are happy. And then towards the end of

32:36 Speaker 3: this period, you going towards twenty sixteen, it's sort of

32:38 Speaker 3: coming back down towards normal. So that's where I'm going

32:43 Speaker 3: to leave it off, and I'm going to hand the

32:46 Speaker 3: mic over to Amita.

32:52 Speaker 1: Thank you so much Mett for your presentation on GRACE

32:55 Speaker 1: Missions data and applications. Next, going to have a short

33:01 Speaker 1: demonstration of GRACE Data Analysis Tool or interactive browser, which

33:07 Speaker 1: allows visualization of terrestrial water storage data from the missions.

33:14 Speaker 1: Is a brief outline we'll learn to navigate the GRACE

33:17 Speaker 1: Data Analysis tool. There are several features that are useful

33:20 Speaker 1: to selecting special and temporal domains. Then view terrestrial water

33:25 Speaker 1: storage or TWS maps and time series using the tool.

33:30 Speaker 1: And will use these two regions for case studies Colorado

33:34 Speaker 1: River Basin and Ogola La Aquifer. These two are very

33:38 Speaker 1: important for water resources in the Western US. So I'm

33:45 Speaker 1: going to share my screen with you. This is the

33:50 Speaker 1: data analysis tool and you can ask questions if you

33:56 Speaker 1: have any and submit questions. For launching tool to just

34:00 Speaker 1: click on launch tool here and then a welcome window opens.

34:05 Speaker 1: At the same time, you can see graced WS data

34:09 Speaker 1: in the background. So let's just quickly go through some

34:13 Speaker 1: of the features, but we are going to demonstrate all

34:16 Speaker 1: of them. So this symbol here this is about different

34:22 Speaker 1: data layers. There are multiple data sets available. You can

34:25 Speaker 1: use plus sign to add data layers. This symbol here

34:30 Speaker 1: it toggles between different layers. The visibility you can turn

34:34 Speaker 1: it off and on. I next to any data source

34:38 Speaker 1: provides information about that particular data, and this symbol here

34:45 Speaker 1: that we will look at. It allows you to draw

34:49 Speaker 1: a box to select a region, or drop a pin

34:52 Speaker 1: to select a point, or even choose river basins as

34:57 Speaker 1: we will see this one. Once you select the region,

35:01 Speaker 1: you can look at map by zooming in, or you

35:04 Speaker 1: can make time series and then you can download data

35:07 Speaker 1: by using this button. This errow here allows you to

35:13 Speaker 1: toggle between different layers, and this animate layer over time

35:18 Speaker 1: allows you to animate TWS data over different time range.

35:25 Speaker 1: So we'll start with looking at the map first. You

35:30 Speaker 1: can zoom in by your computer mouse using your computer mouse,

35:35 Speaker 1: or you can use plus and minus signs here to

35:38 Speaker 1: zoom in and zoom out. Note that here is the

35:42 Speaker 1: time window. You can change time. Last data available here

35:48 Speaker 1: is January twenty twenty six. You can change time, month

35:54 Speaker 1: and year, and you can see TWS changing. Here's the

35:58 Speaker 1: color bar given for that units are in centimeter and

36:03 Speaker 1: the range shown here is minus thirty to thirty centimeter approximately.

36:09 Speaker 1: But you can change a range of these values by

36:14 Speaker 1: sliding here. You can also change visibility or transparency of

36:20 Speaker 1: any layer by changing transparency by moving along this line.

36:27 Speaker 1: And there are some more features here. This is just

36:31 Speaker 1: a different map projection. This allows you to turn off

36:37 Speaker 1: and on, say timeline or there are some options here,

36:41 Speaker 1: but we'll keep them on. And this one allows you

36:47 Speaker 1: to label the map in the sense that once you

36:50 Speaker 1: turn it on you will see country boundaries, which is useful.

36:55 Speaker 1: And if you just click here on the home button,

36:58 Speaker 1: you will be taking back to where we started. You

37:02 Speaker 1: can again zoom in on any region and you can

37:07 Speaker 1: uh look at the year or month that you're interested in.

37:12 Speaker 1: What we are going to do now is uh look

37:16 Speaker 1: at the analysis options. But let's just look at the

37:20 Speaker 1: different data sets available here. Right now we're looking at

37:24 Speaker 1: the maps. Map that you'll see here is from GPL

37:28 Speaker 1: Water Equivalent Thickness over land. If you click here for

37:34 Speaker 1: data search, there are four options, So one way looking

37:38 Speaker 1: at here is land GPL water equivalent thickness, and the

37:44 Speaker 1: similar thickness is available for ocean. Then there are two

37:49 Speaker 1: data data sets CSR water equivalent Thickness. This is from

37:56 Speaker 1: University of Texas, Austin. As was mentioned earlier, and then

38:01 Speaker 1: this is for ocean, so you can be looking at this.

38:06 Speaker 1: You can add this data as well if you like,

38:10 Speaker 1: and then it shows both of these two different color

38:16 Speaker 1: tables show up, and then you can turn this on

38:19 Speaker 1: and off. You can select right. Now, let's just stay

38:25 Speaker 1: with GPL water equivalent thickness, and now you can look

38:32 Speaker 1: at this symbol here. This allows you to drop a pin,

38:38 Speaker 1: draw a box by clicking on the map, and you

38:42 Speaker 1: can select a basin. When you click on select a basin,

38:46 Speaker 1: you can see that all the river basins are now demarketed.

38:51 Speaker 1: And when you point to any of this river basin,

38:55 Speaker 1: it highlights with blue color and it shows which basin.

38:58 Speaker 1: So this is Mississippi, this is Rio Gran and this

39:02 Speaker 1: is Colorado your basin. So let's start with Colorado. This

39:06 Speaker 1: is Amazon and you can you can these are global rivers.

39:10 Speaker 1: Major rivers are shown here, but let's just focus on

39:15 Speaker 1: Colorado River. I'm going to click here to select the

39:19 Speaker 1: river basin and now you can zoom in to look

39:23 Speaker 1: at map. You can change h values here to edges

39:36 Speaker 1: to see proper colors here, and to look at time series.

39:42 Speaker 1: Now you can keep operation as time series and choose

39:49 Speaker 1: the time domain that you want to look at or

39:51 Speaker 1: time range. These are monthly data as we saw, and

39:54 Speaker 1: let's keep the entire range right now and say create chart.

40:00 Speaker 1: When you click on that, you will see time series

40:03 Speaker 1: appearing here. Once you have the time series, you can

40:07 Speaker 1: see monthly data. This is the time access and here

40:12 Speaker 1: you can see terrestrial water storage change is shown in centimeters.

40:17 Speaker 1: The values are fifteen to ten centimeters. And if you

40:24 Speaker 1: see on top here, you have several options. You can

40:28 Speaker 1: de season this data by clicking here. You can have

40:34 Speaker 1: either line or column. You can fit a trend type here,

40:40 Speaker 1: you can have either linear or you can fit polynomial.

40:44 Speaker 1: You can see that not only there is annual variability,

40:48 Speaker 1: but that is inter annual variability as well, and there

40:52 Speaker 1: is a steady decrease in terrestrial water storage as you

40:58 Speaker 1: can see over this period. And this is the polynomial fit.

41:02 Speaker 1: And you can get coefficients here as well. You can

41:07 Speaker 1: download this time series either a spn' G your GPEG

41:11 Speaker 1: image or PDF, or you can download this data as

41:15 Speaker 1: CSV or as XCELS file. You can have this or

41:19 Speaker 1: data table. So multiple options are there, but this allows

41:23 Speaker 1: you to quickly pick an entire river basin and see

41:28 Speaker 1: how TWS is changing over time. Also not to one thing. Here,

41:33 Speaker 1: you will see some between there's a gap between grace

41:36 Speaker 1: and grace fall on, so you will see missing data here.

41:41 Speaker 1: So when you look at maps, it's best to look

41:44 Speaker 1: at where you have the data. Also, once you have

41:48 Speaker 1: the time series, you can move along this xxis and

41:52 Speaker 1: you can see this vertical line. It shows year, month

41:58 Speaker 1: and TWS data for that month. This is for the

42:03 Speaker 1: entire basin, averaged over the basin and when you are

42:06 Speaker 1: clicking through the time, if you look at the map,

42:10 Speaker 1: it will show you map for that particular day. So

42:14 Speaker 1: this is when we find minimum TWS and you can

42:17 Speaker 1: see colors if you see this is mostly in negative TWS.

42:24 Speaker 1: If you go to say January, February or twenty two

42:30 Speaker 1: thousand and five, you can see now this is in

42:32 Speaker 1: blue zone. There is positive TWS animally. So you can

42:38 Speaker 1: look at each individual month and corresponding map here. So

42:42 Speaker 1: average value and map you can see here. So we

42:50 Speaker 1: looked at Colorado basin. We want to now look at

42:56 Speaker 1: Ogula lacquifer and for that we're going to turn the

43:01 Speaker 1: basins off and draw a box. Now approximate location of

43:07 Speaker 1: the Acrefa Gogola Lockwifer is thirty two to forty four

43:11 Speaker 1: north and ninety six two one oh six west. What

43:16 Speaker 1: I'm going to do is you can click here and

43:19 Speaker 1: make a rough box. Then the coordinates will appear. And

43:25 Speaker 1: if you know exact coordinates, you can enter here, or

43:29 Speaker 1: you can just click and draw a box wherever you

43:32 Speaker 1: want to if you're interested in some other region here,

43:36 Speaker 1: I'm just going to put exact latitude longitudes inside. Know

43:40 Speaker 1: what the region I'm looking for, and once you do that,

43:47 Speaker 1: you will see a box appears here the multiple states

43:52 Speaker 1: in Ogola lack Pifer. You can see that, and now

43:56 Speaker 1: you can do the same. You can create chart and

44:00 Speaker 1: this is for that box. Averaged over that box. You

44:03 Speaker 1: can see clearly when you can see that there is

44:10 Speaker 1: a degrees in terrestrial water storage over time, especially after

44:19 Speaker 1: this period. And you can see map also here and

44:29 Speaker 1: then when you go down here there is a slow change,

44:36 Speaker 1: but you can also see that there are this inter

44:38 Speaker 1: annual variability period you can see in the terrestrial water storage.

44:44 Speaker 1: So you can explore this in the area of your

44:48 Speaker 1: own interest. This is just to show the main features.

44:55 Speaker 1: So one more things to see here is that over

44:59 Speaker 1: this box. We want to compare different data sets. We

45:03 Speaker 1: can go back and add let's look at CSR land

45:08 Speaker 1: DWS and you can add the data and now you

45:12 Speaker 1: can see that you can turn this on and off.

45:19 Speaker 1: So this is JPL and this is CSR, so you

45:27 Speaker 1: can compare this. Resolutions are different here. That's why you

45:33 Speaker 1: can see this is three degree. I believe this is

45:38 Speaker 1: half a degree. So you can click back and forth

45:43 Speaker 1: and see this is more smooth. But basic features are

45:47 Speaker 1: the same in both the data sets. But so you

45:50 Speaker 1: can compare two data sets if you like. And if

45:52 Speaker 1: you want to make time series, you will have to

45:54 Speaker 1: pick this data set and go through the same procedure

45:58 Speaker 1: and compare series. So this is the basic browser that

46:05 Speaker 1: we wanted to share with you and you can explore

46:08 Speaker 1: other regions. You have an exercise that is based on

46:12 Speaker 1: this browser. So I want to conclude this demonstration. Now,

46:19 Speaker 1: this concludes our demonstration of the increased Data Analysis tool

46:23 Speaker 1: and you will have chance to work with this data tool.

46:26 Speaker 1: There is an exercise available on the training page that

46:29 Speaker 1: you can download and follow the steps to work on

46:32 Speaker 1: a case study. Before we start the exercise, let's summarize

46:35 Speaker 1: what we saw today we saw that grazed missions provide

46:41 Speaker 1: unique measurements of variations in mass or gravity changes over

46:46 Speaker 1: entire Earth surface, producing monthly maps of the gravity field.

46:52 Speaker 1: These variations in gravity are primarily related to the movement

46:57 Speaker 1: of terrestrial water and they are interpreted in in terms

47:00 Speaker 1: of change in equivalent water thickness or terrestrial water storage

47:04 Speaker 1: t WS. Using the GRACE TWS along with model based

47:10 Speaker 1: hydrologic components, groundwater can be estimated and this is going

47:14 Speaker 1: to be the topic of our next session. Finally, GRACE

47:18 Speaker 1: data are used to obtain t WS and groundwater change

47:22 Speaker 1: information globally and have been useful in monitoring flood and

47:26 Speaker 1: rout conditions and large scale groundwater depletion. Now. Prior to

47:31 Speaker 1: the launch of GRACE, there were no global measurements of groundwater.

47:35 Speaker 1: The only way to estimate groundwater was from well water

47:39 Speaker 1: levels wherever they were available. The GRACE missions have made

47:43 Speaker 1: it possible to estimate TWS and now we have more

47:47 Speaker 1: than twenty years of time series of global water storage

47:50 Speaker 1: data and this is a major advantage of GRACE missions.

47:55 Speaker 1: There's some limitations we should keep in mind. GRACE and

47:59 Speaker 1: GRACE swallow based estimates of t ws and groundwater are

48:03 Speaker 1: available globally. However, their special resolution is relatively low. It

48:09 Speaker 1: is three hundred eighty by three hundred eighty kilometers square approximately,

48:13 Speaker 1: and so it cannot resolve small watersheds. T WS is

48:18 Speaker 1: measured in centimeters or meters, which is much smaller compared

48:23 Speaker 1: to the Earth's radius which is approximately six and seventy

48:28 Speaker 1: eight kilometers, and so TWS has an estimated uncertainty of

48:34 Speaker 1: about two to three centimeters. In addition, gravity change is

48:39 Speaker 1: caused by mass distribution in the solid earth, such as

48:43 Speaker 1: large earthquakes or glacial adjustments. They must be removed from

48:48 Speaker 1: the measurements before deriving t WS thickness, so that also

48:52 Speaker 1: should be kept in mind. With that, our next ssion

48:58 Speaker 1: is going to be about view of groundwater data from

49:02 Speaker 1: a Global Land Data Assimilation System version two point two.

49:08 Speaker 1: As I mentioned earlier, there is going to be one

49:10 Speaker 1: homework assignment that will open on thirtieth of April at

49:15 Speaker 1: the end of the training and it will be available

49:18 Speaker 1: from the training web page. Answers must be submitted via

49:22 Speaker 1: Google forms and the homework will be due by fifteenth

49:25 Speaker 1: of May. A certificate of completion will be awarded to

49:31 Speaker 1: those who attend all three live webinars and complete the

49:34 Speaker 1: homework assignment by the deadline. You will receive a certificate

49:38 Speaker 1: via email approximately two months after completion of the course.

49:44 Speaker 1: Once again, we want to thank doctor Matthew Rodell for

49:47 Speaker 1: his excellent presentation about race missions, data and applications, and

49:53 Speaker 1: he will be our speaker next week as well talking

49:55 Speaker 1: about jailed as peace groundwater. Contact information for doctor Rottel

50:02 Speaker 1: and also you can contact our set anytime with any questions.

50:06 Speaker 1: Our set, website and YouTube links are given here. For questions, comments,

50:11 Speaker 1: or to share how you have applied our trainings to

50:14 Speaker 1: your work or studies, Please email at our set at

50:18 Speaker 1: gmail dot com and join our quarterly newsletter to stay

50:22 Speaker 1: up to date on our latest trainings and you can

50:25 Speaker 1: do that by joining our list serve. Here are some

50:29 Speaker 1: useful resources for your information and we want to thank

50:33 Speaker 1: you for attending today's session. We have a few minutes

50:36 Speaker 1: for our exercise to start the exercise now and then

50:39 Speaker 1: we will have our question and answer session. Okay, so

50:49 Speaker 1: we'll start with the question and answer session and if

50:53 Speaker 1: you have any questions about the exercise, you can email

50:57 Speaker 1: us or we can talk next in next session on

51:00 Speaker 1: twenty eighth alsome. So we'll start with the questions. Question

51:07 Speaker 1: one is that's the distance between both sensors change sometimes?

51:14 Speaker 1: And magic can unmuted and answer the question.

51:21 Speaker 3: Yeah, hi, I'm sorry, I just uh, I was looking

51:26 Speaker 3: at something. Can you say the question more? Oh, does

51:28 Speaker 3: the distance be both sensors change sometimes? So yes, it does.

51:33 Speaker 3: And actually that's you know, that's the that's the key measurement, right,

51:37 Speaker 3: the distance between the two satellites, and normally it's about

51:41 Speaker 3: they're about two.

51:42 Speaker 2: Hundred kilometers apart, but.

51:46 Speaker 3: You know that's sort of the two satellites are free floating,

51:50 Speaker 3: so it may vary from you know, by a few kilometers,

51:54 Speaker 3: so if you tens of kilometers or the course of

51:56 Speaker 3: the mission, I guess, but but yeah, I mean that's

51:59 Speaker 3: that's the key measurement, is the the distance between the satellites.

52:03 Speaker 3: And really how you know they've measured down to you know,

52:06 Speaker 3: how fast is that distance changing? More of a distance

52:10 Speaker 3: range rate we call it, and even like the acceleration

52:13 Speaker 3: of how it's changing.

52:15 Speaker 2: They can look at that as well.

52:21 Speaker 1: Thank you. The next question is how can multi source

52:24 Speaker 1: satellite data be combined to reduce uncertainty in groundwater estimation

52:29 Speaker 1: and what are the limitations of this approach for decision making.

52:36 Speaker 2: Well, so the answer is yes and no.

52:40 Speaker 3: Here uh, the the grade data are really the only

52:45 Speaker 3: satellite observation that provided direct you know's not even a

52:48 Speaker 3: direct measurement, but really a measurement that you can use

52:52 Speaker 3: to understand changes in groundwater, you know, fairly directly.

52:59 Speaker 2: There are others satellites that can give us sort of

53:03 Speaker 2: sort of a.

53:06 Speaker 3: You know, an idea of what might be happening with groundwater,

53:09 Speaker 3: like the recently launched nice Our satellite, which can tell

53:12 Speaker 3: us very precise changes in the elevation of the land

53:16 Speaker 3: surface when an.

53:18 Speaker 2: OKFA is is is dewatered.

53:20 Speaker 3: When you remove a lot of water from the ocfur,

53:23 Speaker 3: the the the OCFA compacts and so the land surface

53:27 Speaker 3: declines a little bit, and we can see that with

53:30 Speaker 3: the nice Our satellite. But but it's not really a

53:35 Speaker 3: one to one. You know, the Earth isn't perfectly elastic,

53:39 Speaker 3: so when you add more water back to the OXFA,

53:41 Speaker 3: it doesn't necessarily, you know, the landsurface sort of bouncing

53:46 Speaker 3: back to where it was before. And then of course

53:50 Speaker 3: there are other observations things like precipitation and solar radiation

53:54 Speaker 3: we can be incorporated into our landsurface model as as

53:57 Speaker 3: we talked about, and those helped you constrain the overall

54:02 Speaker 3: water balance, which then helps us to you know, understand

54:05 Speaker 3: how much water may be entering and leaving the Aquifa.

54:12 Speaker 1: Thank you. The question three is is the Great Satellite

54:14 Speaker 1: Mission suitable for monitoring interest and what stortage changes in

54:18 Speaker 1: archipelagic countries such as Indonesia.

54:22 Speaker 3: Unfortunately no, And the problem there is that the islands

54:29 Speaker 3: are really smaller than the effective spatial resolution of GRACE

54:33 Speaker 3: or Grace follow on, and so you have a lot

54:36 Speaker 3: of leakage of the signal from the ocean into the island.

54:42 Speaker 2: So it's it's very hard to.

54:44 Speaker 3: To sort of isolate the mass changes that are happening

54:49 Speaker 3: in an archipelagalo or the or an island from from

54:54 Speaker 3: the often much larger changes than the ocean that surrounds it.

55:03 Speaker 1: And the next question is how can uncertainty in satellite

55:06 Speaker 1: dare groundwater estimates be effectively communicated to policy makers? And

55:11 Speaker 1: are there probabilistics or machine learning approaches such as measure

55:16 Speaker 1: density networks that can better characterize the multi source uncertainty

55:20 Speaker 1: inherent in groundwater estimation.

55:27 Speaker 3: Well, so I'll take the first part of that is

55:30 Speaker 3: effectively communicating to policy makers. What we've really found is

55:35 Speaker 3: that you have to put everything into their language. It's

55:38 Speaker 3: not enough to say, uh, here's here's a change in

55:42 Speaker 3: trust your water storage, or here's a trend in trust

55:44 Speaker 3: your water storage. You know, they might just sort of say, yawn,

55:48 Speaker 3: I don't know what, I don't know how that I

55:50 Speaker 3: can apply that. So so we we found that it's

55:53 Speaker 3: important to work with stakeholders and policymakers to develop products

56:01 Speaker 3: that are really sort of tailored to their specific needs.

56:07 Speaker 3: And and I think, you know, you're sort of answering

56:10 Speaker 3: your own question here. Of course, you know, we've only

56:13 Speaker 3: just begun to look at how machine learning and AI

56:16 Speaker 3: could help with with improving that communication and developing the

56:21 Speaker 3: sorts of products that would really be more more valuable

56:26 Speaker 3: to these these end users, so that we can ensure

56:29 Speaker 3: that they that they really make good use of the

56:32 Speaker 3: information that's available.

56:39 Speaker 1: Thank you. The next question is does vegetation contribute to

56:43 Speaker 1: these animalies or depletion that we see, for example in

56:47 Speaker 1: the himalay and north of India and maybe the mountain

56:52 Speaker 1: glacier retreat.

56:54 Speaker 2: So the glacier retreat.

56:55 Speaker 3: Absolutely, there are areas in the world where glaciers have

56:59 Speaker 3: been retreating rapidly and that's and there can be a

57:02 Speaker 3: very large mass change signal associated with that. And we've actually,

57:06 Speaker 3: you know, especially areas where there are multiple glaciers, we've

57:10 Speaker 3: we've looked at grace and seen at least the component

57:13 Speaker 3: of the grace trend that we can attribute to to

57:18 Speaker 3: the the mass loss from the glaciers recording vegetation. I

57:25 Speaker 3: did you know a study in that twenty one years ago,

57:28 Speaker 3: and and we found that in terms of vegetation water storage,

57:33 Speaker 3: the largest changes happened actually over agricultural regions, you know,

57:38 Speaker 3: between you know, the time of the times of you know,

57:42 Speaker 3: plant growth to to when they're their their maximum and

57:46 Speaker 3: most you know, most massive to when they're either harvested

57:50 Speaker 3: or they go into sin essence. But those changes are

57:55 Speaker 3: still pretty small. They're basically within the uncertainty range of grace.

58:03 Speaker 3: I'd say it's on the order of half a millimeter

58:07 Speaker 3: per year of equivalent height of water in terms of

58:11 Speaker 3: mass change in vegetation water storage. So it's really it's

58:18 Speaker 3: not something we typically think about too much, and it's

58:21 Speaker 3: you know, if you're interested in that component. It's unfortunately

58:25 Speaker 3: not something that's going to be you could easily detect

58:29 Speaker 3: within the grace signal because there are so many other

58:31 Speaker 3: larger components of the grace signal like the soil moisture,

58:35 Speaker 3: changes in groundwater, et cetera.

58:41 Speaker 1: Great, Thank you so much. The next question is I

58:44 Speaker 1: would like to know what the observation that of grace

58:47 Speaker 1: data is in the subsurface. In other words, is it

58:51 Speaker 1: useful for evaluating for example, animal is in deep confined

58:55 Speaker 1: a prefer systems.

58:57 Speaker 3: Well, there's sort of two answers to this question. The

59:00 Speaker 3: first is that grace is not limited by depth whatsoever,

59:03 Speaker 3: because it's because the grace system is really measuring changes

59:09 Speaker 3: in gravity that are associated with changes in mass at

59:13 Speaker 3: all depths. There's no there's no limit on how deep

59:17 Speaker 3: grace can measure.

59:18 Speaker 2: However, if you're talking.

59:20 Speaker 3: About confined octfers, there's not so much of a one

59:24 Speaker 3: to one between a change in in headed a confined

59:29 Speaker 3: oct for and a mass change. So confined oct for

59:34 Speaker 3: head is really you know, if you mentioned if you

59:36 Speaker 3: put it well into a confined oct for, you're really

59:38 Speaker 3: measuring changes in the in the water pressure in that

59:42 Speaker 3: confined oc for uh and and that cannot be easily

59:47 Speaker 3: translated into a change in in mass storage. So it's

59:52 Speaker 3: it's difficult to to say, you know with certainty how

59:58 Speaker 3: much of a mass change is is how might be

1:00:00 Speaker 3: happening in the in the confined oct for But we

1:00:05 Speaker 3: do know that there are areas, you know, where unconfined

1:00:09 Speaker 3: and and confined octoras are being over exploited and over

1:00:14 Speaker 3: you know, average, over that very large area that you know,

1:00:17 Speaker 3: at the at the spatial scale that Grace would observe,

1:00:20 Speaker 3: we can see mass changes that almost certainly would be

1:00:25 Speaker 3: partly attributed to changes in those confined oct for us.

1:00:33 Speaker 1: To learn that. The next question is also is interesting.

1:00:35 Speaker 1: Has anyone investigated groundwork the storage changes in area experiencing

1:00:40 Speaker 1: mysterious earthquakes not related to fourth zones, like in the

1:00:44 Speaker 1: Great Plain or Louisiana.

1:00:50 Speaker 3: That's a good question. I don't I don't know that

1:00:53 Speaker 3: anyone has done that, and and I'm not sure exactly

1:00:59 Speaker 3: what the linkage would be there. I guess the idea

1:01:02 Speaker 3: is that if you've removed or added a lot of

1:01:06 Speaker 3: water to the to an opfer, that can then either

1:01:11 Speaker 3: lubricate the faults or or cause other changes that would

1:01:16 Speaker 3: then initiate.

1:01:20 Speaker 2: Such earthquakes. But I don't.

1:01:22 Speaker 3: I don't know of any specific research that's looked into that.

1:01:29 Speaker 1: The next question is, how can we design a unified

1:01:32 Speaker 1: probabilistic data fusion framework that integrates multi scale satellite observations

1:01:37 Speaker 1: and hydrological models to robustly estimate groundwater under uncertainty?

1:01:46 Speaker 3: Wow, that sounds like did AI asked this question? I'm

1:01:50 Speaker 3: not even sure how to answer that. This sounds like

1:01:53 Speaker 3: something that you might write a proposal to do. It's

1:01:57 Speaker 3: not something I can I can just answer off the

1:01:59 Speaker 3: top of my head. But certainly, you know, data fusion,

1:02:04 Speaker 3: machine learning, artificial intelligence, those are all things that we're

1:02:08 Speaker 3: beginning to you know, incorporate into how we analyze satellite observations,

1:02:15 Speaker 3: including GRACE and and combine them with hydrological and other

1:02:20 Speaker 3: other models.

1:02:24 Speaker 1: M The next question is from the Philippines. Given the

1:02:29 Speaker 1: archipelagic nature of our country, how useful would GRACE data be?

1:02:34 Speaker 1: Is that a way to overcome the pixel limitation on

1:02:39 Speaker 1: island nations?

1:02:42 Speaker 2: Well, again, unfortunately not at this time.

1:02:45 Speaker 3: It's it's again an issue with you know, islands being

1:02:49 Speaker 3: surrounded by ocean and they're being very large mass changes

1:02:52 Speaker 3: in the ocean.

1:02:53 Speaker 2: And because you know, Grace is not.

1:02:57 Speaker 3: Looking downward like a like you know, like a camera

1:03:02 Speaker 3: or an imager, it doesn't it can't get you know,

1:03:06 Speaker 3: crisply does define a region. Say this is what's happening

1:03:09 Speaker 3: in the region. This was happening outside. Remember, the key

1:03:12 Speaker 3: measurement is the distance between the satellites, and so we're

1:03:16 Speaker 3: very limited in terms of the spatial resolution we can get.

1:03:20 Speaker 3: And it's you know, you just cannot distinguish, for example,

1:03:23 Speaker 3: Philippine Islands from from the ocean around them in terms

1:03:27 Speaker 3: of in terms of the mass changes. Now if we

1:03:30 Speaker 3: you know, we are researching.

1:03:33 Speaker 2: You know, next generation satellite based.

1:03:37 Speaker 3: Grab imagery and there are some you know, potential systems

1:03:43 Speaker 3: that would increase the special resolution, maybe we get to

1:03:45 Speaker 3: the point where we were able to distinguish an island

1:03:48 Speaker 3: from the ocean around it.

1:03:49 Speaker 2: But that's that's at least a decade away.

1:03:58 Speaker 1: The next question is about I think it's about the browser.

1:04:04 Speaker 1: Why are there not river basins in Scandinavia? And we

1:04:08 Speaker 1: check and let you know, I'm just checking the browser.

1:04:12 Speaker 1: Major river basins, of course are there? Uh, they are

1:04:15 Speaker 1: very small river basins, they may not be included, but

1:04:18 Speaker 1: I will check and it might. Your question the next

1:04:24 Speaker 1: question is to what extent can groundwater depletion infert from

1:04:28 Speaker 1: graze based terrestrial water storage animal is act as a

1:04:31 Speaker 1: predictor or amplifier of compound climate extremes such as heat

1:04:35 Speaker 1: waves and wildfires.

1:04:38 Speaker 2: Yeah, I would say this is this is an active

1:04:41 Speaker 2: area of research.

1:04:44 Speaker 3: And uh, you know, when there's you know it say,

1:04:48 Speaker 3: it's more like the thresher water storage when that is

1:04:51 Speaker 3: is lower than you're you know, treasure water stort is lower,

1:04:55 Speaker 3: that means that the land is drier, there's less water

1:04:58 Speaker 3: available to plant roots, so the vegetation is is not

1:05:03 Speaker 3: as as moist as it could be, and those conditions

1:05:08 Speaker 3: make it more likely that you're going to have a wildfire.

1:05:12 Speaker 3: And similarly, when there's less water available for for evaporation

1:05:17 Speaker 3: and transporation at the surface, you know, when they when

1:05:20 Speaker 3: water evaporates or transpires, the air cools and when there's

1:05:24 Speaker 3: less whata are available, there's less that evaporative cooling and

1:05:28 Speaker 3: so of course that can that can amplify a heat wave.

1:05:32 Speaker 3: And and again I think this is the type of

1:05:34 Speaker 3: thing that the people are very interested in and researching

1:05:38 Speaker 3: right now.

1:05:42 Speaker 1: Great, thank you. The next question is again about the brows.

1:05:46 Speaker 1: The data analysis tool in the chart section for gris

1:05:51 Speaker 1: follow on why not replace no data with nuts and

1:05:55 Speaker 1: we'll give that feedback to the browser developer. Next question

1:06:02 Speaker 1: is what does D season mean? So, and that is

1:06:06 Speaker 1: about browser.

1:06:08 Speaker 2: No no, no, I think, I think, I know this is so?

1:06:10 Speaker 2: This is uh is a great question.

1:06:13 Speaker 3: D season means if you have a time series of

1:06:17 Speaker 3: observations from Grace. So let's say you have a time

1:06:19 Speaker 3: series of trust your water storage anomalies from GRACE over

1:06:23 Speaker 3: a particular region of interest. In that time series, you

1:06:27 Speaker 3: will see a natural seasonal cycle. You know it'll be

1:06:31 Speaker 3: there will be more trust your water storage during the

1:06:33 Speaker 3: wet season and less trust your water storage or in

1:06:35 Speaker 3: the dry season. And you know, depending on what part

1:06:38 Speaker 3: of the world you're in that you know that seasonality

1:06:42 Speaker 3: is different to D season means we use a statistical

1:06:47 Speaker 3: approach to basically remove that seasonal cycle so that we

1:06:51 Speaker 3: can then look at the the sort of non seasonal

1:06:55 Speaker 3: variations in trust your water storage. So, for example, well,

1:07:00 Speaker 3: if it's you know, if the average stress of water

1:07:02 Speaker 3: storage anomaly in the region is is you know, plus

1:07:06 Speaker 3: ten in the in the spring, and the averages is

1:07:10 Speaker 3: uh minus ten in the fall. You remove the seasonal

1:07:15 Speaker 3: cycles so that you have you know, basically zero in

1:07:17 Speaker 3: all seasons, and then it's easier to see like, Okay,

1:07:20 Speaker 3: we're drier than normal this spring, or we're you know,

1:07:24 Speaker 3: wetter than normal this fall. It's easier to sort of

1:07:27 Speaker 3: to understand where we are in terms of thrush of

1:07:31 Speaker 3: water stewards relative to the normal for that time of year.

1:07:41 Speaker 1: Thank you. The next question is how how can you

1:07:45 Speaker 1: get the actual volume of d w S in water

1:07:50 Speaker 1: volume rather than water thickness or equivalent thickness.

1:07:55 Speaker 2: That's that's pretty simple actually, just multiply.

1:07:57 Speaker 3: The the the equivalent water thickness by the area.

1:08:02 Speaker 2: So so if you have a.

1:08:05 Speaker 3: River basin that's one hundred thousand square kilometers and you

1:08:08 Speaker 3: have a trusted water storage anomaly of of of ten millimeters,

1:08:14 Speaker 3: multiply the ten milimeters by one hundred thousand square kilometers.

1:08:17 Speaker 3: You'll have to convert the units to you know, to

1:08:20 Speaker 3: probably be you know, cubic kilometers or cubic meters or whatever.

1:08:26 Speaker 3: And then and then that's how you estimate the volume.

1:08:36 Speaker 1: The next question again is about but the browser are

1:08:41 Speaker 1: the data an is tool. I'm interested in the analysis

1:08:45 Speaker 1: tool and I have a couple of questions. The option

1:08:49 Speaker 1: dtarend data. How is it detrend in the data set?

1:08:53 Speaker 1: So it's just fitting either line or a polynomial as

1:08:58 Speaker 1: you can see two time series and you can see

1:09:02 Speaker 1: the coefficients that it finds at the bottom. And if

1:09:05 Speaker 1: you're talking about removing seasonal trend what explained It just

1:09:11 Speaker 1: statistically removes seasonal variations. And the second part of it

1:09:16 Speaker 1: is that JPL water equivalent and CSR water equivalent. There

1:09:20 Speaker 1: are two data data sets. I see that CSR has

1:09:23 Speaker 1: better special resolution. But can you expand on the differences

1:09:28 Speaker 1: of these data sets? So MET can add to this.

1:09:32 Speaker 1: But I did share a link earlier and I'll put

1:09:36 Speaker 1: it here as well. It describes how to choose a

1:09:39 Speaker 1: data set or how you can average available data sets

1:09:44 Speaker 1: and then use that. But MET, if you have anything

1:09:46 Speaker 1: to add to that, please go ahead.

1:09:49 Speaker 2: Yeah, I just want to I.

1:09:51 Speaker 3: Want people to be careful about, you know, what's viewed

1:09:54 Speaker 3: as better spatial resolution.

1:09:57 Speaker 2: You know, just because CSR provides their.

1:10:02 Speaker 3: Their trust or water storage anomalies on a finer grid,

1:10:06 Speaker 3: maybe I forget if it's the zero point five degrees

1:10:09 Speaker 3: versus one degree, the effective spatial resolution revolution of grace

1:10:14 Speaker 3: remains the same. You still have to average those pixels

1:10:19 Speaker 3: over a sufficiently large region for your results to be meaningful.

1:10:23 Speaker 3: So that especially large region is you know, one hundred

1:10:25 Speaker 3: to one hundred fifty thousands core kilometers at a minimum. So,

1:10:31 Speaker 3: but in terms of other differences, you know, they do

1:10:35 Speaker 3: their processing in different ways, and you know, JPL may

1:10:42 Speaker 3: be better in some ways and CSR in in other ways,

1:10:44 Speaker 3: so it's hard for me to say which one is

1:10:46 Speaker 3: better overall. You know, sometimes people look at the differences

1:10:51 Speaker 3: between the JPL and the CSR and the Goddess Space

1:10:55 Speaker 3: Flight Center and the GFZ products and use that the

1:11:00 Speaker 3: spread among those different products as a measure of uncertainty.

1:11:04 Speaker 3: I don't really like doing that very much because I

1:11:07 Speaker 3: think it only tells you measure of the uncertainty in

1:11:09 Speaker 3: the way they process it, and not so much other

1:11:14 Speaker 3: sources of uncertainty, like the the limitations of the satellite

1:11:18 Speaker 3: observation itself, which would be you know, inheriting all the products.

1:11:25 Speaker 3: And I prefer to just focus on just pick one

1:11:27 Speaker 3: and focus on the science, is always my advice.

1:11:35 Speaker 1: Thank you. So much. The last part of the question is,

1:11:38 Speaker 1: I was wondering if these results would be the same

1:11:41 Speaker 1: similar as downloading the mask ons and plotting the data myself.

1:11:47 Speaker 1: I think, yes, that is true, it's the same data

1:11:50 Speaker 1: that's been shown here. Next question is could a grace

1:11:56 Speaker 1: like mission detect temporal mass changes on Mars and what

1:12:01 Speaker 1: magnitude of water movement would be required for detection.

1:12:07 Speaker 3: So so if you know, if there were significant mass

1:12:12 Speaker 3: changes on Mars, then yes, a grace like mission could

1:12:16 Speaker 3: could detect those. You know, for Earth, we know that

1:12:21 Speaker 3: you know, the the uncertainty level for a monthly gravity

1:12:27 Speaker 3: field or trustre water storage derived field is on the

1:12:32 Speaker 3: order of one to two centimeters equivalent height of water.

1:12:35 Speaker 3: So you need to have changes in water storage on

1:12:38 Speaker 3: Mars that were at least you know, one two centimeters

1:12:41 Speaker 3: equivalent height of water.

1:12:45 Speaker 2: You know.

1:12:45 Speaker 3: I don't know if anyone knows this for sure, but

1:12:48 Speaker 3: my intuition is that there's you know, pretty much no

1:12:51 Speaker 3: water cycle on Mars, so you're, you know, very small

1:12:55 Speaker 3: changes in mass associated with water storage. You know, there

1:12:59 Speaker 3: may be water know in you know, frozen in the

1:13:01 Speaker 3: polls on Mars, but if it's not moving, then a

1:13:04 Speaker 3: grace like mission isn't really going to tell you much information.

1:13:07 Speaker 2: I can't tell you, just.

1:13:09 Speaker 3: Like on Earth, they can't tell you the absolute amount

1:13:11 Speaker 3: of water there, can only tell you how it changes

1:13:14 Speaker 3: over time.

1:13:14 Speaker 2: And my guess is on Mars not changing much at all.

1:13:21 Speaker 1: Yeah, I think you next question is can I find

1:13:24 Speaker 1: the water depths through this in a twenty square kilometer area.

1:13:32 Speaker 3: Not using grace alone. You know, if you use one

1:13:37 Speaker 3: of the the GLEDS two point two data Grace data

1:13:44 Speaker 3: simulation based products, those are available on a on a

1:13:49 Speaker 3: quarter degree grid, so you know, twenty five by twenty

1:13:53 Speaker 3: five kilometers, which is not going to eat you down

1:13:57 Speaker 3: to your twenty square kilometers. And you know, as we discussed,

1:14:02 Speaker 3: there are a bunch of caveats in terms of interpreting

1:14:05 Speaker 3: the Child's two point two data.

1:14:08 Speaker 2: In particular, you.

1:14:09 Speaker 3: Know, the model doesn't know anything about you know, groundwater

1:14:12 Speaker 3: pumping and other human water management, so there are some

1:14:16 Speaker 3: fine scale properties processes not captured by the model, and

1:14:20 Speaker 3: therefore it's not it's quite imperfect in terms of downscaling

1:14:26 Speaker 3: the GRACE data.

1:14:29 Speaker 1: Yeah, just to note that next session on twenty eight

1:14:32 Speaker 1: will be about Jilda's two point two and noctre Rotal

1:14:35 Speaker 1: will explain this again. Question eighteen high everyone, I'm asking

1:14:40 Speaker 1: that Grace twenty five kilometer resolution data is available. I

1:14:44 Speaker 1: think you just answered that we will see that next

1:14:49 Speaker 1: week and with some caveats, but yes, Gildas has quarter

1:14:52 Speaker 1: degree data.

1:14:55 Speaker 3: Right, and again that's not purely Grace data. That's yeah,

1:14:59 Speaker 3: Gray data to you know, assimilate into a landservice model

1:15:02 Speaker 3: and integrated with other observations.

1:15:07 Speaker 1: Next question is can you please help me regarding how

1:15:11 Speaker 1: to create or get started to prepare such interactive web browser,

1:15:17 Speaker 1: So that actually is beyond the scope of this training,

1:15:21 Speaker 1: but we will get you some reference material for that information.

1:15:27 Speaker 1: Next question is is it possible to upload our own

1:15:29 Speaker 1: basin in a shape file format or is there another

1:15:33 Speaker 1: way to download data more systematically and more locally. So

1:15:40 Speaker 1: next a week we will this browser will not allow

1:15:44 Speaker 1: you to upload your own shape file, but you can

1:15:49 Speaker 1: download the data.

1:15:50 Speaker 3: Uh.

1:15:50 Speaker 1: The links and information was given in the presentation slides.

1:15:54 Speaker 1: So the data available in net CDF and GOOT format,

1:15:59 Speaker 1: so you can and get them into a GIS QGIS platform.

1:16:04 Speaker 1: You can also specially subset data so your region can

1:16:08 Speaker 1: be extracted and then you can in GIS, you can

1:16:11 Speaker 1: upload your own shape file and do the analysis. So

1:16:15 Speaker 1: if there is interest, we will have a small information

1:16:20 Speaker 1: We will provide information on that. Next question is do

1:16:26 Speaker 1: we need the newest QGS version for the next exercise.

1:16:31 Speaker 1: I believe we're not using any special features, but the

1:16:37 Speaker 1: procedure is based on the newer QGIS version, so when

1:16:41 Speaker 1: you follow the steps you may have to make some adjustments.

1:16:47 Speaker 1: The next question is for identification of a regional signal.

1:16:51 Speaker 1: Can you recommend practices for combining multiple mask ons for

1:16:56 Speaker 1: a single product or for multiple products to estimately W

1:17:00 Speaker 1: S and the uncertainty or of d W S I

1:17:03 Speaker 1: G E G. If you calculate the meantime series, should

1:17:08 Speaker 1: some waiting be applied to de emphasize mascons at the

1:17:13 Speaker 1: edge of the basins or would you say that the

1:17:17 Speaker 1: full range of all mask on across all products represent

1:17:22 Speaker 1: the uncertainty for a region.

1:17:29 Speaker 3: You know, the the mask on products they've already done

1:17:33 Speaker 3: some You know, they put some effort into trying to

1:17:38 Speaker 3: trying to account for for leakage across the basins. So

1:17:43 Speaker 3: so I understand what you're asking, and and you know

1:17:47 Speaker 3: it's possible that you could do a study and show that,

1:17:49 Speaker 3: you know, it's better to to you, you know, wait

1:17:54 Speaker 3: more heavily the interior mask ons than the ones around

1:17:57 Speaker 3: the edge. But I don't, I don't know that's that's

1:18:01 Speaker 3: ever been done. And like I said, you know, the

1:18:04 Speaker 3: mask on products do attempt to to counteract leakage. So

1:18:10 Speaker 3: you know, my general advice is just to use all

1:18:13 Speaker 3: of the equally weight all of the mask ons within

1:18:17 Speaker 3: your region of interest.

1:18:18 Speaker 2: But I'm you know, I'm not certain that that's the

1:18:23 Speaker 2: optimal way.

1:18:26 Speaker 1: The next question is what does water thickness mean?

1:18:31 Speaker 3: Yeah, so we use the term water thickness or equivalent

1:18:35 Speaker 3: height of water two as a sort of a simplified

1:18:40 Speaker 3: way of explaining a mass change. So so if you

1:18:45 Speaker 3: have a mass change it's you know, in terms of uh,

1:18:49 Speaker 3: you know, kilograms of water. We can then say, what

1:18:52 Speaker 3: what would that equate to, you.

1:18:54 Speaker 2: Know, averaged over a region of interest.

1:18:57 Speaker 3: So if we have a you know, grace region that's

1:19:00 Speaker 3: hundred thousand square kilometers, you know, you have a change

1:19:04 Speaker 3: in mass over that region, what would be the equivalent

1:19:09 Speaker 3: height of water that would cause that change in mass?

1:19:12 Speaker 3: And and you can think of it like, you know,

1:19:17 Speaker 3: again grace measures all the different components of trustre water storage.

1:19:21 Speaker 3: So there's the groundwater, the sawmash or the snow, and

1:19:23 Speaker 3: the surface waters. If you took all of that water

1:19:27 Speaker 3: and ponded it on the surface of your region, what

1:19:30 Speaker 3: GRACE is measuring is like changes in the in the

1:19:34 Speaker 3: height or depth of that water. So think of like

1:19:37 Speaker 3: a bathtub, right, So maybe you're you might maybe have

1:19:40 Speaker 3: a bathtub that's you fill a pathway with water and

1:19:45 Speaker 3: then you drain some of the water out and you

1:19:47 Speaker 3: see that you're, you know, one centimeter lower than you

1:19:50 Speaker 3: were before.

1:19:50 Speaker 2: And maybe you don't know.

1:19:51 Speaker 3: The total amount of water in that bathtub, but you

1:19:53 Speaker 3: know you've lost a centimeter of water.

1:19:55 Speaker 2: That's basically what we do with the GRACE data.

1:20:00 Speaker 1: Thank you, Matt. It's a great explanation. And the next

1:20:03 Speaker 1: one is two level three products account for regional earthquakes

1:20:07 Speaker 1: such as the one in twenty twenty three in Turkey, Syria.

1:20:12 Speaker 1: If studying a region like this for groundwater depletion, should

1:20:16 Speaker 1: the impact of seismic events be taken into account.

1:20:21 Speaker 3: So typically what we've seen is an earthquake needs to

1:20:23 Speaker 3: be greater than magnitude eight in order for it to

1:20:27 Speaker 3: have a larger mass change to be you know, to

1:20:31 Speaker 3: really affect the time series in a meaningful way. And

1:20:34 Speaker 3: when there are those large earthquakes, you know, like the

1:20:37 Speaker 3: Sumatra earthquake, then we there are some some specialists who

1:20:43 Speaker 3: work on earthquakes and related you know, gravitational changes that

1:20:48 Speaker 3: have provided data that that the the Grace team has

1:20:56 Speaker 3: used to sort of try to remove that signal.

1:20:59 Speaker 2: It's not perfect.

1:21:00 Speaker 3: It's actually surprisingly difficult to to provide even a mass

1:21:07 Speaker 3: change estimate for an earthquake because because you'll see sort

1:21:12 Speaker 3: of this ringing effect where there's you know, large mass

1:21:14 Speaker 3: change in the middle and then then the opposite sign

1:21:18 Speaker 3: mass change as you uh as you go outward from there,

1:21:22 Speaker 3: and uh, it's it's difficult to to remove that signal.

1:21:27 Speaker 2: But they do make an effort.

1:21:29 Speaker 3: Again for those earthquakes that are larger about magnitude eight,

1:21:32 Speaker 3: that's already incorporated into the products, but it's not perfect.

1:21:36 Speaker 3: So if you see, you know, if if you look

1:21:39 Speaker 3: at a gracetime series around you know, Sumatra area and

1:21:43 Speaker 3: you see some large changes, you know, some of those

1:21:48 Speaker 3: might be real, but a lot of it might be

1:21:49 Speaker 3: you know, part of the earthquake signal that was not

1:21:53 Speaker 3: completely removed.

1:21:56 Speaker 1: Well, that's interesting. The next question is how good is

1:22:01 Speaker 1: the GRASE data if I want to check the correlation

1:22:05 Speaker 1: between levee subsidence and groundwater or is it good when

1:22:10 Speaker 1: analyzing a large area.

1:22:14 Speaker 3: Well, if you're talking about a levee, I mean that's

1:22:17 Speaker 3: you know, then you're on the order of, you know,

1:22:19 Speaker 3: less than a kilometer, right, if I'm understanding the question correctly,

1:22:22 Speaker 3: and you know, again, the GRACE effective spatial resolution is

1:22:26 Speaker 3: about one hundred thousand square kilometers. So so even a

1:22:30 Speaker 3: you know, even a pretty large reservoir, because it's a

1:22:36 Speaker 3: small area, it can be sometimes hard to see the

1:22:39 Speaker 3: changes in water stored in that reservoir behind a behind

1:22:44 Speaker 3: a levee or a dam. For the larger reservoirs in

1:22:48 Speaker 3: the world, as they're filling, you know, for example, the

1:22:51 Speaker 3: three Gorgeous Reservoir in China, we can see that signal

1:22:54 Speaker 3: in the GRACE data.

1:22:55 Speaker 2: But if you're talking about you know.

1:22:57 Speaker 3: A smaller, smaller reservoir or levee, then it's probably going

1:23:03 Speaker 3: to be too small of a signal for GRACE to detect.

1:23:10 Speaker 1: The next question is the tool indicates that the inflammation

1:23:14 Speaker 1: is water thickness, does it mean the thickness of the

1:23:17 Speaker 1: water above and below the ground, is it ignoring the

1:23:20 Speaker 1: layers of soil in between?

1:23:23 Speaker 3: So yeah, I sort of answered this before, But the

1:23:25 Speaker 3: answer is like you take all the water on end

1:23:29 Speaker 3: below the surface and combine it into one. Almost like

1:23:33 Speaker 3: you took all the water and you ponded it on

1:23:34 Speaker 3: the surface, and that's what the thickness of water is.

1:23:39 Speaker 3: You know, changes in that in that ponded water, So

1:23:42 Speaker 3: it's not is ignoring the layers of soil in between?

1:23:47 Speaker 3: I guess the answer is sort of yes, because that's

1:23:49 Speaker 3: the soil layers are not changing, right, You're the mass

1:23:54 Speaker 3: of the soil is not changing unless you're having huge

1:23:57 Speaker 3: amounts of erosion.

1:24:00 Speaker 2: But you know, so it's really just looking at the water.

1:24:08 Speaker 1: Thank you. The next two questions are about the exercise.

1:24:12 Speaker 1: So does this exercise have to be done now? I

1:24:14 Speaker 1: mean in the live session or there is a deadline

1:24:17 Speaker 1: to it. We just gave some time so that you

1:24:20 Speaker 1: can explore the tool and if you have any questions,

1:24:24 Speaker 1: but it's not due today, you have almost between now

1:24:29 Speaker 1: and fifteenth of May when the homework is due. Your

1:24:32 Speaker 1: homework questions will be based on the exercise so you

1:24:35 Speaker 1: have time to finish that later on. The next is

1:24:39 Speaker 1: also about exercise. Yeah, it says month here July. Okay,

1:24:49 Speaker 1: there's a question in the exercise that says to move

1:24:52 Speaker 1: the month here to July twenty twenty one, and then

1:24:55 Speaker 1: it follows with a question regarding July twenty twenty two,

1:24:59 Speaker 1: should we used July twenty twenty two? So the question

1:25:03 Speaker 1: is about It goes from you're checking twenty one, twenty two,

1:25:07 Speaker 1: twenty three, twenty four, twenty five, all years, and then

1:25:11 Speaker 1: you are just then looking at one month, so one

1:25:15 Speaker 1: July twenty twenty two. Next question is could you please

1:25:21 Speaker 1: repeat what the vertical line on the plot was. So

1:25:26 Speaker 1: vertical line just it's the it's the place for month

1:25:30 Speaker 1: and year. So when you move that line along X axis,

1:25:34 Speaker 1: it shows which month and the year you're looking at,

1:25:37 Speaker 1: and then you can see the actual TWS animally at

1:25:44 Speaker 1: that that month and year along that line. And also

1:25:49 Speaker 1: as you move the line, you can see the maps

1:25:52 Speaker 1: changing in the browser, so you can see corresponding distribution

1:25:58 Speaker 1: of DWS animalies. Next question is are there any attempts

1:26:02 Speaker 1: to make to downscape the grace data for smaller watersheds

1:26:06 Speaker 1: or high resolution impact studies.

1:26:10 Speaker 3: Yes, there are, and and that's one of the things

1:26:12 Speaker 3: we'll be talking about with GLEDS two point two next week.

1:26:16 Speaker 3: GLS two point two is when we perform data assimilation,

1:26:20 Speaker 3: we take the grace data when we use it to

1:26:21 Speaker 3: constrain a land service model that incorporates a lot of

1:26:25 Speaker 3: a lot of other higher information, higher resolution information like

1:26:28 Speaker 3: precipitation and solar radiation and and soil types and other things.

1:26:37 Speaker 3: So so the answer is yes here, there's also you know,

1:26:41 Speaker 3: people are starting to look at you know, machine learning

1:26:43 Speaker 3: approaches and artificial intelligence and that sort of thing.

1:26:49 Speaker 2: But you know, none of these are perfect answers.

1:26:52 Speaker 3: You're never going to get, you know, a truly high

1:26:54 Speaker 3: resolution grace only product question.

1:27:00 Speaker 1: I think it's a follow up question to the one

1:27:02 Speaker 1: that you answered about the coastal region. It refers to

1:27:06 Speaker 1: the question about the identification of a regional signal given

1:27:09 Speaker 1: multiple mask ons and products and the uncertainty for the signal.

1:27:14 Speaker 1: In answering that question, please also consider the case of

1:27:17 Speaker 1: a coastal region. I think you covered that right, you mean.

1:27:23 Speaker 2: Yeah, I'm a little I'm a little lost because I

1:27:25 Speaker 2: don't know what the original question was. I'm not I'm

1:27:30 Speaker 2: to answer this one.

1:27:31 Speaker 1: Yeah, I'm not sure either. The question was about River

1:27:35 Speaker 1: Basini that you know near the edge. Would you just

1:27:39 Speaker 1: consider if you're looking at a region, how would you

1:27:44 Speaker 1: wait different mask ons. We can revisit this later on

1:27:49 Speaker 1: a question that you too. Can you plot multiple data

1:27:51 Speaker 1: on the same chart for comparison? I believe that you

1:27:56 Speaker 1: will have to download CSV files from two different data

1:28:00 Speaker 1: sets and then make time cites, either in Excel or

1:28:03 Speaker 1: in some other software. Question thirty three when will the

1:28:10 Speaker 1: present data March April twenty six be available?

1:28:16 Speaker 3: So the Great team has a goal of making the

1:28:20 Speaker 3: data available within about three months of real time. Often

1:28:25 Speaker 3: it may be more like four or five months, and

1:28:28 Speaker 3: it just depends on the degree of processing that's required.

1:28:32 Speaker 3: And sometimes the satellites are in a better orbit with

1:28:37 Speaker 3: fewer repeat pass overpasses, and other times there's more uncertainty

1:28:42 Speaker 3: to deal with. So I don't know exactly the answer

1:28:46 Speaker 3: to that because I'm not one of the product developers,

1:28:48 Speaker 3: but it's typically typically I would say it's on the

1:28:52 Speaker 3: order three to four months after real time, And this

1:28:55 Speaker 3: is actually one of the things we address with GLS

1:28:57 Speaker 3: two point two and data assimilations we can and much

1:29:01 Speaker 3: closer to real time using other information to extrapolate to

1:29:07 Speaker 3: near real time.

1:29:10 Speaker 1: Great, thank you. So currently I think February twenty six

1:29:13 Speaker 1: is available, but then March April or not there yet.

1:29:18 Speaker 1: Question thirty four. I wonder if we have options for

1:29:21 Speaker 1: downscaling this grazed data so it can fit for analysis

1:29:24 Speaker 1: in the archipelago, and whether there are some approaches to

1:29:28 Speaker 1: mitigate the leakage signal problems on the edge of the island.

1:29:35 Speaker 1: I think tot total is answered this question, but meant,

1:29:38 Speaker 1: if you want to say anything more, yeah.

1:29:41 Speaker 3: Again, I mean, unfortunately, there's just not a whole lot

1:29:43 Speaker 3: we can do to get a good result. You know,

1:29:48 Speaker 3: Grace observation over island where the ocean signals leaking in.

1:29:52 Speaker 3: I mean, it's just it's almost impossible to mitigate that effect.

1:29:57 Speaker 3: So it depends on the side of the island. Of course,

1:30:00 Speaker 3: you know Greenland, you know it is a huge island.

1:30:02 Speaker 3: Then we can monitor changes in Greenland. But if you're

1:30:05 Speaker 3: talking about you know, the Philippines, those islands are going

1:30:09 Speaker 3: to be too small for Grace to resolve.

1:30:14 Speaker 1: Right. Next question is can be interprete water equivalent thickness

1:30:18 Speaker 1: data to identify each in regional wet and dry periods

1:30:22 Speaker 1: influenced my flood events.

1:30:26 Speaker 3: Yeah, I mean so so if you have a flood,

1:30:30 Speaker 3: I mean, you use there's there's more water there than

1:30:33 Speaker 3: than average, and so you should see you know, a

1:30:36 Speaker 3: significant positive trust your water storage anomaly. But it all,

1:30:40 Speaker 3: you know, it does depend on what we're talking about

1:30:42 Speaker 3: localized flooding. You know, if it's just if it's a

1:30:46 Speaker 3: small area, then that then then it might be too

1:30:49 Speaker 3: small for grace to resolve.

1:30:51 Speaker 1: Uh.

1:30:52 Speaker 3: And if it's something like flash flooding where it's really

1:30:54 Speaker 3: just you know, heavy rain and water ponding on the

1:30:58 Speaker 3: surface temporarily, that can be less of a signal, you know,

1:31:03 Speaker 3: less detectable than something like you know, continued heavy rains

1:31:09 Speaker 3: or what happens after a hurricane where you've just had

1:31:11 Speaker 3: you know, many many inches of water.

1:31:15 Speaker 2: Rained down.

1:31:18 Speaker 3: Yeah, so it's it's yes with a with a caveat there.

1:31:26 Speaker 1: Thank you. Next question, Please correct me if I'm wrong.

1:31:29 Speaker 1: But since this is a monthly average data set, it

1:31:32 Speaker 1: may have limited usefulness for water logging analysis influenced by

1:31:37 Speaker 1: surface water and groundwater flooding, especially in a country like

1:31:41 Speaker 1: the UK where rainfall events are frequent and highly variable.

1:31:46 Speaker 2: Yeah, that's true.

1:31:47 Speaker 3: I mean it's it's monthly, so so we don't have

1:31:51 Speaker 3: any direct information on what happened within a given month.

1:31:55 Speaker 2: It's only the average over the course of that month.

1:31:59 Speaker 3: And I'm going to talk about GLS two point two again.

1:32:04 Speaker 3: You know, GEOS two x two with the data simulation

1:32:06 Speaker 3: and the corporation of other higher spatial and tempered resolution

1:32:11 Speaker 3: data sets, means that we can look at some monthly variability.

1:32:19 Speaker 1: Yeah. The next question is, I'm not quite sure which

1:32:22 Speaker 1: CEA basin to use for California. I believe this is

1:32:25 Speaker 1: about the exercise. You're actually not looking at any basin.

1:32:28 Speaker 1: You're looking at the state of California. Just look at

1:32:31 Speaker 1: the map to answer the questions. So that's basically you're

1:32:36 Speaker 1: not looking at time seed is you're looking at the map.

1:32:41 Speaker 1: Question thirty eight. Given the course resolution of GRACE data,

1:32:45 Speaker 1: what is the best way to apply it to small basins?

1:32:49 Speaker 1: Is it better to use mask on products or combine

1:32:52 Speaker 1: GRACE with hydroologic model.

1:32:56 Speaker 2: Well, but you know, it depends on the definitive of small.

1:32:58 Speaker 3: So if small or is smaller than about one hundred

1:33:01 Speaker 3: thousand square kilometers, then you know, if you just average

1:33:07 Speaker 3: over the the the grace grids that are available on

1:33:12 Speaker 3: one of the one degree or cattery products. Then then

1:33:15 Speaker 3: you can you can come up with a time series,

1:33:17 Speaker 3: but the errors are gonna be very large because there's

1:33:19 Speaker 3: gonna be a lot of leakage, and I don't recommend

1:33:22 Speaker 3: doing that. It's you know, it's not very meaningful. So

1:33:26 Speaker 3: so the integrated products with the hydrogical models like GLS

1:33:31 Speaker 3: two point two are going to be better for that

1:33:33 Speaker 3: sort of application.

1:33:36 Speaker 1: Thank you so much, Matt. We are almost at the

1:33:39 Speaker 1: end of the hour here and it's the end of

1:33:42 Speaker 1: our webinar time. There are questions remaining that we will

1:33:46 Speaker 1: be answering on the Q and a doc and post

1:33:48 Speaker 1: on the website in a week or so. So we

1:33:51 Speaker 1: really want to thank you for returning to this session

1:33:54 Speaker 1: and we hope to see you on twenty eighth of April,

1:33:57 Speaker 1: where doctor Dorot and will again be talking about held

1:34:00 Speaker 1: US two point two with quarter degree resolution to look

1:34:05 Speaker 1: at daily groundwater storage data. And with that we want

1:34:11 Speaker 1: to thank our set team for help, especially our coordinators

1:34:15 Speaker 1: Anatasha Johnson and Jerry Morris, our helper and editor Maria Marabito,

1:34:22 Speaker 1: Jonathan O'Brien, Arrakashell, our coordinator Prop Levins, and our our

1:34:28 Speaker 1: set team Sean McCartney, Rica Podees, our instructional designer Susan Monty.

1:34:35 Speaker 1: Thank you also very much for your help with this webinar,

1:34:39 Speaker 1: and we hope to see you on twenty eighth of

1:34:43 Speaker 1: April at the same time. Thank you. Thanks Matt very

1:34:45 Speaker 1: much for your presentation answering one of the questions