 Welcome back, everybody. So I hope you all had a good breakout group discussion. I'm sorry, I was trying to say group out break discussion, sir. And now we have the breakout reports back. And we have, as we said, we combined it and made it three groups. So we have Ed, Stephanie, and Ali. And each of them will speak for 10 minutes on the same formats of the PowerPoint slide. So go ahead. Who's first on that? OK, that's it. There you go. Yes, OK. So again, just first, I'm talking for the group. But if anyone from our session has any comments or I misrepresent anything, please speak up. So again, we went through systematically question by question. And for the first one related to challenges and opportunities, the big one that came up was access to data, the in-situ data availability. In addition to the data that is available, the data, the methods, the models, uncertainty and bias. So quantifying and getting a sense of the actual uncertainties and bias with what we have, we think is also a challenge. And then human aspects in their role in the water cycle, characterizing that, understanding that, quantifying that in terms of qualitatively, but also to a degree that could be integrated into various models. And so the first three represent really the challenges. And the next couple of bullets, we focused more on the opportunities. So the first one was more data fusion. So we know there's data fusion activities taking place. But a larger effort put into data fusion with the available data that we do have. Integrating modeling and science centers in terms of more integrated models that capture the adaptive feedbacks from multiple spatial and temporal scales as well as processes. In terms of the physical processes, also the human aspects of these systems, models that integrate all of that is an opportunity. Ability to obtain water quality and quantity via sensors and the internet of things. So the idea is there are lots of sensors, capabilities out there now that aren't huge money. If that's connected to the internet, getting access to that data is something then we could start to do on a large scale. In investment in translating water cycle understanding to local regional policy and management. And again, so the idea is we have various understandings, we have things going on in various places, expanding that to link what we basically do to the policy and the management aspects of things, focusing on that in the future as an opportunity. So does anyone have anything else? I don't know how you wanna do these. We wanna do question by question to just kind of go through as a whole or? Okay, I'm representing the orange and green groups. We had a good discussion. So we, to answer the question, what are the challenges and opportunities for future research? We kind of broke out in both opportunities and challenges and although we discovered that some of our opportunities were the same as the challenges. So first in terms of opportunities, the group felt that remote sensing is really in its infancy in a lot of ways for groundwater monitoring and that as those technologies improve and new ones become available 30 years from now, we're gonna be asking ourselves how we ever monitor groundwater today. We talked about CubeSats, airborne instruments, weather balloons, UAVs, cell phone signals, et cetera. We talked a lot about how to leverage remote sensing data and to compare with models. There are a lot of challenges with remote sensing, uncertainties, biases, how do we get a better understanding of those? We talked about integrating the need to integrate remote sensing and in situ data. And challenges and opportunities associated with those. We talked about improved technology for in situ data. There's more and more remote sensing data, but in a lot of ways the trove of in situ data is not expanding and it's often expensive as well. So how do we collect that data? How do we share that data? Are there standards, et cetera? We talked about EM systems and potentially an Earthscope type campaign, probably in the US. More sophisticated modeling and advances in computing power. We said, talked about the fact that there are just a few people running these large models but how do we do a better job of taking advantage of those advances and share the results with the broader community? We talked a lot about, well, this is large scale model accessibility, the barriers to entry. US groundwater modeling and information exchange, using remote sensing to guide requests for water sampling. So how do we prioritize regions where, rather than looking at the globe as a whole, can we use remote sensing to help us prioritize regions for more focused studies? We also discussed new data techniques. This was kind of folded up in talking about formats as well. There's artificial intelligence, these new knowledge discovery techniques, machine learning, how do we take care of those techniques? In terms of challenges, uncertainties, how do we characterize the uncertainties? Not only in the data, but in the modeling output, biases. The raw data in particular, the INSAR needs a lot of processing. So how do we share that data when it requires a lot of processing and knowledge to understand it? We need transparency in the data underlying the data sets, whether they're direct or it's inferred. We talked about the need for more information on the deep aquifer systems, and components that are really hard to get to. Again, model evaluation is not only an opportunity, it's a challenge. So what parts of these data sets should be used for validation? And then we talked a lot about international partnerships, and that'll also show up in the fourth question on a project-by-project basis. Then one of our call-in folks talked about the sparseness of in situ monitoring in rural data sets. So we are data rich with remote sensing, data poor oftentimes with in situ data sets, and so how do we harmonize those challenges? Go back to the yellow group. Right. Our structures are similar to the blue group. The first few comments are the challenges, and then we talk about the opportunities. So like others, the lack of in situ data was the primary concern that was raised by everyone that relates to groundwater level, pumping rates, stream flow data, and also the frequency and the spatial and temporal coverage of the different in situ data that are available. Scale issues of groundwater extent and other related variables, depending on the basin we are talking about, depending on the data sets such as grace, depending on the communities that we are talking about, there's a lot of scale issues that come into play here. Diversity of purpose of the measurements, why are we actually taking the measurements? What is the goal? What is the audience? And one point that we came back again and again is to that, we are trying to measure an unnatural system. There's a lot of human involvement through all the, in every layer, and there is no way to get sufficient data on the human and societal factors. And I think we talk about that in the later questions as well. One point was raised is that we could look at it as an opportunities that looking at the systems dynamics of groundwater issues and looking at a systems point of view. And then the other major point that was raised was the uncertainties. How do we measure them? Where are the uncertainties coming from and how do we reduce them depending on our knowledge of where they're coming from and how do we incorporate that into decision-making? So these were the major, I guess, challenges as well as opportunities that we can address in our future endeavors. Thank you. Okay, so let's go back to the first group and do the second question. And let's hold all of our comments till the end because otherwise I think I'm afraid we'll run out of time. Okay, so again, what data are most useful in determining the freshwater budgets and what information will be useful to have that we currently struggle to collect? So in this one, again, some of the things, the data that would be most useful, precipitation, water storage, discharge, the close of the water budget. We also talked about the spatial temporal scales and measurements depend on the questions being asked and the system complexity. So depending on, again, what you're asking, where you're asking it, the data that we need change, also the spatial temporal resolution of that data changes. And again, the second bullet is really getting at the sort of the built human system. So getting data on the water usage, you know, how much you're taking and then the consumption aspect of that. So maybe you're taking 100,000 gallons per day or something and you're only using 80% of that or something. But there's the usage and then the actual consumption, various transfers out of a system. And then there's losses within a system that we really have very limited data on. Again, I think one of the other groups talked about this a little bit for bullet one, but the dynamic aspects of the water budget and the interactions and impacts with infrastructure. So looking at a system, at a particular snapshot in time tells you something, but some of these systems change dramatically based on season or there's major trends taking place now related to potentially climate change or again, human activities in a region. And then we also talked a little bit about extremes. So when you have an extreme drought or flood, that's something that it'd be more nice to have more data on. Again, the spatial temporal scales, we talked a little bit about, it kind of relates to the first bullet, but you have scales that, you know, the farmer scale, the individual field, the individual stretch of river to the watershed or the climate scale. And again, depending on what you're asking, you really have very different scales to think about. And then information on human decision-making related to economics and impacts on water budgets. So there's the financial piece driving a lot of these things and decisions are being made and then that's ultimately impacting the water budget. And so having that connected piece, understanding how the economics come into play to the decisions that are made. And then groundwater in general, and then this point was brought up, you know, depending on your interest in the groundwater, is it a store or is it a flux? So like what's your interest in groundwater kind of changes the way you look at the system and what you're trying to capture. Again, if you're thinking of as a, you know, it's a resource that's just there or you really focused on the fluxes in and out of it to balance whatever it is you're working on in your system. So that's all we had for that. So we came up with some very similar responses. For us, the primary fluxes are most important, precipitation, et cetera, in terms of data. We also felt that we needed to have a better understanding of the human industrial and irrigation use, not so not just the natural system, but we also need to have more information about how data are actually used. We talked about the need for 3D data and how is that integrated into models, again along with point data or other data. We need these hydrogeologic subsurface variables. We did talk about, and this sort of carried on from the previous discussion, about prioritizing on a global scale. And we, the group felt that the priorities depends on the process that we have to understand the need in order to prioritize data. We talked about understanding large scale data systems and about closing the water budget, but there was one dream, I forget who said that, but we don't have everything without isotopes. It is important for helping us to understand residence time for modeling studies and assessing sustainability. We talked about the need for subsidence data that would, and other remote sensing, this is, again, back to the previous, that could give us some indication of where we prioritize studies. Again, this is similar, or a frame we've heard from others, the importance and the need to have data-driven models that integrate both in situ and remote sensing. We talked a lot about where the data go, and so where do we store the data? Is it, there's, with some discussion about universities perhaps storing the data, we talked about how FAS serves data sets, can FAS or USDA serve data, and there was an example that was given in terms of storing data, the quasi-example was one, but there was the USGS example that was, or not USGS, I'm sorry, it was a seismic community has developed a repository called Iris Pascal for seismic data that could be a model for groundwater data. And we talked about how important it is to pursue international partnerships in order to increase access to data globally. In terms of data that are most useful in situ data came up, water quality data that was one issue that we definitely thought was very important, soil data and then all the land surface, I mean we started writing all the variables, but then all the land surface and hydrological data that is, that variables that are there, but water quality came out again as very important because it's very set specific and local level contaminations and biogeochemistry and the dynamic nature of it that remains as a key hurdle. One question that came up was that how much in situ data do we really need? How much do we need to complement the remote sensing data so that we can validate it as well as think that we can move forward and continue with our studies. In terms of that information that would be useful is maps of groundwater recharge, rates of recharge, well locations that came up that could be a very good product that could be used to assess freshwater budgets and then training data for new techniques, so machine learning and all those new techniques that are coming up, where do we get the training data for those from so that we can develop and validate and sharpen these new techniques, that's it. Now to question three please. Okay, so what aspects of freshwater balance beyond precipitation and SWE data would benefit from NGA resources? So again, there's lots of stuff out there we focused on some of the things that if resources were allocated, these are the priority areas. So again, some of the things that are just difficult to measure, river discharge and I have degree of regulation which has a lot of stuff that follow here, but the idea is we have data, it could be river discharge, it could be groundwater data. Knowing more about the system for which the data was collected in terms of if it's a well in their pumping water, is it used for irrigation, is it used for municipal, what's the purpose of the data, is it a natural system, is it a managed system, a regulated system? We get this data, but we don't often know the system context for which the data is used. So a focus on that aspect of data. And again, some of the things that, basically the list of remotely sensed data products that are out there except the addition of water use and then bringing it all back to characterization of the hydrologic variables, processes, the system characteristics, we often think about data products as the quantities that we're interested in but having the variables, the parameters that go into the model would also be of value. The natural language processing of historical data, so again, thinking of ways to get access to some of the data that exists that is just in a different format. And then it's also got us thinking about a centralized data storage location. Again, we talked about Kwasi or other places but having a location where everyone felt comfortable having the data that again is often not shared for various reasons, that's gonna be a sensitive topic but having a place for that could be fairly useful. And the one example was the disaster index insurance related to water, I don't know why I have sources but I was thinking water hazards here. There are places where you can go to for earthquakes and if there's an event, someone tells you there's a database central location, insurance companies pay based on what that source says happened during a time. Maybe there's something related to groundwater that could be developed, something similar to that. A community-abled cyber infrastructure example was like EarthCube and development of potential technologies or actions that could help understand or manage the water budget. So again, what could we add to the system that would help us characterize it, maybe more real time and improve the way it operates and the technology side of things in terms of monitoring as well as the ability to change the system. Oh, sorry, lost for a second. So one thing that we, well I heard somebody say funding but it didn't show up on the list. We talked about potentially as a steward for airborne campaigns. That might be difficult in other countries. So where possible gives us that high resolution data. We also wanted thought that data on human water usage and infrastructure is critical, reservoirs, et cetera. Irrigation use if available. The parameters that are used in the models are absolutely essential and it can be in areas where data are sparse, they can be difficult to get so that would be very helpful. Observations of water levels and storage, declassified water data if it's available. Partnerships to improve instruments or models. So partnering with NGA for example. Changes in drinking water supplies, especially near cities. We talked about having an understanding of mining effects and effects on communities. We have examples where it's been very difficult to get river discharge data. The Nile is one, I think the current data is eight years old so current data, discharge data from the Nile from the Ganges and it's from other rivers. Data on different scales is important, especially local scales, weather data, underground structure if possible and this is related back up to the human water usage and infrastructure but where it's possible metered irrigation data. So we looked at where the new types of activities or data sets that where NGA could devote resources for and we talked about drone based measurements to cover for in situ data sets. Improved computational resources and image processing such as going through Landsat for all the well locations that used as an example. Metadata standards, so metadata on what sort of data we might ask from NGA or the types of data NGA could share with other agencies or with unclassified data that could be shared with academics and other institutions. It could, NGA could facilitate academic research collaborations that way we could exploit or work on the existing remote sensing data sets to use for groundwater based studies and interdisciplinary collaborations such as NGA already works with Department of State on the Worldwide Human Geography Data Working Group. So projects like that NGA could sponsor more to work with other agencies. The last question. All right, so what are some examples of the successful collaboration opportunities and what are the promising partnerships that could help advise our understanding? So again, from the collaborative side of thing a real focus on new funding possibilities for interdisciplinary research and this is just pointing to some existing programs that focus on the interdisciplinary aspect but again, thinking beyond the groundwater system of really bringing in the human aspect to that. And so studies that are bridging both the physical and the human aspects of these systems. And I think for most of these we just kind of listed out that again, the big piece was a focus on interdisciplinary research. It allows you to look at both sides of the problem and then the rest is just potential partners that seem obvious for this type of work at a global scale. And then again, the last piece was again, bringing in the insurance world is also quite vested in a lot of these things. And this is another avenue for potential collaborations that go beyond just the scientific or the nonprofit groups. So we talked about a number of different examples and some promising partnerships. For in terms of examples, USGS had a project in Brazil mapping irrigated land, which was quite successful. It was an example of a successful collaboration. The advisory committee for water information has a subcommittee on groundwater. And that I think could be a potential partnership as well as an example of interagency coordination. The National Groundwater Monitoring Network is an example of collaboration and something that we could contribute to. There was an example of a partnership in Israel. Working on groundwater and surface water. And this was through USDA. USDA also has relationships with Jordan, China and Britain. USGS had some examples of collaborations in a number of different countries where they developed a groundwater model. I believe that was the Nubian aquifer example. The NASA Applied Sciences program works with a number of different agencies, both in country as well as internationally. And I think the other group also brought up Applied Sciences, NASA-Serveer is another example, NASA-USAID-Serveer, I should say. We talked about the Infuse Project where researchers write proposals and get funded to do their work. Signals in soil is USDA. It's that's in partnership with NSF and both can fund international activities. NSF Pire, Coasts and People. We're not sure though that groundwater could be tied to these. The USDA has a new water quality sub-priority and they are funding proposals and groundwater may be underfunded. This is called the Agricultural Food Research Initiative and I think they're due August 1st, is what I heard. Yes. And then lastly, the USGS is undergoing a modernization effort, the National Water Information System is becoming modernized and that's a potential for partnership. Our list is much smaller. There were a few successful examples that were talked about, the FAO Water Accounting Project that was brought on as one of the successful examples. The NASA collaboration with the UN Environmental Groups, especially the UN SDG, the Sustainable Development Groups, Sustainable Development Goals work, I think under the geo banner, as well as the other geo, the Group on Earth Observations Project, the Geoglows on Global Water Sustainability. And the GRACE program was brought on as an example of similar successful work where gravimetric measurements have been used successfully for groundwater measurements. So those are the breakout reports and as you can see there's many similarities and I mean, thankfully there's no contradictions.