 My name is Kathy Kling and I'm chair of the water science and technology board Welcome everybody this morning. So what's going to be a really exciting joint effort of the water science and technology Monology board and whatever BASC stands for Our close sister board No, so first we're going to do some business Yeah The first piece of business is for me to learn what that stands for And no and first is to do some introduction. So what we want to do is Everybody in the room. We're just going to go around. Please say your name your affiliation in terms of which board you're with and maybe your home will your your day job and we'll go around quickly we do have about a Great number of people online So when you when you speak you do need to put your your speaker on we will not do names for People that are online just get get to time consuming but please do put your your speaker on so Let's start on this end Stephanie Johnson staff with the water science and technology board Every Joseph University at Albany BASC Rob Dunbar Stanford University BASC Bill Gale global weather corporation BASC Dave Titley Penn State BASC Okay, what luck consulting engineer from Chicago water science and technology board I'm Mark. I'm also on the water science technology board Mary Glock and BASC the weather company IBM Dave Wagner water science technology board retired scientist Jonathan over back University of Michigan board at atmospheric sciences and climate John Arthur state geologists Florida director of Florida Geological Survey was to be Nick Keener Duke Energy Corporation BASC Terry Hogue Colorado School of Mines BASC Let's say the director of the water science and technology board Ravi Shankara BASC From Colorado State University Shu Chen University Miami University, Washington I recently moved BASC Amanda Stout director of the board and atmospheric sciences and climate staff is back David said like you see Berkeley water science and technology board I'm Jonathan Martin. I'm at the University of Wisconsin Madison, and I'm on the BASC Margaret Palmer University of Maryland and National Society Environmental Synthesis Center, and I'm on the water science Pam M. Schnerzer Grumman BASC Peter from Hof Union of Concerned Scientists BASC David Halpern Jet Propulsion Laboratory I'm there the a zone to BASC from the Ocean Studies Board Dave Duzan back Carnegie Mellon University with the water science and technology board Allison Steiner University of Michigan BASC Wendy Graham University of Florida water science and technology board Ruby Long Pacific Northwest National Lab BASC Dwayne Wallister the Jet Propulsion Laboratory BASC I'm Lauren Everett with BASC staff caught me off guard Mr. Corey Hummel from headquarters US Air Force Joe Burns the USDA Forest Service Office of Science and Sustainability and Climate April Melvin with BASC staff Maudou Khan NASA Earth Science Division Katie Thomas BASC staff Jesse Carmen Noah's Office of Weather and Air Quality Bonnie Brown Noah OAR Office of Weather and Air Quality John Enfante Noah's Office of Weather and Air Quality Mike McCracken Climate Institute Grant Davis with Sonoma Water Radger's University Andy Robertson from the International Research Institute for Climate and Society Columbia University David Erath Bureau of Reclamation Janine Jones California Department of Water Resources and Western State Water Council Andy Miller AMS Jessica Mormon Department of Energy Climate Environmental Sciences Division Erica Brown Association of Metropolitan Water Agencies Bob Hirsch hydrologist recently retired from USGS Laura Samonsky Geological Society of America Anurita Mariotti, NOAA Research Climate Program Office on the Details to the National Weather Service Rahai Kimnevar, USDA Forest Service Laura Ehlers, staff with the Water Science and Technology Board Dave DeWitt, Director of NOAA National Weather Service Climate Prediction Center Ari Gerstmann, University Corporation for Atmospheric Research National Center for Atmospheric Research Emily Remel, National Association of Clean Water Agencies I am Laurie Deller, I'm with the staff of BASC Is that it? Hi, I'm Peter Colahan from the Office of Water Prediction at NOAA Carly Brody, Water Science and Technology Board Okay excellent that was actually a test I would say an A, a minus So almost everybody used their mic correctly and everybody knew where they worked So we got a good start on the day, very good Okay a couple more logistic things This is going to be recorded just so everyone is aware that this is being done Afterwards we've asked speakers to talk this morning for 25 minutes We're going to stay pretty close to that so that we have 15 to 20 minutes of time to ask questions In terms of doing questions if you're in the room and at the table Please put your sign up and I'll keep a list of the order If you're online there is a way through Zoom that you can post questions We have Amanda will be watching and collecting those questions and to the Depending on how time goes we will get to as many of those as we can So and also when you do ask a question, we're going to ask that you just say your name and where you're from Again because for people online to know to put a name and a face together Okay, I'm going to now turn it to Elizabeth for a couple more questions and then we'll be able to really kick this off Good morning, everyone. I just have a couple of housekeeping announcements and and very importantly our safety announcement as well Because you all are our guests here today We want to make sure that you have an enjoyable day a safe day a fun day and a comfortable day If we do hear an alarm sound What we need you all to do is take your very small items not the suitcases very small items With you and exit through the designated doors Many of you came in to the doors off here to my left That's one good way to exit the building and going out The way you came through the great hall and then out through the doors The main doors of the building onto the constitution album inside those of you who are sitting on this side You'll also see a door where underneath the clock. You can also get out to the the front lobby that way You'll exit through there and down a short flight of stairs All of you would very much appreciate trying to squeeze Many people through a very small opening Versus many people through two large openings into a larger space. So try to Gauge the door space and and where people are headed and just exit in a calm fashion our Assembly point is actually on constitution avenue, which is over to the the window side here So we just exit move away from the building down to the sidewalk and wait until we were told to re-enter the building As far as other parts of the day we will have refreshments out out in the the area Next to the room here Lunch as well will be provided everyone is welcome to join us for lunch The restrooms are also located directly across from this The this little atrium Here if you if you need to use those we have breaks, but please feel free to to pop up and and Get something to drink or refresh yourself if you need to do that As as Kathy said this this will be recorded We would like to try to post the recording if we can Subsequent to the meeting and we would let you all know When when that that recording was posted so with that I'll turn things back over to Kathy and Robbie Thank you those of this And thank you all for coming. We we have a really exciting day I'm just gonna make a couple of really again welcoming remarks This is a joint activity of the board on atmospheric sciences and climate And the water science and technology board We feel that this is the kind of activity We should probably be doing more of joining our board's intellect and efforts Learning from each other one of the ways that That I've started to learn to think about things is new terminology to me and that is the is horizontal water and vertical water And our board is sort of the the horizontal water the vertical water is That stuff that goes up and down in the air, which I don't really understand so Seriously, I think that this is an extremely important and valuable contribution and today we have an amazing group of people To really give us some insight into the frontiers of some very important work in terms of predictions And modeling to help us understand where seasonal subseasonal Predictions are going what kinds of research is happening and how that can really be used For impact on the ground and the various Agencies so welcome To you i'm going to turn it to ravi now for a few more comments and then i'll be moderating this morning And i am going to be kind of tough on people so please do try to keep us on time I will Wave and look angsty if it starts getting Getting long because I do want to respect everybody's everybody's time in here. So with that ravi, please Thank you. Kathy I think Kathy covered some of the key points I just want to add a like about five Quick points from my perspective and that of basque um I haven't checked with basque members. Hopefully they'll agree with what i'm going to say If if you don't please tell me off in the break rather than right now Okay, um, you know what really matters to the country and the citizens is how life can be better for them And what various organizations do for them? But science and technology needs In-depth understanding and expertise that essentially means that we're kind of siloed in how we do things in any ways But the real world issues require cross-disciplinary work to make things work This is why nas And nrc need to go beyond our expertise areas and this is a one such step In kind of bringing together expertise in related issues um In in dealing with some important things specifically what? So I think nas is working together and it's doing a good job for the most part But really is in the initial stages We don't have as many of these kinds of meetings as they should we had a couple of others before I'm I'm sure way before my time, but Um, it's a good start and water in particular is a very important issue um It's the center of life as you know And most of you probably don't appreciate water as much as you probably would if you're from a country where I was born like India Water is life If you think about the global Kind of sustainability development goals water is at the center of many of them In many nations and many other parts of international issues are about water The big problem about water is even though we have a big ocean as you all know is having water when you need it what and um Where you want it not too much not too little either of which can cause you problems So this is kind of what makes water and such an important issue, especially fresh water um And this is a very good opportunity for us to come together to discuss issues related to water which is affected by climate meteorology weather Along with other things that happen. We're talking the vertical water as well as the horizontal water So with that, um, show you do you want to add anything? Thank you You said no, okay. Thank you in that case catch it over to you. All right Wonderful. Thank you very much. Well with that we'll go right to our panelists um The bios for all the panelists are in the program So we're not going to take time to do a bio introduction I'd just like to turn it right now to our first speaker andrew robertson And if you just maybe a one sentence to kick things off of who you are and where you're from just to remind people And then um, we're really looking forward to your presentation Okay, so uh, yes, i'm andy robertson from the iri. I am the head of the climate group there And i'm a co-chair on the international World weather world climate research program vest to s s to s project So that's probably why i'm here to win to you. So thanks so much for the opportunity for To to present here is a great honor for me And I think it's just wonderful to see this joint session Of the two boards on on on emissary sciences on on on water And i'm focusing on this topic of s2s And it's it's exactly the kind of thing that we're thinking of in in in this international s2s project so I think there's so many opportunities and i'm thrilled and thanks for so many people from the outside So coming coming and being part of this and and online too So I thought I just flashed this one up this slide up. I'm sure you're all familiar with this uh, let me Yes, I the the report uh, that the national academies Came out with a couple of years ago on this on this topic and I thought it was it was great to see this vision that was that was put forward there by by this This panel that put this this report together and I guess they wanted to get ambitious I put this here in in 10 years time the vision is that these s2s forecasts will be as widely used As as weather forecasts are today so, uh, I think you know This this is something that maybe this is possible And lots of work to do this and so they put in this this graphic some of the things that you know Needed to get there and these are some of the things I'll talk about also in the s2s project How do we increase the skill of the forecast and improve early warning of events how do we you know improve improve the models improve the forecast and really connect Through products through how how we connect the outputs from these these atmospheric forecasting models with with with user needs What I thought I should just put up, you know What is s2s here in the subseasonals a seasonal prediction project of the wmo we we defined this as Roughly two weeks to a season so between the weather forecast and a seasonal forecast in this academy report It's slightly longer out to 12 months sort of so sort of it's slightly gray Including the the seasonal forecasting and you can see that many of the the same sources of predictability really overlaps here. So It's well motivated. So I'll talk about Just to set the scene in terms of weather and climate prediction Then fill you in a bit on on updates from from this international s2s project And say a few words about on on the on the water context Okay, so I'll start with a slide of Dwayne's here that I think nicely encapsulates Where s2s fits in this sort of gap between Weather forecasting we all know weather forecasting out to roughly roughly a week 10 days in advance uh, you know roots going back to uh At the beginning of the 20th century We're we're I think it's fair to say we're pretty good at this with there's a good Especially in the middle latitudes. We know how to predict the evolution of of baroclinic waves Then on the seasonal time scale we've been doing this to some time too since the mid 80s or so in terms of Seasonal outlooks these are much more probabilistic What we can say about the season as a whole In terms of likelihood of it being wetter or drier than normal warmer or cooler than normal But what can we say and and we've got a good source of predictability there in terms of ENSO So here you can say, you know, we've got we our source of predictability is a baroclinic ways and the middle latitudes Here at ENSO, but what about in the middle? What can we what why is it that we have? The situation where we we we see our our weather forecast on on the tv like this And if you go to various websites like like ctc or iri or many centers around the world You can find these seasonal outlooks, but you can't find much in between. But why why is that? So here's my little little take on it on this gray area in between here in in the sub seasonal timescale so the weather is more or less an initial value problem of of forecasting this you know baroclinic wave development development whether whether development or you know easterly waves in the tropics atmospheric African easterly waves for example On daily timescale the climate is much more a boundary value problem for the atmosphere point of view How do the sea surface temperature anomalies or or other surface anomalies impact on the atmosphere? And they they can tilt the odds in one way or the other or as you go out to longer timescales atmospheric composition As in the middle here Then it's really a mix of this Initial value problem and boundary value problem, which is you know makes it somewhat more difficult And then maybe more sources of predictability coming in here one thing that's that's a Really shared As you get away from the daily weather timescale If this roll of I get into the more climate timescale is the role of time averaging So it's a key thing when we when we think of the seasonal forecasting We're typically averaging over three months because we need to average out the the the weather noise and just see How the the sea surface temperatures are tilting those odds But as we go down to the sub seasonal it's also we're thinking there as well In terms of this sort of predictability of the second kind that you can say something about the characteristics of the weather But it starts to become much more probabilistic. So we may not be averaging over three months, but we're still averaging over some Some period or we're looking at well. What are the statistics within a week for it, for example? This is a slide from the The NOAA NOAA's modeling and analysis prediction and projections program. They have a they have an initiative on on research to on on s2s A prediction task force and they put this nice Nice graphic together showing all the just the complexity Really of all that the source has a predictability that come into play on this on the This intervening timescale between weather and climate. So You still have ENSO, but you have other things coming in the madman julian oscillation Maybe the stratospheric polar vortex the nao teleconnection patterns, but also surface memory of land surface conditions, maybe ci things like that modes of atmospheric variability to do with to do with the atmospheric Atmospheric rosby waves So it's a more complex situation in terms of in terms of the modeling and prediction perhaps than we have In on the weather and the the seasonal seasonal timescale How are we doing in terms of forecast skill on those scales or across all the scales So I think this is so I put this one up here showing what we the way that people Describe how well we're doing for weather forecast Typically tends to be something like this Well, the will they look at anomaly correlation of 500 500 millibar geopartential height An anomalies 12 months 12 months running means and we'll tend to look at something like this An evolution. So it's something very broad scale And show how this has been improving over time. So For the the 10 day forecast today are you know where we were maybe Back in the back in the 80s from the seven day forecast, but it tends to be in terms of something like geopartential height Where's the seasonal forecast? This is the way that that will be characterized. This is something from the iri Really much more from a user point of view this is The skill in terms of how much what would be the rate of return on on using such forecast and one sees regions where it's good and regions where it's less good So what, you know, we're we're what what happens in between? this is from a an earlier paper of ours just I think gives a nice flavor of Going from across the weeks I said that you know on on the sub seasonal timescale. We're more more interested Our targets get shorter than for the the seasonal one where we're looking at three month averages So I'm showing here just weekly averages of precipitation skills from the ECMWS model and you can see lots of Red means good skill. You can see in that that first week The information from the initial conditions is very strong But already at week two, you're losing a lot of that But you can see there are some red areas that persist through the four weeks And they're not they're not everywhere. You can see the region here it's the The role the signature of Enso that's coming through even on the sub seasonal timescales But then also you can look you see this this region around the indian ocean western pacific That's actually the the mjo that's coming through there So as with seasonal forecast with sub seasonal it's uh, it's not it's not it's not everywhere that we that we can expect to have skills This is zooming in more over the us in terms of uh This ECMWS model again precipitation skill at top winter spring summer And and fall at the bottom Here's precipitation temperature in the middle and geopotential on the right So the main thing I want to point out here is that across the variables again There's much there's much higher skill in geopotential It's also regional. There are there are regions that have better skill than others and seasons that have better skill Tends to be more skillful in winter for example in precip and I thought I'd just show a result here. This is from the sub-ex project that's Another sub seasonal to see that's really the sub seasonal to see an initiative funded funded interagency wise in the us They it's showing here uh A result of looking averaged over the over the us at some of those scores And they're showing here. This is a multimodal ensemble. They've put together a database also of many many models here and showing that the multimodal ensemble is actually improving on any individual model, which is something we've seen On the seasonal timescale So I'll say a few words now about this s2s project of the wmo It's it's goals to improve the forecast skill And understanding on on the sub seasonal to seasonal timescale with special emphasis on high impact weather events promote the initiatives uptake by operational centers and exploitation by the application community and really to capitalize on on expertise in the weather And climate research communities. So I think that's a big opportunity here That we have in s2s is really bringing together You know expertise and different methodologies that have been used both in in modeling Both in in post-processing verification and things like this in the weather and climate communities to see how we can What should really be done on these intermediate timescales? so this project Started about five years ago. And so we're just coming to the end of the the first phase of it and one of the the main thing has been that's been creation of a of a database of 11 global producing Center wmo global producing center model. So this is a weather services around the world Not all these centers and they've been archived at ecm wf tma in china And also we have them at the iri now as well We have the full set of hind cars we forecast over past years As well as the the forecast in real time But these are delayed by three weeks before real time behind the real time That's an important point because some of the some of the of the operational interests of the center There's a strong linkage in the s2s project with the wmo's operational armor with their commission for for basic systems And so the idea is to to that they can get access to this database without any Without delay and develop products that then can be shared with the national So the way that the research was organized in the in the s2s project in its first first five years was to identify all these kinds of issues that were also in that that That first first graphic that I showed in terms of the various modeling issues What do we need to do to improve the forecast through the models? How do we initialize? How do we Generate the ensembles because ensembles are Are generated typically in in these models from from the weather from the weather point of view What resolution do we need? Do we need to what's what's the role of ocean atmosphere coupling things like that? uh What are the main sources of predictability? And how can we in this this last column here? How can we connect? with with that application And some of these topics here Teleconnections mad and julian oscillation. That's been a big one monsoons, how can how can? African Met Services making for these what about extremes and how to How to document how well that we're doing with these forecasts? I think quite a bit of There's been a lot of progress in in the first five years Putting that data out there has been a huge catalyst For people to be able to actually, you know, look at how well about the skill of these four classes So just a couple of a couple of results from the project Mjo prediction, that's where that's really what I think you could say led to the research research resurgence of interest In this intermediate timescale and why there wasn't anything you could say before is because those sources of predictability Specifically, you know the mjo the stratosphere were not well treated by the models and a big change has been that the the mjo is now much better better represented and much better forecast in models And this has important teleconnection. So also important to the middle attitude So it's just showing that actually you have Good skill out to three or four weeks in many of these models in mjo prediction and then this translates into into things like Things like North Atlantic oscillation teleconnection pattern But to say, you know, one of the challenges is there are still, you know Systematic errors and biases in these patterns So in order to be able to really capitalize on how well you can predict the mjo You have to capitalize on that in middle attitude. You also have to go to get the teleconnections right the stratosphere There's been there's there's more and more interest in that and how For example, southern stratosphere straight warmings can Boost the skill. So this is a case where where you have a weak vortex. This is a normal vortex Giving giving you enhanced skills from that several weeks later So just to tell you a little bit about the the second phase of the s2s and I obviously I don't want to go Through this in too much detail. How am I doing for time? Actually Five moments. Okay, I just say that we we did a kind of gap analysis to see well, you know, how people been using the how people been finding the The results of the s2s project so far and what should we focus on? And this is things like a frequently mentioned gaps land surface processes ensemble generation Initialization some of the things like this We need more faster and access to a popular suite of variables like Like like weekly averages things like that We need more calibrated products might more work needed on on products Skip along. We asked people in user sectors and they said, uh, well Accuracy of the forecast is still lacking. Uh, we need more post-processing. We need to be able to better connect the models with using by product development with user user relevant variables So for our plan for phase two, which is actually kicking off at the beginning of january next year So this is all very timely for that. I'm really looking forward to your your the input today So for the feedback onto our plans in the second phase of the of the project To enhance the database new research both I in particular The roles of ocean and land and sea ice land surface stratosphere atmospheric composition at the aerosols ensemble generation and enhancing the operational Infrastructure and really going to much more toward toward application but I thought I would just say one thing about the land surface and I think that's a common common topic today With with with the with the water board. We have a sub project on this This is from called del maya and some of the the phase two questions will be What's the impact of the observing system on land land initialization? Can we better initialize the land? How well are the coupled land ocean land atmosphere processes represented in the models? And how might land anomalies in particular contribute to to extremes and it's really seen as a kind of sweet spot in here for land Where I talk about atmospheric initial conditions for weather and more ocean and so for seasonal forecasting But maybe for land as a kind of sweet spot in between here That it's an important source of predictability that hasn't yet been been fully exploited in the model and we can do a lot to improve improve on that So operational infrastructure just to really hear I think a key point is To accelerate developments of things like when we have all the the ensemble output from these models But how can we calibrate bias correct? downscale that toward product that that can really inform decisions and One thing we want to do down at the bottom here is to establish a pilot a real what we call a real-time pilot program for s2s applications to really catalyze work across our various sectors on on Demonstrating skills in user user decision context. So the idea is though We is to do this make the forecast available in real time for one to two years to a subgroup of people of Projects that are already underway that begin So just getting to at the end here toward. Well, how do we use that information? Just splashing this one up to say Forget we get more specific information as we go from seasonal to to weather But the subseason was really coming in here and it's really a key time in many user decisions You know between the less than 10 days and and the three to six months The many user decisions fall in that range In the water context. So I just took this out of your nice report Some of the issues i'm sure we just discussed a lot today Water supply management including flood control And drought. So what's the probability of heavy rainfall and runoff atmospheric rivers for example snowpack snow melt reservoir operations hydropower scheduling supply and demand also temperature force cars can come into that And I put at the bottom also this that I already mentioned, you know, this really important role But how do we connect between the the climate model output and and the water Decision-making in terms of you know bias correction to be a quantile mapping regression Or downscaling it's something we haven't really discussed very much in the SOS project today I thought I would just flash a couple of things up at the end here. So this is from from Dwayne's group at JPL and there's a lot of interest in the room here with the engineer People from the west in terms of atmospheric river prediction. So to say there's work going on This is connected through the SOS project Be one of this real-time pilot applications to predict to be able to forecast The the number of atmospheric river events and maybe someone is going to show a slide on this later This is some of our own work looking at large-scale atmospheric patterns. We call the weather regimes Can we say something about the can we forecast these? large-scale patterns which could inform various sectors And what i'm showing here is an an analysis of the Identify these patterns four patterns Over the pacific and north america and particularly i draw your attention to this one with a ridge over the west coast That's this regime one that's that's in red here on this chart And this is showing in real time from the beginning of october Which regime we've been under and you can see that there would be none of this west coast ridge a lot of the time Which is for sure been associated with all the fire the extreme fire event in california And what's showing up in the vertical is how well that could be predicted By the by the cfsb-2 model and you we can see that if you have vertical bars It means it could be it was predicted well up to that lead time So you could see we have we have skill out to about 10 days or something Tending to lose it after maybe it's a little bit up here Uh, and this is being produced at the iri now in real time. So I encourage you to to look at this or get in touch with me very very Much interested in in in this we could see that we have just in the last few days gone over into this blue Which is the more pacific trough? El Nino kind of pattern. So that's uh Uh, will co also was was forecast out to about 10 days in advance. So what you can see in such plots is Uh, the evolution of the season so far in terms of these large-scale patterns Uh, and how well they were being forecast to date and what and also if you look up this The diagonal that's the forecast so you can see that the forecast at the moment is to stay more or less Uh in this pacific trough regime so, uh almost at the end now Some some things on on product development. Uh, these are Forecast maps coming out to a week three week four outlooks From from ctc So you're you're familiar with already This is a product that we're also making at at the iri in terms of terfile Categories This is the same kind of style that we have in our seasonal forecast What's the probability of below normal rainfall above normal rainfall? This is a calibrated sub-ex project product coming from that that Noah sub-ex project So showing above normal below normal. So this is something that that we're now experimental making in real time So I think we're starting to be more and more products out there that are that are available to people to look at Uh on To on on this time scale What should the window be maybe there's a final one there is this is within your seasonal seamless We're going from just daily Daily values on the weather scale to three month averages. What should the window be? Maybe you can have a kind of sliding seamless window as you as you go to longer lead where you expand the window in time That's an idea. I think so just in summary. I think this is really an emerging area still sub-seasons and seasonal Improving forecast capabilities. I hope I've shown you something of that and also give you an idea of That that you know product development is underway, but still much more is needed Bringing together weather and climate. How should we What should the product look like on those timescales in terms of you know weather products versus seasonal forecast products or more seamless prediction across scales creation of these databases has been a big impetus and you know to us to know and others in the u.s. From for making the seasonal North american multimodal ensemble um Database available making it available in real time and now we have the sub-ex Hopefully this can be continued. Remember that one is in in real time The s2s database coming from the s2s project In in our project. I think there's a this really Since we're just starting the second phase of the project in january. There's a lot of scope for You know feedback from the community from you and others on how those how this book How those new foci go forward in terms of getting better Better forecast from the sources of predictability in the slowly varying surface conditions ocean sea ice land surface impact of stratosphere how can prognostic aerosols improve forecast And then how can we better, you know And generate the ensemble product development I mentioned this real-time pilot with a sub-seasonal database will be made available in real time It's it's delayed by three weeks behind real time to a set of demonstration projects Well, I'm allowed again the sub-ex project has already already demonstrated As a s2s the value of multimodal combination So I think that having these multimodal databases is very very important Many opportunities in the water sector for us to discuss today And the challenges of integrating that probabilistic forecast information across these lead times from days to weeks to months to help mitigate a Range of hazards and we'll see that much more work Again, I emphasize that on on things like the post-processing bias correction calibration downscaling towards sector specific variables and models And then finally we had it. We had a great conference that at NCAR S2S with S2D together. There's a huge interest in the community also this sort of seamless Paradigm that has also been taken up in a big way by the WMOs. You want to learn more about S2S project? There's a website S2S.com.net. There's a newsletter on that. And finally, we just published a book actually for Vita and myself with On the topic that just came out so I hope I I hope this has been useful and Appreciate any comments any any questions anything I can do to clarify and very much looking forward to the discussion throughout the day. Thank you Okay, lots of information to digest there. Do we have some questions? please Put your card up and I will try to keep an order so Go ahead John Yeah Mike and name, please Okay, thanks, uh, Dave Titley Penn State. So thanks. I thought that was tremendous presentation Thanks so much on your graphic that you showed the predictability of atmosphere land and ocean Where where would the cryosphere fit into that? Where would ice be on that if if you have any thoughts on that? Thanks It would be it would be both at the sub seasonal as well as the the seasonal That would be way to say So it would not just be it would be On on more on on a longer on a longer breadth of timescale But sub seasonal would would be part of it David and then sushi and then johnson Thank you, Andy wonderful presentation a lot of material to digest Before lunch. Sorry if it was too much But I have a question related to you mentioned, uh, El Nino La Nina a couple of times is that part of that Phenomenon that timescale and space scale phenomena part of this your s2s study or Yeah, and then if you say yes, I have a follow-on. Yeah. Yeah. Yes. It is Okay Especially over australia people have been finding out a lot of the skill and the sub seasonal forecast is really coming from from anso Yeah, I know So my question is Is your group going to investigate the poor? Predictions that were made in 2014 2015 that the community at large said there's going to be a big El Nino But it came a year later. So in some sense they were correct if you Don't worry about time scales within a year or so. I'm being facetious So is is it your group or some other group? That's because you mentioned Specific phenomena and then you also mentioned we need to do better on just the regular Determination of all the variables in the atmosphere. Yeah, which are two separate things right? So we're I mean within the WMO system. There's a working group on sub seasonal to inter decadal prediction and predictability WG sip and it's really their their purview especially on the on the seasonal scale the the the the nitrocyst the the f2s project is in Looking at the role of what the shorter timescale variability is How is that impacting on the lack of skill that we're seeing at the subs at the seasonal timescale? So if you look at the winter of of 2015 2016 It was grossly over forecasted to to have El Nino conditions with wet wet in southern california That didn't pan out. But if you look if you look at one of those that kind of plot that I showed that You call this Mike typically calls these articulate plots You can see there's a heck of a lot ahead of a lot of blue in there at the seasonal at the seasonal lead time so the models are really uh, you know predicting strongly that it was going to be uh canonical El Nino Impacts pattern But what actually happens is when you get down to the the lead times, you know in intended to 15 days You see that what that these sub seasonal scales became started to become important And there was predictability on those timescales that you could see those as noise in terms of uh, you know the seasonal forecast so Well, I think I want to try and say is that the s2f That provides a way to see it sort of holistically in terms of If there's a bad prediction coming from the A bad ENSO prediction. Why why was that? Is that because of other other phenomena playing a role on on the sub seasonal scale? I'm not sure if that answers your question quite, but Yeah, thank you for your presentation. Um, I have a question. Um, hope is in spirit of learning from our sister board of Water colleagues about What are the quantity or things we forecast that can speak to broader community in some ways? Maybe I'll start with prediction of precipitation So in the weather regime, we usually like to think the next day or two or a couple of days down the road We'll call the quantitative precipitation forecast the qpf So that have specific meanings as each location By the time we get into seasonal The current way looking at it. It's the outlook either above normal or below normal Then seasonal the sub seasonal part of this it's Sort of in this zone. Um, I wonder if you can give us some sort of a Um quantitative description about what should we expect from qpf to this outlook? Is there something can be specifically? Looking down the road the next few years to be an improvement from above normal below normal or some sort of a qpf So I think that that's a that's a great. That's a great comment And I think it's really something that You know, we're we're only starting to grapple with now and That point comes out of this slide where if we look at the week three four outlooks from cpc And if you look at the way that we've been doing the same thing is also week three week four at the iri It's in terms of these above normal below normal This is precipitation at every point the probability that it's it's just above above normal below normal Or we have another product where you can look at the pdf itself So you can say well, what's the probability of being above a certain threshold? Something like that, which maybe move moves more in that direction and in our seasonal forecast. We we also have that but it's very much in terms of You know, we're looking at odds over some Some window period and it could be It could be the average in a week or it could be well, what's the probability of having a number of days? A number of extreme days or things like that Which is also something that's lent from the seasonal side. So in seasonal forecasting We talk about weather within climate and can we say something about the number of dry days in the season or something like that? Those kinds of statistics. So, I mean, I guess I come from the seasonal side Seasonal forecasting side. So I'm biased, but I see, you know, there's a lot of work on taking those methodologies from the seasonal forecasting And translating them onto the sub seasonal scale where we just make the window We just make the window shorter, essentially But I think your point is also getting toward well on the weather forecast side We have these things every day and that's what of envelope is going out to into the medium range Is there some way that we can go more seamlessly between those? and you know, maybe it sort of gets to that that that that kind of a uh What encapsulated here and what does a product look like on this s2s scale? Yeah, thank you, Andrew Could you comment on the role of the linear inverse modeling in the s2s universe? Yeah, thanks. So in the s2s universe, I can say from the s2s project side I mean obviously from what I've what I've been talking about. It's it's a coordination of global producing centers using ensemble prediction systems So it is heavily biased. The work has been heavily biased in that direction But there's very interesting work coming out of the of the these reduced inverse modeling linear inverse model community particularly from you know, Matt Newman and others in in boulder And he was he was showing them at our meeting in in in boulder and in Santa Barbara as well how Using such models you can also get you can get a skillful forecast That may may rival the the global the ensemble prediction systems So I think they play firstly they play a very important role in terms of providing Benchmarks so a baseline you should be able to beat, you know, what a linear inverse model can can tell you That they can also elucidate the dynamics. What's what's going on? Where's the what are the sources of predictability? And in certain cases they they they may be they may be useful as predictive tools And I think there's not enough work has been done On on on that and I think in in our S2S project. It's something that we need more of Thank you for that Next mark and then we'll finish with Robbie Thank you, Kathy You one of the questions I have as you were presenting and then you said you've kind of dismissed it Is the downscaling? You know the the maps you show Pretty much western united states is going to be above or below normal What what's what's the progress and what what's your Your forecast for having that That that resolution they'll see this at least maybe even on a on a on a watershed basis For example to know that you know that a particular basin may be more More above or below normal rather than you know such such a large scale and and And so what what's being done and what what's your your forecast? Yeah, so to be honest in in S2S in the first phase It's really been focused on looking at the larger scales. What's the skill on larger scales? and in fact the The data in that database was all coarse grain to one and a half degrees. So We don't have the fine-scale information even if the ECMWF model is is You know tens of kilometers in in resolution So I think there's a there's a lot of work to be done there on on how to downscale should it be What models to use linking with you know region a limited area atmospheric models toward hydrologic models or other ways to statistically Calibrate and downscale using quantum quantum mapping people have been using that or regression methods that identify a decision variable Maybe it's on on your your basement scale and then regress that on to The largest scales that are Are predictable in the model So identifying, you know, what's the predictable scales and on in the atmosphere in the atmosphere and then relating those to the the hydrologic basin scale, but I think there's there's important work to be done both on on that kind of more statistical approach versus, you know, really coupling or or not couplings the wrong word, but but interfacing the The the large scale models with limited limited area modeling It's not something that's that's being done very much or we haven't really we don't have a sub project For example on that in in s2s. It's not something that that particular that our group has Is is focusing on very strongly, although we have heard You know that many that there's a lot of a lot of interest in that So you don't foresee that that's not in your vision your 10-year vision To make this this kind of practical applications Our five is a five-year vision now since we were halfway through But the five-year vision in terms of demonstrations is to Is I think through this I mean it's through those foci research foci and so forth that I mentioned that I mean downscaling isn't one of those But within the real-time pilot if we can identify some groups That are already working on on in that in that Domain then then that would be an opportunity to to To catalyze that Okay, great. Did you have a quick related to this going to make a comment that There's another world climate research program called cordex the coordinated regional downscaling experiment That's really designed for climate longer time at climb sales And there would be a natural marriage that we started to think about between S2s and cordex at some point there could be some natural ways of bringing those together in the future That might provide downscaling to the s2s time scale. Thank you very helpful. Yeah, great And um, ravi will have the last question and then we'll move to our next speaker. Thank you um, I'm going to be a little provocative just to kind of go where are I shouldn't say we you compared to farmers almanac in precipitation I'm just Well, we you know at IRI on the seasonal forecasting we've been fighting against the farmers on almanac for 20 years And we do think they were quite a bit better than that And uh, you know that the map that I showed at the beginning on seasonal forecast skill It's an expression of that. There's been a lot of a lot of verification done on real-time seasonal forecasting uh, so I I think that uh You know there's a lot being done in the seasonal community It's just starting to be done now on the sub seasonal timescale and I showed a a couple of examples But I think you know with the with these databases becoming available also in the the subex project They've been doing a ton of work that's just been a a two-year project on on Looking at the skill of those those those us and models of over north america I don't know if that answers the question One way of hedging it isn't it you thank you very much. Oh, we really appreciate that And uh, next, um, we have uh, yin fan from ruckers university and I'll let um, Dr. Ryan Belder introduce herself as well and um, take it away, please Okay, my name is Yin Fan Ryan Belder. I'm a professor at Rutgers University in the earth and planetary science department I'm a hydrologist and I do very large-scale water cycle models really from the perspective of water Psycho impact on the water energy and carbon cycle So some I'm somewhere in between those two boxes. So on the On the right and on the on the left. Where's the pointer out here? This is the what Kathy said the the the weather and climate world where we have Kind of vertical water and to be fair atmospheric is totally 3d So, you know, and but but but it's more 3d than than land actually and because they go to kilometers up We only go to a few hundred meters down But but we're clear as land dwellers and looking up and we need liquid fresh water And this is what? Uh, Ampere gives us this how much it wants back. Basically, that's how we view the the weather System, so I think it's vertical water. It's not really a fair statement And and but this vertical but again, we're going to come back to land and pretend We're just land creatures. We cannot move anywhere else. We cannot fly. We cannot die And so then we can look at the water as a 2d and in this supply and demand from the Weather climate side Actually gets redistributed on land in a 3d fashion Not just horizontal water, but also goes down into aquifers and come back up. So it's entirely also 3d but again from the If you're a climate scientist you look at land a land is 2d and you right you give us Evaporation will give you water you give us evaporation that drives the boundary layer dynamics thermodynamics And so it depends on who which side you are. We're all 3d But again, we're going to stay on this side on the water side because this meeting is about water And so we can think of land as 3d and the atmosphere is winding up and down and There is there is A lot in between and so I'm working really somewhere in between and so Again, I'm trying to figure out this Okay, so first I want to start by saying that We do as a community A beautiful job in really predicting the atmospheric water balance the up and down fluxes I'm sorry shown here and you go back again. I'm going to get used to this for a little bit So we have this Okay, it's not moving anymore. Okay, I'm gonna go back one more time and then go do this again Okay, so it's moving. So you this is an old model simulation. This is the in-car CCSM Just about 10 years ago simulation. This is the water vapor moving In the atmosphere the white is the evaporation and then the orange and red are precipitation Basically, you'll have the up and the down fluxes and this is hourly times that for 10 years You can see the the all the seasonal and and you know hourly Dynamics of atmosphere and we do a beautiful job because the climate scientists are really really organized and they put their Community Into a field a limited number of models and they are really do a fantastic job But a hydrology side not so so This this beautiful job of vertical water flux Do not really give us a full picture of hydrology because they're not designed to do so They are weather and climate models and the land is a boundary condition And so as a boundary condition, then you get the skin right and you pretty pretty much do a good job Getting the land surface feedback back and so that's what it's done And so it does a good job for the climate models But these models are by design not built to forecast hydrology So you can you can see that they usually have a very large land slab field degrees and and he was saying that model 1.5 degrees at 150 Kilometers wide and then the thickness is only about two to three meters because that's a boundary condition That's the skin and then the water that goes through this skin We'll get into the reverse and it comes somehow routed out and so So doesn't really have the so-called meat and potatoes of hydrology And hydrology is to think of the land as you know, we have topography water flows downhill You'll have the cilantro flow You also have vertical flow going down and come back up and then that's the near the surface and deeper down We have aquifers that's where most of our water is aquifers And this this two picture from the left side and right side are very different. So The difference for example Is that these models are very very shallow. They are only two to three meters and Ground water goes to 100 meters and the the almond Orchards were in california irrigated by water 400 meters deep So that's where we came we get our water to put on the land surface and evaporate And so that is the depth side and then the horizontal scales are very also very different and then most climate weather models, sorry have a Very sort of a flat big slab without the the local hydrologic processes And on land from the ridge to the valley is only about 10 meters 100 meters and most And so horizontally they cannot resolve the processes that generate our stream flows and then Really another important missing of the Meat and potato is there's no groundwater The water leaks through in those that in those models in climate weather models and moving to rivers without a groundwater storage So we our most as I said most of our water comes from our aquifers and without the aquifers We our our groundwater Not there and our wetlands cannot be supported our low flow when it doesn't rain the flow forecast cannot be Served because you know our rivers are flowing days and weeks and months without rain And that water comes from groundwater. So this is really the importance bringing groundwater into this framework And so so what do we do? Right, uh, this is a this difference has a historic reason and for a good reason But now we really wanted to to know You know From weather to climbing models and to today's earth system models integrated Impact analysis models those models participated in IPCC They forecast the water resource change into the future and these models don't really have The hydrology and even potatoes the hydrologists like and the land water managers and on the ground would like to know And so they don't have this current information. So what do we do? And this difference will continue. There's two different water world vertical versus This this land 3d model will continue. So what uh, how do we make hydrologic forecasts? That's our question, right? These models cannot see rest. We gave us The information we want on land. So what can we do? So i'm going to briefly talk about the two general approaches and then where we are What are the challenges and going forward what we can do? Uh, so the first very common approach is to use the climbing model output And then drive an offline hydrology model. This is the common practice. We see hundreds of papers in the literature And then second uh approach is to bring hydrologic processes into Weather and climbing models in a simple way but capture the fundamental processes. So those are the two Sort of approaches i'm going to briefly discuss Okay, so approach number one don't basically using the climbing model output you gave me the supply and the demand I'm going to drive a Fine scale the fine grid Land hydrology model There are many many hydrology models. You can pick one and then use those coarse grade climbing downscaling Present and the future and the drive your hydrology models that can have reverse and hill slopes and farmland and irrigation and aquifers So this is a very common approach and very successful approach in many ways It can cover regional to local watershed scales And and really those models are built from local agencies state and county Water management area level so you can directly involve stakeholders. And so you have a targeted sort of a problem driven Model that can give you the information you want and you can also constrain the model better with the local observations You can get the observations. You need to constrain your model. You calibrate your model Uh, so this is most are the really the pros. You can really have a very useful model for your local place and but the cons is that We have just a patchwork hydrologic models Unlike climbing models, there are virtually hundreds of hydrologic models And every research group likes to develop their own hydrology models So they're just way too many models and very confusing designed for different purposes and depending on who you talk to you get a model to use And so and it's very costly Because you recalibrate a rebuild model labor and the computation and data intensive We waste a lot of energy by doing this patchwork models and it's very hard to compare across the board You know for federal agencies to make decisions for FEMA for example, it is who do you listen to they use different models and so I mean, I was talking to Dave made a man a colleague at UT Austin. He said in Texas alone for river forecast. There are over 60 models 60 60 and it's so depending on which agency you talk to and which Research to form you you you talk to they all have different models. So this Really really disorganized hydrologic landscape Is the the main issue so then the question is can we do better? Uh, I think so and everybody knows in this room. We need integrated. We need to organize the hydrology Academic community should really follow the climate and weather community example and get organized and develop those Integrated, you know from all the radio zones soil that supports crops and our forests And to aquifers and to rivers and wetlands and the quality and the quantity All these things we need to have some kind of integrated modeling framework And and also we need to have multi-scale so that we can serve the nation with consistent Framework as well as have the local details that can be useful for local problem solving And we need to provide these parameters And we need to have a community level support just like ncar has rallied around The community rallied behind those those feel very powerful models and put our best Wisdom and knowledge into those models and hydrologists need to do the same And so and we also need to have really integrated funding I know we have the many models developing in parallel And I think a lot of taxpayers money and they don't talk to each other. They serve different purposes And I think this landscape need to be changed So in this regard, I'm going to highlight the two models. Why is the usgs? GS flow and why is the national the noa national water model as some kind of future directions I'm not endorsing those models personally, but I think they represent a healthy direction for the community to go Okay, so the GS flow the GS flow here. You can read this report, but really it couples the usgs Very old the land surface runoff model vedo so model and with the usgs mod flow, which is You know, very well, you know, basically a community model It's a beautiful model. I personally think so I teach use those models and so to coupling this Is a really great direction to go and so right now usgs is building this so-called the national hydrologic model national It's built on the watershed level. So that gives you the details local details And so but then it has national coverage and has a long way to go because they're calibrating watershed by watershed With surface water with the wrong water with irrigation with human impact with land use change, etc and they also side by side provided a national Calibri model database so that anyone if they're going covering from the western states And expanding outward and then the parameters are stored so people can come to this national database and look at What's there already? What models are developed already? And so this is that is the usgs Model and then the second one I want to quickly mention is the national weather service The national water model spearheaded by Dave Maidman the shown here And so the the computation and the training everything is housed in the national water center in alabama And they hold summer institutes every summer have grad students and postdocs coming in To to learn to use the model. So what does model do right now? It's really surface water right now. And so what this shown this is shown is like our lake River you can see the river every storm comes and then moves down on a national databases for Hundreds of thousands of stream reaches Down to stream reach level they computed the national river width and height river bank storage And so this is can be done in a real time. I said I find this quite inspiring And then they also went one step further to provide a flooding prediction In the in the past and the national weather service would provide a prediction of river flow at one point Okay, it's the flooding stage and so what we don't really know the extent of flooding So use some simple terrain analysis to call the hand They just map the elevation around the each channel one foot two feet three feet And if the river rises, what is the area that's flooded? And so this really provides a very very useful information and this is uh, actually Harvey And this is a great example And so the using the national weather mode Using national water model in a way the flood mapping and this model predicted that region two including houston will be the one most impacted by by the by the flooding and although the Landfall is here. It's not in houston And so here tells you the weather prediction alone does not give you Really on the land what happens and so by doing this by mapping this look at this there right on This is three days before Harvey landfall. They can predict houston will be flooded at big time This is flooding area one almost 100 percent And so this kind of information with three days at least time can really be used for for FEMA for for state disaster response agencies This kind of information should be done On the national level and then they are now doing that the national wet the water model And so they have made great strides in Getting the surface water right the river and flooding extreme events by the groundwater Not yet. And so this is something i'm hoping to help them to bring in the subsurface part because We are not only experiencing flood and what about the low water? What about the drought? What about the ground water slow input and during no no rainfall drop years? And so that's a big part the national weather model is working toward Okay, so these are some just some quick highlights of this approach number one downscaling But I do think that we really need to get organized better to have multi-scale from local to national So that agencies especially federal agencies all level agencies can rely on the same kind of consistent information and the second approach i'm going to highlight is to bring hydrology into these weather models as a first place And so this came really all came motivated by this problem of this one way Downscaling idea So you have a climbing model driving the land model as if the land is just a passive receiver Does not influence back to the atmosphere and we have this room for many many Colleagues mine who has spent their career studying land and spear interaction We know in many places what happens on the land that can impact weather and it can impact large-scale circulation and so This is the motivation we we basically cut off by using this approach. We basically cut off So science wise it's just not the best science way to do science And so land cannot feed back and in nature These two of course are always coupled the coupling strengths are debatable and aware and what mechanism But we know water flows seamlessly in nature and we can do better And so this is where i'm going to talk about a little bit Of the second approach to bringing hydrology processes into the weather and climate models at the first place Okay, so the idea is to extend this very very thin crust In those gcm and earth system models Into deeper into the crust To include then that little shallow two three meter land surface free drainage land surface into Something more real into something that has shallow subsurface flow And and with the deeper aquifer storage and flopsis. So that's the idea And the the pros of course and we can really capture the system level feedback as nature does it So we can produce dynamically consistent uh water forecasts And which is just better science to begin with And so we can also use this kind of better science to make regional to global and long term for water cycle Predictions and because they're coupled now and so We can have one a unified framework and we can really help using this kind of forecast To help the international and national community setting agenda setting priorities on a very large scale and so that is really the The pro part of it But the con part is that when you build a model show Comprehensive so big you lose the particular usefulness to a local place, right? You know, you'll have a One model fix. Oh idea. So we really Have to have to really understand that we're not going to do a nice job as the local models We're going to do the stakeholder driven local problem driven But this is a framework and then you can now use that a framework to Drive a smaller model And but the two major cons is that it's very expensive to actually representing global models the land hydrogenity the land health processes and also it is also Demanding information on a global scale of the subsurface you want to bring in the aquifers. Where are the aquifers? How deep do we go? So there's a lot of question marks. So I'm going to address What are the ways we can do better and so the first? there is Right in the first count they're expensive and not feasible to resolve land surface processes Here we have tens of meters and here we have tens of kilometers One thousand times of scale difference. What do I do? And it's just simply not feasible. So I I organize a synthesis team a few years back And so we really put together the hydrologists in the academic communities from 50 people or so All levels their career to really talk about This question. What are the real structures to land hydrology that we can't represent without representing everything explicitly So we talked and talked and we decided that the two things that are land surface We really need to capture one is down valley drainage water flows downhill. Nobody's going to say no to that And the other one is that the sunny side is going to be warmer and drier than the shady side And nobody's going to argue with you. That's not true. And so those two Critical structures and functions of land surface hydrology is what we hope to capture. So if we do that We can represent a big watershed Or a big model grid into something like a stadium what I call the stadium model So if you sit down here it rains, you're going to get wetter And then if you sit on the sun facing side, you're going to get hotter Basically using this simple stadium. We can collapse a watershed into Water and energy differences across landscapes. So by doing this we can capture the fundamentals without causing a lot of computation So this is going on right now Two lines one is the n car warf high warf model NOAA MP is the land model we're putting Organizing the grid into watersheds watershed is the natural hydrology unit for atmosphere Cartesian coordinates are perfect But for the land the land does not follow Cartesian coordinates watersheds are basic units So we are Remapping the land grid into watersheds and then with each watershed It's a state little stadium and so warf will have this capability very soon And on the parallel line is the n car clm the land model And we're doing the same thing and my grad students and my former postdoc We were working together to Improve those two models to include the land surface processes However, that is only scratching the surface We capture the topography because something we have data we can observe We can immediately capture the topography induce differences in Aspects in down valley drainage and but what about the deeper actually first here? We need a subsurface information here. We need to get into geology hydro geology And so this is our second Challenge is that we need to put together the subsurface data. We don't have a global scale data So I led another synthesis team starting with the usgs paul synthesis center. What can we do? You know, we've been mapping geology of the world for 300 years for god's sake And all the data is scattered everywhere. There's good sailing cross sections boreholes nobody ever bothered to put them together and so Even with usgs and I was very frustrated working with usgs colleagues How they have been mapping the aquifers of the country Since the 80s, this is the usgs rafa project They have beautiful cross sections boreholes all the major aquifers are characterized But they're not in one place And we cannot find them in digital formats I talked to so many people and are frustrated for five years. I'm not getting this data together And so they have I'm going to show three examples and here is the All the coastal point aquifers are beautifully characterized and makes the cp in vehement the subsurface geology aquifers Confining layers beautifully mapped three dimensional and then there here is denver basin mapped here is central valley Mapped and so we have all this information. We need to put them together as a national database So I mean in over the world I was just talking to uh, stephan broda who is leading this unethical effort to put in world groundwater resources on one place in one place Where groundwater is how deep it is how much water do we have? How are they going to Be impacted by future climate by by intensive with withdrawals for irrigation And so these all are data existing. We just need to put them together So the synthesis team said we're going to build a digital cost And so we have uh, google earth, right? But we can go down there Show on google earth. What are the layers when you dive down not just zoom in but dive down What are the three dimensions of subsurface? That's most that's where our water is So we got some funding. Sorry, but but I wanted to uh went too fast We got some funding But this is something so big and really needs international needs national level coordination We need to get us just motivated us USDA and the soil data everything put together to give us a three-dimensional The australia has the national hydrologic fabric They match it completely for the whole nation the canadians do it right now doing 3d canada And why can't we do a 3d us and we have the resources to have the data We just have to need to have the will to put it together so that we can support models of global scale and continental scale to have the real hydrology brought in and the hydro hydrologic computations isn't it's trivial compared to i don't think integration computation But yet by something like this we can so much empower those climate and weather prediction models to have some real hydrology So their predictions of water resource impacts can be Really more meaningful to the land so to summarize the key point is that Climate models are not designed for water prediction And so what we can do is to use climate model output to drive offline fine green fine grid hydrologic models to get hydrologic reality But then the problem is that We have too many models. We need to really get our landscapes organized second approach is to Bring hydrology directly into climate models is something my group my many my colleagues do to It's just better science, but we have data and computation challenges and so going forward I think For the for the approach number one We need to continue to do this because it has tremendous value to the local stakeholders We need to really invest in In the community modeling system and get organized like the climate community. We need to get organized We have a quality and the Climate community have in car. We have quality But the bucket is not comfortable not even close not even the drop in the bucket in comparison But water is so essential for everybody and not just for humans But for ecosystems for the carbon cycle and we really need to get our landscapes organized And so the integrate community modeling systems. There's some examples here GS flow and national water model and work hydro I won't have time to talk about which is the coupling framework of weather predictions with the land hydrology Those are the really the directions we need to invest We need to really put them on the table and I see how we can as a community can help can focus our effort on the seal better quality models And so for to for approach to bringing hydrology into climate models The biggest challenge is we need to integrate support data We have not done still and the hydrology Operate on watershed scales. We just have not zoomed out and really think the large picture Produce a map like this, you know, I cannot we don't have an exciting hydrological flow map of the world. How are the rivers waning waxing? How about the ground water waning waxing? We don't have a vision of that at all and so digital class, I think it's really an important direction to go and and to do all this we really need to build a very very big partnership With with google for example, and they would love to do something like this And if we have a shared vision and we can pursue this vision With the integrated multi agency funding so we can have really organized seamless and consistent and meaningful Forecast framework to translate weather the virtual water to the 3d From rivers to soil to groundwater to ecosystems to crops to to our forestry. So that's all I have Okay, wonderful, thank you so very much and I see all kinds of of stuff going up. I believe I had David and then some Rudy and Chewy will go with those. I hope I'm stating in time Thank you, David. Thank you co-chair Thank you very much for the very informative discussion presentation As I represent the ocean studies board, I'm very pleased to see twice the word ocean And that is the storage of everything that comes from the land I apologize. No, no, no, that's just a comment. No, no my question is coming. My question is coming We are talking about land this meeting, but I firmly believe that ocean Ocean drives the big you need when you talk about forecast Okay, I apologize more later On the downscaling of from the climate models to the continental scale or the regional scale that We sort of have on the screen here What utilization do you make or how do you use the soil moisture data that's now been coming from satellites for the last decade? Do you use the data at all that you mean the smat? Whether it's or smoth Yeah, yeah, the europeans or the u.s. I know there'll be others So thank you. Yeah Yes, and we need data and data no matter how flawed teaches of something real, right No question about that. But with the soil moisture satellite remote sensing It's difficult because so we can only see soul skin deep And so and also in between there are clouds. There are trees and not all soil is exposed to the satellite So there is some limitation there But where it is exposed and definitely that should be brought into a comprehensive frame the comprehensive framework It's not just modeling framework, but also benchmarking validation framework that have data comparable to the scale of the model Well, I'll interpret that that you don't use the data yet I don't know I'm not talking about my research here in my research. I do. Yes, or you do. Yes. Yes. Yeah I mean, I use grace. I use rivers. I use in situ. I use every observation. I can get my hand on But I'm talking about, you know, a forward vision for the community as a whole what we should invest Yes, absolutely answer to your question. Yes, we have to I'm on the nasa earth science advisory committee. I'm fully aware of the The the tremendous value of what we do especially in places We cannot go there on the ground to measure the streams to measure the soil moisture You can't go basin to big water bathtub. We have no data and the remote sensing is the only way for us to get something Thank you for the question Okay, ruby is next Hey, yin is always wonderful to hear hear your talk. So thank you very much for the for the review so, um I mean just for the sake of uh making this point But I know that you of course really aware of this and your own great work is Has emphasized a lot on vegetation So I wouldn't want to say that part of the cons of The first approach is that when people do hydrologic modeling We like to kind of focus on the specific things, right? So if we want to do the hydrology Well, then we put all the focus on the groundwater The the lateral flow and all of these different things But then we forget about the vegetation as we know how important the plant response is This is the model response to co2 and things like that. It's really important for the et Which therefore is really important for the runoff and stream flow Right, so chris milley has a really great paper about that. So just want to Say that I mean it's important uh Even when we are talking about like doing a community hydrologic model We need to put a put some focus on the vegetation and the interaction with the hydrology as well Absolutely, and so the synthesis project and you were involved early I was really focusing on the hydrology on the vegetation So the motivation was the vegetation vegetation Transpires back Two-thirds of the rain on land and that's a huge water cycle pipe But we call we call vegetation the plumbers of the earth system There are the main plumbers and they tap deep they pull deep water into the atmosphere And so this is a huge player not just water cycle But as you said the carbon cycle and that is the deep Role fundamental role the vegetation plays and so In in regulating the climate, you know, it's a particularly long-term scale And so here on my personal research is vegetation in regulating carbon cycle Going back 400 million years when the land vegetation first evolved And so it's big going back more you really start to see vegetation with without vegetation The world was a very very different place and there's no question about that I'm glad you brought that out but for the for this uh for this conference for this meeting It's really uh water for human this point. And so I didn't want to uh overemphasize this Yeah, absolutely. Thank you You Thank you so much. This is very educational for me. Um, I learned a lot from your talk So I have a question maybe I sound naive because maybe all of it ignorance But I'm trying to understand that this multi-scale nature of a communication for water Cycling itself it's kind of on different timescale So in the way there's a precipitation process itself happens on the very short timescale But then the same time king relation and so on so on the decision making process Which is the focus of the section it's more on a few weeks to say maybe a seasonal scale So you speak to a lot of climate skills. I'm trying to Get a sense on the intermediate scale where that a lot of decision making happens And what kind of a need from the hydrology side? Which relate to my question to the first speaker is what kind of product going forward to be the most useful in terms of sub seasonal prediction Both on the adam stir side because adam stir side now We're moving toward earth system prediction model that we know land surface. It's a part of a predictability source Like solid moisture, but then the same time I'm a little less in terms of the knowledge of what's needed in the hydrology side on that timescale If you can comment on yeah, yeah, and and I apologize for going on a longer timescales because there's the inherent bias on the timescales here hydrologists working with water that has memory has a longer memory as you said It's not just a sub seasonal and yes the sub seasonal the event scale precipitation forecast is hugely hugely important in disasters in flooding in landslides I think a grant will be speaking of atmospheric rivers and these are directly useful They don't need hydrological models really and hurricanes. They don't need intermediate hydrological model to inform the decision making and the disaster response process and I think that is a direct translation from the sub seasonal The sub seasonal for partly sub seasonal event scale forecast hydrology but for for for the seasonal flow and as Andy's graph showed you have atmosphere on days and you have the The land surface weeks and months and then the ocean and ice probably ice when local I mean the snow packs will be really the seasonal scale sub seasonal scale And so there is this scale sort of a window and the land is somewhere in between And so somewhere in between I'm very biased to think of seasonal to inter annual to decadal scales And as a geologist I'm biased to think about the geologic timescale glacial inter glacial Yes, let's scale back this meeting is about the seasonal to sub seasonal So going back to the sub seasonal the disaster relief the atmospheric rivers hurricanes and these have made huge advances in enough in you know Providing actionable information on the ground There's no question about that, but for the seasonal scale. I think the translation Through the land filter is really important the land filter filters out some of the small fluctuations But really retain the longer times Signals particularly the seasonal cycle and the plants are regulated. They have their evolution adaptation to the seasonal scale as well And so the hydrologic cycle is very much has a very very strong pulse at the seasonal scale Especially river flow that uses shallow groundwater and the larger rivers that actually taps deeper groundwater and so This is where hydrologic models Really going forward and need to nail down. How can we translate the shorter more episodic atmospheric events into more predictable, you know filtered sort of response of the river basins and the catchments And I think that's where we really Need to get on the same page and I think we We need to we have many many models can we can can produce this But it'd be really nice to clean up the clutter a little bit and to Put our community wisdom into a field following the climate and weather communities model I hope I'd answer your question Great. Thank you. I'll just quick question so We can talk during the break is that in terms of coupling Of the system that's multi-scale. So at what scale is thinking the most Important process should be taken into account right now. Yeah, that's that's a great question uh scholar question and Ruby has thought about this so much and ruby has I like to call ruby ruby used to say what is your game changing scale You know, so game changing scale in the atmosphere. She's been talking about the clouds resolving Convection resolving right at the kilometer one kilometer scale. What is the land? What's that the fundamental skill on land to me? It's hill slopes We know that it's the steepest slope on land really the basic force is gravity Not that not that complicated And gravity says water flow downhill and then the gradient is the steepest from a ridge to a valley when you zoom out The gradient is not so steep anymore and it's really a break of scale in land It's the from ridge to valley and so that's why the early a graph I showed capture that the ridge to valley Difference capture that the sunny side and shady side difference. We capture a lot So that is a scale we need to capture But we don't have to do it explicitly. We can build a stadium To simplify and to get a structure and function without explicitly resolving every single slope That that's thank you. That's great. Um, we do have a bunch more cards up Um, I don't don't panic people. We're going past our time, but we will have time for a break We're not scheduled to starting until 1115. So we'll do another five minutes or so Um of questions with yang and then we'll take our go ahead and take our break. You're not done You're still working Yang you're still on So, um, I'm gonna I want to um really that's if you could try to have short answers We're gonna I think we'll do about one more minute. Yes or no answers. Yes or no answers from you. Exactly. Okay So next I have I have Allison and then I have john mason uh over peck So we'll go there and see where we are in time. So short is appreciated I now have to reform you my question is a yes answer. Um, so I really I appreciate your point about trying to do these two different approaches and this focus on advancing for the coupled models And I think that's a really Exciting direction to go and I know there's a lot of work that's been going into that And in terms of your information about what you think data is needed in terms of how you talked about this Global project in terms of explaining the crust butter that could be used in coupled models Um, you didn't they've just asked you about soil moisture But I guess one question I have is you're talking about hill slope and drainage But then what about all the soil and soil moisture column? And so is there data that you think you could put into that Network that would also improve our predictability for soil moisture. That would be really useful at that scale Yeah, and and the soil moisture Is is measured? Mostly traditionally at point scale, right? You have a probe going down and then you measure the volume volumetric water content that goes deeper But it's a point It's very difficult to compare with a model grid which is so big and it's so heterogeneous and the soil is Notoriously heterogeneous from one meter away You have very different soil and so that is what you hear in difficulties in comparing But but we need to bring it in and then there's a lot of research, you know, and you yourself know so well Scaling this point observation to a great level observations. What can make meaningful comparisons? And secondly, we have the area that there's the What's that the neutral the cosmos the cosmos network? as well as satellite observations And they gave us different scales and you know from point to Area with the cosmos and with remote sensing large footprint of satellite pixels and so All this information need to be brought in There's no question about that and the models You know can only be as good as the data That that it's compared against Absolutely, and so I don't have solutions and I don't really I haven't thought too much about it But I know this is active research area and we've been doing this for 20 30 years We still haven't solved the problem. We don't have a very clean theoretical upscaling downscaling Framework for something so heterogeneous and we think soil moisture is heterogeneous, right? But you look at the soil microbial processes. It's even more hopeless And so what do we do? We have an inherent problem of earth scientists That the processes occur in such a huge range of scales And how do we design our models that captures the fundamentals and without killing ourselves is trying to capture every detail And so that isn't in a way of art and so this is Where community level synthesis are critically important? We need to put it so I led the two synthesis projects So just really not one person can stop this problem But we can put everybody together in the room what we know what we don't know What's the best way to do it? It's given how hopeless situation. Yes. I was supposed to give you a yes or no answer. I'm sorry Yeah, okay All right, I think we're only going to have time for one more question So I'm going to let johnson over tech have the last one before break But there was an online question inquiring about Whether you and or maybe anybody else in the room can point to The viewers about some of the published work on the national water model and whether that is in peer reviewed journals So rather than take time now I think if we if people know of where we might be able to direct people that would be really useful With that jonathan, would you please take the last yes or no question? And then we will be on break and we will come back and start exactly at 1115 exactly So the good news is I don't think I need a yes or no I just want to pile on to your comment about Not knowing what our groundwater resources are A paper came out this week in environmental research letters Ferguson at all and in that letter what they uh in that piece they highlighted that there's far less Fresh groundwater in our aquifers across this country than we believe And that we are contaminating some of them With the water, you know for producing gas natural gas and also getting rid of the produced water from oil and gas exploration And so I just think it's really critical as a climate scientist seeing us get more drought more severe drought more dry days In the southwest seeing rivers in particular river flows starting to go down precipitously Due to climate change that we really need to understand what our buffers are So that is something that uh when I talk to policymakers Uh, people they just like to say, you know, don't worry about the climate. We've got lots of groundwater That's the that's the arizona message It's totally wrong and it's gonna be a really big problem in the southwest and on the high plains Two of our most critical regions in this country, but this paper says it's a problem everywhere in the united states Absolutely, and not just in u.s. And in the middle east and in africa Montanso is going into north africa knowing there's groundwater. They're going to they're buying up all the farmers land to have You know industrial-sized Groundwater pumping like the high plains like how we depleted our Ogallala aquifers And we've got to do something to draw more attention to this and get an idea of what our resources really are So we can use them wisely. Yes. Yes So we really need to make this translation of climate Climate trend into groundwater trend. This is what unesco is right doing right now And i'm helping them to put together data To look at the long-term trends and to make projections on aquifers. That's our biggest terrestrial waters 97 of our freshwater is in groundwater Excellent buffer is only limited the call to arms before coffee unless you live very much 1115 back here Thank you. Kathy and that really was a quite effective to me if I go over too long. You can use that same same thing next Well, I spoke for coming in. I want to say good morning. I'm grant davis I'm the general manager of sonoma water based in california And right out of the gate. I'm going to admit my california bias. You just have to do that It's sprinkled throughout this presentation in fact when I got the call from the academy I was down in san diego with janine and they said you have to be here on the 29th and 30th if you're gonna Come in and get your Nomination approved for best general manager in a supporting role So I thought we'd be up in hollywood And I was finding I got to get on a plane to come out to to dc So that california bias. It's real It's western and we tend to think of things about what california needs and hell be damned on the rest of the nation But in all seriousness, uh, I am really delighted to be here I can tell you that I wish I had gone to rutgers and had been in ling's class And you look at what she's putting out. Uh, that would have been a real benefit early on in my career And I want to thank the uh the academy and the joint session here with both of you coming together So I want to encourage you to do this because uh As you can see we have to admit that we have a long way to go But water managers, I I'm representing today the point of view of the end user that forecasts information Situation where we take what you produce and and make real-time decisions on that so as a result of that Um, I want to share today some of the lessons learned what the current state of affairs are and where we may go Years years from now and I love andrew's start which would talk about the 10-year vision Looking out with forecasting and hope that s2s the sub seasonal seasonal forecasts Could actually be the equivalent of what our weather forecasts are at some point Currently today, so a 10-year vision. I I subscribe to that I'd like to see it even earlier and we're going to need to invest in the type of large-scale data The type of science that you're doing the rnd To get to where we need to and I'm very hopeful because if you think about it Uh, the we're we're skillful with the five-day forecasts right now And they're actually to a point where the three-day forecast was 15 years ago That's the type of evolution that we've we've come about by a concerted effort We need to do more of that so we can get the andrew's 10-year effort and hopefully be at around the 10 day possibly to a two-week A time frame, but the fact of the matter right now the two-week Skill set is not developed enough for water managers like us to actually make informed decisions that integrate it into our water management plan So that's really the challenge and i'm going to talk a little bit about what we're doing with our colleagues to advance that and then what water managers probably throughout the nation could benefit from with continued effort So with that I I would like to uh indicate just A way to serve where we're coming from if you look at this particular map It's not showing up really well and I was afraid of that when we put it in there But I wanted to make sure you saw the golden gate bridge most people know where that is And we serve water to the three counties north of the golden gate bridge So that white area there is the russian river watershed out in california and that's our primary source of drinking water So lane mentioned about the importance of watersheds I completely agree that watersheds are in fact The basis by which we should be looking at models and downscaling models and being able to inform our decision making process So to orient you further If you look at up north about 100 miles from the golden gate bridge Is our lake mendicina. It's our smaller reservoir that i'm going to talk about a lot today That's also about 50 miles inland from the coast And it's that reservoir that we've been doing some pioneering work with some cutting-edge scientists with many of you in the room And I'd like to focus on that There's the california bias again and Before I get there. I want to just give a little more background about sonoma water Because you come all the way out here You want to talk about what else you do we're wholesalers to 600 000 customers in those three counties to the north I mentioned mendicino sonoma and marine county But we also have charge for flood control operations, which is rather unique and we actually manage a series of over 75 miles of engineered and natural channels for flood protection purposes along with a series of Large attention basins that provide flood capacity throughout sonoma county In addition to that we have management responsibilities for eight different zones and districts for sanitation in sonoma county And that means we're producing recycled water and water wastewater services throughout the region So we're already a very integrated operation The other thing I should mention about us before going into detail is that the russian river system where we're driving our water supply from Is not connected to the bay delta system in california And that has some strategic advantages. Basically when we're innovating out. They're doing good work And we find something that we can replicate. We do that fine The reverse is also true if we really screw things up. We're not going to take down the bay delta system It's isolated to the russian river And I have a board of directors that's also unique because the five of them are not just the Sonoma water board. They're also board of supervisors members, which means they care about transportation and health care And energy and a much more integrated fashion. So when you look at sonoma water Those integrated components are what we're managing for and it makes us a little bit more willing to reach out to the academic community And build those partnerships and start pioneering that r&d work that we're going to talk about today So california bias again if you look at this, what do you see big red circle in our in our area? But that is in fact what we're responsible for and then you look and you're drawn to the black and the blue and the Dark green and that's that's california We have the most variability in terms of precipitation in the u.s And that creates significant challenges that variability is something that we have to manage to And it makes our job very challenging You look at the east coast. It's generally plentiful with water in most cases as robby said you have plenty of water sometimes It's too much in our case. It's more often than not too much and too little the Slide you see here is our biggest challenge our extreme form of weather was mentioned earlier. They're atmospheric rivers These are essentially just like they sound rivers in the sky and one of them is pictured here. It's a february 16 2017 Atmospheric river striking the north coast and up into oregon and these atmospheric rivers are our form of Extreme weather that cause over 80 of the flooding in in our region We must do a better job of understanding this phenomenon these long narrow bands of the water vapor They're actually carrying upwards of 27 times the water vapor and the water at the mouth of the mississippi river The staggering amount of water coming into hit and we need to know when they're coming We need to know if they're going to be above or below our dams And we need to make corrective action and the problem right now is we've got a lot of work to do to be able to integrate Where atmospheric river research is going into our water supply planning This slide by uh Mike Dettinger out of the usgs and danken out of uc santa san San Diego Is really a story in that variability. I was talking about this is the russian river going way back to 1900 The top line is actual water year precipitation Throughout the years and that red line is the top 10 percent of precipitation days The green is merely background noise. It's all the rest. So you'll see that the top 10 precipitation days correlate Very nicely along that line of of atmospheric rivers. They're associated with with water precipitation And a 10% exceedance. So this is something that's also true primarily. I've seen a very similar situation for the entire state of california the atmospheric rivers are causational for this type of extreme weather Now why sunoma county, uh, I've talked to some of you earlier this morning We've gone through as many may know the drought on record back in 2013-14-15 right into early 16 And that was a significant Set of years and I'll tell you more a little bit about that but in terms of drought s2s Would be extremely helpful if you're looking at what the definition is earlier said it was about a three week to a year time frame If we knew and we had better measurements and better warning with drought conditions Imagine what water managers would do they'd be holding back water and they'd be metering that out very judiciously if they knew They weren't going to get any more They would be much more inclined to to take the risk because drought conditions I can assure you at lake mendicino at the end of 2015. I was managing a mud puddle in fact governor brown's first drought task force was at In mendicino county at the top of lake mendicino staring into a reservoir that the ag community and fish were relying upon And we were in in in serious shape. So I want to tell that story a bit. But before I do You've also heard the news about wildfires very very serious and large catastrophic wildfires out west in california We have just been hammered. So it just so happens two years ago in 2017 sonoma county was the Unfortunate bearer of the largest and most destructive wildfire in california history We lost over 40 lives in that process 5000 plus structures Mainly residents and businesses burned to the ground and I cannot believe but a year later The county of butte ended up with that noble distinction. They lost 7 000 structures primarily residences They've lost over 80 humans and the list is growing with those who have been missing It's a tragedy and there is not a roadmap for resiliency and recovery and we have to develop one We have to learn from each of these incidents The story here about wildfire is such that it's an obvious threat to your water supply facilities. We've had the ffpc and others Throughout san diego. We've got lessons learned But what we are worrying about right now Is a series of wildfires followed by atmospheric rivers that are going to dump the type of volume of water I talked about into the exact same area and the mudslides and the debris removal the toxicity into your aquatic ecosystems Are not good So um in sonoma's case, uh, we hit we had fires october 2017 We 2018 was a year that worked out pretty well the atmospheric rivers came but they were spread out and they didn't The soil was not entirely saturated when they came through that came up earlier today So we made it through without the type of debris and mudslides, but in southern california They had fires in santa barbara in the venturia area and so monoceto Even though it was predicted that you had atmospheric rivers on the way You still saw significant mudslides and over 20 human humans lost their lives and millions of dollars in damages This has huge implications. We're talking about billions of dollars here Just out west and why california and the western region has to get a handle on this So again the message here is that atmospheric rivers are are the cause when they come behind fires You have serious results that you have to be aware of and then lastly the flooding And I would say that uh, what's not known is that the russian river actually has the highest incidence of flood reoccurrence West of the mississippi river So you can kind of see why uh, california is a good case study for how to handle these extremes And when you know what the cause is we have an urgent need to understand that phenomena and take corrective action So getting back to the russian river the red circles right here you've got lake lake menesino lake sonoma Lake sonoma, I'll briefly say is a multi-year reservoir built in 1983 Has carryover storage. It's got 245,000 acre feet. We're pretty good That reservoir is one of the latest ones built multi-year storage. We did pretty well We can manage that particularly as our demands have actually gone down Through the years through the drought, uh, the per capita water use trends gone down And we've actually taken action to reduce our call on the russian river But not so on lake menesino again 100 miles north of the golden gate bridge smaller reservoir about 111,000 acre feet That's what we co-manage with the us army corps of engineers They have it for flood control purposes and we have it for water supply purposes So when ravi said earlier that water is a situation where you never have enough or you have too much And it all depends on when you get it there. You need it when you need it What I like to tell is a story about the the corps and the folks that are responsible for flood control What's their optimum situation with a reservoir like ours completely empty For flood control, you can't get any better than that, right? Me i'm on the other side of that I want that thing full as can possibly be because I got to make it through the Summer months and I want to see full reservoirs that dynamic tension is what we're dealing with Those are what water managers are faced with when you think about your science You look at linking climate and hydrology And I really love what yin was was talking about is narrowing those Disciplines bringing them together and having longer range forecast because that's going to be our story So in this case, uh, I forgot to mention but it's listed here one of the main drivers on the russian rivers that we have three Listed species of fish and we have coho salmon steelhead and chinook and Those fish and the endangered species list drive a lot of our work because on the russian river We use those two main tributaries the dry creek and the russian river as our natural pipeline We take care of our watershed and our premise is that we need to recover the fish species Make the habitat improvements and if you do that you'll securitize your water supply That's the premise that Sonoma water is living up to every day And uh as I go into this next slide, I want to give credit and scientists are better than than water managers are doing this but um Chris Delaney is one of my crackerjack engineers who helped me with this presentation has done some pioneer modeling work And it made me think when ling was talking about 60 different hydrologic models in one of the regions of the us What is one of the better better jobs you could have right now for job security water modeling in the hydraulic region? Right. Tell me about it. How many we don't get our act together? What happens is you build your own models and you just regard those and you want to make sure that they work But she did point out a couple promising things. I just want to add lib here and that is The national water model and the usgs flow. You look at those two examples on a national scale What you look at with partnering with agencies like us is that we can help refine those and you build those up on a watershed scale Or multiple watershed scales and you're able to make those more refined Bits of models available to regions that can benefit both on the land and waterside So this is a story like Mendocino and this is the guide curve And I have to to laugh because this is no there's no curve here. That's a misnomer. Would you anyone see a curve there? That's literally our guide curve. This is the operational control manual And you are looking at something on the left which says in october you can be up around 111 000 acre feet But come november 1 You need to be Down at the the lower level around 68 000 acre feet And the reason you do that is because of the flood control needs the danger of floods Ars coming in hitting that reservoir. You need to have a very conservative Straight line. So literally this was built and designed in 1959 And I like to say if you are dealing with a heart problem You would not want your surgeon to be using 1959 technology. Would you I wouldn't feel too comfortable about that We've made a lot of progress in the way so Then come around march you can begin up the trend line and get by by may you can back up the 111 000 acre feet because You can expand the supply pool because the threat of large rain events is now gone This is going to take a little talking, but it's important This is the main theme I want to talk about it how water managers Have to deal in the area of extremes If you look at the solid lines on here The solid green line is the actual storage in lake mendicino in year 2012 and i'm talking about two year types here is to make my point The blue solid line is the year 2013 an actual storage in lake mendicino And before you get too far into that below the dashed lines are actually the year in precipitation The green is 2012 and what you'll see is that around mid january You start to see atmospheric rivers come in and really hit us in march And you see those spikes in 2012 go up right on the edge of the rule curve on the gray there And we were able to hang onto that water all the way through the summer and start the year in pretty good shape Jump forward to year 2013 And you see that that virtually the same amount of water was was brought into our watershed and delivered in the atmosphere But it's the timing and the sequence that we have to manage for and what happened here is It happened in In december all the action was in december And in late december early christmas present We ended up with an atmospheric river that literally spiked us up into the flood control pool there and you see What happened right after that? what happened after that is we Had the core looking at these two operations They are above the flood control line that i spoke of And they had to actually release all that water almost instantaneously And i'm not criticizing them directly because you have to use the control manual Those are the rules they're conservative for a reason because you don't want to take on extra flood liability But my goodness We at that point what's really tragic is that's the very beginning of the drought a four-year drought right after this We didn't see a drop of water barely for the rest of the four-year period That's what i'm talking about. It had real life implications I'm managing a mud mud pool up in mendicino not good Some of the prime wine grape growing region in the country with fish that are endangered And i'm starting to really worry. So that's the sense of urgency that soma water is bringing. So what do we do? We said how can we Begin to get more flexibility on that 37 000 acre feet that was released That is about 80 of the average water supply that i sell in a year. Can you imagine that? You can see that we had to do something. So here was our response We took a look at the two week forecast from the hydrologic ensemble put out by the weather service And we said that's where you begin you got to look at that two week period and you can start to see that That's the the starting edge of where s2s is going to be. So we're interested in seeing the zero The two-week forecast be as strong as possible and quite frankly it's better in the one week So that two week is is not there yet and we got to get there I know i'm going to get a question from your son because you asked about this Um We said we need to come up with the process using the the weather service models and ensembles We need to build an interdisciplinary team That is going to demonstrate a way that we can come up with improved water supply reliability Not make the flood flood worse in fact not diminish the flood control capacity I think we can actually improve upon it and we need improved environmental outcomes That was our challenge and we started back in 2014 the forecast informed reservoir operations or furow committee And many members of that are here and you can see we put together in a disciplinary team of collaborators Many that are here that will be some talking later on you've got the bureau reclamation the army corps of engineers usgs More importantly department of water resources for our purposes because they are bringing in the type of state support That's required for this and then our local partners in particular You have the university of san diego scripts institutional oceanography and now the cw3e that's located there Putting out this incredible atmospheric river research because that is where we've got to hone our resources Using what they had there We put together co-chairs my chief engineer j jaspers Deserves a lot of credit because he's able of blending those two hydrologic and climate science disciplines that ling was talking about He has unique capabilities to doing that. He teamed up with the dr. Marty ralph from uc san diego scripts And the rest of the members are here Crystallini obviously should be on this this list and somehow got left off. But look at that Uh, this is what it takes to solve complex issues Today in water management in a disciplinary team and I heard that theme earlier. I subscribe to that The team put out a preliminary viability assessment. Uh back in 2017 Uh, which concluded that in fact forecast informed reservoir operations Could be of value to water management in lake mendicino in addition the The pilot demonstration indicated that uh major Deviation requests should be put forward to the core of engineers and I'm I'm pleased to say that we've made some progress there I'll tell you about in just a minute The preliminary viability also had Notice that longer lead times from s2s forecasts would enhance and benefit the water supply and flood control purposes that we're dealing with and lastly that we need further research Into meteorology hydrology and reservoir operations. That's a common theme so 2019 We fortunately just this month received notice from the army core of engineers that the committee the furrow committee requested Which was a major deviation that was made in january of 2018 Just this month. We've received notice that we're able to manage 2019 with forecast informed reservoir operations as a control That's progress right Looking at this particular area right now. You see about a 10,000 acre foot Difference between the 68,400 acre foot. That's the flood pool up to 80,000 acre feet We're able to use forecasts now this year to demonstrate proof of concept in that reservoir And what we're trying to do is improve water supply at the end of the year Actually not make any flood control flood capacity worse In fact, hopefully improve that and have water left over for fisheries and environmental considerations like I mentioned earlier So that's our challenge We were given this opportunity because of great coordination Support from congress and and the state we were able to get initial money so that folks are not doing this for free That's not fair. You need to bring people in and the stuff does cost money We happen to coordinate on a major level and this is the result 2017 I mentioned earlier was The wettest year on record out in california remember that team about extremes and variability four-year drought broken by an atmospheric river Not only did we have one we had 17 Moderate the strong atmospheric rivers hit the west coast Typically, you're looking at a year that could be anywhere from three to five of these And with 17 it was the perfect year to use a very wet year to test our theory So that's what we'll be doing. We're able to hind cast back looking backwards into the year and Utilize this very wet year and 17 ars to see what the response would be and see if we couldn't do a better job than the current control manual Pretty basic but pretty ingenious work on modeling So bear with me here What you'll see this december through february through a major deviation Is the blue line is the actual observe? This is what the core did and would do for that year with a major with just regular operational criteria The red line is what we call the hybrid the virtual hybrid But the major deviation that we're going to operate to and you'll see in december this year at the beginning there The core starts releasing water back down to that lower level So those lines that go across that are dotted the blue line is what the current control manual is And the red would be the forecast informed reservoir level 10 percent more if that makes sense You see the core in december drops down and starts releasing to make room for what would possibly be Atmospheric rivers and high rain But we realized the forecast did not have those in there and we were able to start banking water in that december time period Jump forward again for those spikes and you start to see that through january and february We're tracking we're doing just about what the the uh observes was But when you when you look at the bottom trend here the reason we were not able to get uh We didn't have increased flooding is we're following the most flood-plown area Down below on this grid is hoplin right below lake mendicino. It floods quite a bit And that's the one we used to make sure we were not astrobating that problem So the high peaks are right here. They're observed You have the no increase in the peak flow down below for flooding And when you then jump forward from that period you'll see we added the months of april and may And that's really where the action comes in. That's the the spring season And what you'll see is the old way of doing business is the core Would have been taking the water that comes in and vacating that down around the may 10th time frame You see that drop that the blue observed Instead with the virtual hybrid and the zero informed you begin to hang on to that water because the threat of atmospheric rivers Diminishes into the spring and you're looking backwards 15 day forecast and every day you move it out forward It's pretty pretty straightforward. You're looking at forecast to help inform your your operational criteria So that by the end of may you're looking at this scenario and you store 8,000 acre feet of water in may going into the new year And uh, that's a significant amount of water and you wouldn't have expected this on the wettest year on record You start to see the power of this So i'm pleased that we were able to demonstrate this looking back with hind cast for 2017 I'm seriously pleased that we have a major deviation request that's been approved by the core and that now the final viability assessment is ready to uh We're working on that it has components the scientific r&d research which includes additional hydrology and engineering We're going to be looking and utilizing s2s forecast to help improve our precision on that with that viability assessment We're going to improve the ar detection And observation and monitoring to help inform that piece of it and then interim operations Ultimately, we're looking at technical studies that will be decision support tools to help us and inform how we will operate our reservoirs Obviously what folks want to know is can this be replicated and i'm saying not yet I'm saying we have to get this viability assessment done and done correctly because flooding is not Not something you want to play around with i don't want to take on and be cause causing additional flooding So we have to be conservative you have to demonstrate this using the best available science and you got to be pretty darn sure So this viability assessment is really important that you don't jump the gun But ultimately it is leading to a update of the water control manual that 1959 version that i spoke about We need to have forecast informed that and ideally the science is going to help us Produce a product that is not only more flexible and more accurate But something that you don't have to keep renewing it's very expensive to renew these water control manuals We want to do it once and have it evolve over time So in closing I'd like to say that I do believe that we're going to improve s2s forecasting that that we're on track If we work together if the national academy continues to take this on like you had a couple years ago And you keep making progress and we go from the five day out to the seven day Only the ten day and maybe a year that is going to help water managers throughout the entire west in fact the entire nation So in water supply planning another factor to consider would be in droughts Imagine if you knew a drought was coming and you had three months to prepare for that You'd be building up your water conservation portfolios You'd be budgeting something that would actually plan on delivering less water. So you'd be Putting in programs that would take that water and use it more efficiently and also would not hit your rate base Because that is unfortunately what happens in droughts Forecast and form reservoir operations as a result of all this coordination are going to be improved. I mentioned that with water supply and with flooding Uh conjunctive use management. It came up earlier. That's when you start integrating the groundwater models That usgs is quite good with with the hydraulic models the surface and groundwater modeling that's got to come together It is coming together, but not fast enough. It's got to be done I think on a watershed scale should be integrated into a national model Not unlike the gs flow is doing and the water center and you begin to build a much more precise portfolio of how Better more informed water management can occur And just to round that out to comment because we do run a sanitation facilities If you had s2s forecasting that was accurate You'd be making sure that you're less prone for for sewage spills and overflows Out in the sensitive areas particularly into the san francisco bay area And my last concluding one on the environmental side would be imagine if you had an s2s forecast And you're being asked to make environmental releases for fish purposes You would be more inclined to do that if you knew that water was going to be replaced in your reservoir It's a lot harder to make that decision to make a pulse flow occur when you don't know It's a flip of the coin whether it's going to be wet or dry So with that that's what I wanted to leave you with today And I appreciate very much the opportunity to be here and I don't care if I don't get the oscar That's okay. Thank you very much Cards are flying so pamela and then ravi Hi, Pam M. Shit north from Grumman. I'm on the back So one thing that you did not mention here. Maybe it doesn't maybe It doesn't impact you your water your watershed as much So you talked about atmospheric rivers and predicting those but you didn't talk about snowpack Yeah So can you say a few things about if you've considered that or other? Yeah, that's a great question in fact when you think about california You have to be considering snowpack the only difference is I mentioned earlier probably should have re-emphasized it The russian river is not a snowpack dependent system. We're fortunate for that. We have one less feature We have to worry about but you may believe me when janine starts talking about and others talk about california's water system It's no pack driven and atmospheric rivers can do one of two things they can fill your reservoirs But depending on the temperature they can actually decrease your volume of snow if you think about when they come in and at what elevation But they also can add to it So we've got to do a better job and that's a key feature that the if we take this system and start replicating it We're already looking at prado dam down in orange county Which is on the bay deltas system and has a connectivity, but someplace like fulsome Recently doing their spillway definitely looking at snowpack and snow melt and that has to be factored into your modeling Robbie and then david tetley Grand thank you for this talk. You do get my Oscar If I if I had one I'll give it to you. Okay. Um, you really Just kind of you're a poster child For how science can make society better. I wish you could get people like you up And talk to people who actually support science There might be a role you might want to think about but anyway, but I was slightly This is wonderful But the question though is that you're a pretty high tech high resourced Operation it seems to me But the rest of the country and the rest of the world is probably not there Yeah, what are the lessons? You're gonna give them well I have a follow-up question on that Okay, let me if I can't ask that and if you follow up Yeah, it's true. We're in Sonoma County, California. We're blessed. We also are heavily stressed I think there's a bit of an obligation and I mentioned some of the reasons why we do this Because my board is already integrated. They they understand it can't be all about water. There is flood control. There's sanitation There's renewable energy. I didn't have time to go into this But we made a major investment throughout my tenure and we're now delivering our water supply carbon-free Did that in 2015 that means it's all renewable that's coming into that system That's a sustainable approach We were I think we're the only one to do it, but I believe now others will follow So if we can be a template if we can do things and replicate what works We can cut down on the the time it takes to be number two and number three and you can learn Much like we have with fires. We had to learn from San Diego. We had to learn from Santa Barbara We had to learn from other places that got decimated now butte is asking us Can you help you learn as you go and that's what science is all about I will always be an advocate for this and whether you you're holding this meeting I would encourage you to do more of it and bring in Bring in my other the real engineers the ones who know this in and out bring chris and j back here My hope is that the academy will do that or come out west But we need your help. We are water managers But we're not going to do a good job without the longer term forecasting and the the seasonality that you're talking about today Wonderful, I think we'll go ahead and get started We have a really interesting afternoon panel Terry hope from this colorado school of minds is going to run the panel for us So I'm with no further ado. I'm going to pass it to her and she will introduce panelists and run the afternoon session Thank you. Great. Thank you. Kathy So i'm super excited to run this panel this afternoon Um, we're going to focus this afternoon kind of on this much more on the water management water use and management side So kind of following up on grants Kind of segue into this So I think this is a great opportunity for us to really hear from The stakeholders and users what they need from us and how we can help facilitate integration of our our science into their operations So kind of bringing us back to what this panel was about It's kind of looking at this research needs challenges and opportunities for all of us to engage I'm going to introduce the panel. I'll have Tersu Asafa from Tampa Bay water authority David rath from bureau of reclamation Jeanine Jones from california department of water resources and roger poverty from noah So I think we're going to go in that order also for our talks today And and again just kind of bringing us back to the goals that were listed at the beginning of and in our Agenda for today Really the goals of the entire day today are to really nurture ongoing discussions on research challenges and opportunity in the seasonal to sub seasonal forecasting arena And look at opportunities for kind of connecting the hydrologic atmosphere and climate modeling communities And how we can better inform applications and integrations into stakeholder decision making tools And then again I think the big goal of this today was also to kind of think about how we can exchange and interact more between the boards And our stakeholder communities and really look for future projects to kind of move us forward Um, so with that I'll let the speakers go ahead and we'll start with Tersu and um I'm going to have them each I can think they're each doing 10 to 15 minutes Maybe so we'll hold questions unless there's a clarifying question I think until the end and then we'll have a I think plenty of time Hopefully about an hour or so for some really great in depth discussion I've got some questions to generate some discussion if we need it. So with that Good afternoon happy to be here Good afternoon happy to be here it's always good to see everybody in one place we started today with Challenge in seasonal forecast from climate perspective We heard where we are and where we could be going and then the hydrology the challenge of hydrologic projection and Changing that climate information into hydrology Then put that together. That's the problem I have So we have to make a decision on Real time to some seasonal in order to bring that to for for making a decision So time of day water before I get there. I want to tell you that A lot of utilities in the u.s. Have tremendous amount of debt for infrastructure Most of them they have 50 percent A lot of the revenue goes to towards the infrastructure building new supply I want to argue here that what we do in a seasonal scale Is also related to what we do in the in terms of infrastructure Over the last decade and a half at time of a water. I learned a thing or two how these seemingly unrelated Two aspects are related I lead the planning and decision support group planning planning is basically looking at the future with our supply The decision support is Who's doing the modeling now typically people don't Really remember about us as long as there is shortage or there is a bill which coming up because of infrastructure Hey, your water bill is going up Then people really remember what we are doing. So most of the time what we do is behind the scene It's okay, but that's how we do To give you a perspective Grant mentioned his bias for california My bias is for florida and he's actually generous enough to give you the whole map I'm not showing the whole map of u.s. Even Time of the water is at West central Cost you can see we have we are whole shared water delivery for three counties and three cities The right side you will see that it's just Some of our infrastructure. So we have a very diversified portfolio Groundwater than the one in the left top panel. You see is the ground water a typical well filled And then also surface water withdrawal Off-site reservoir and a desalinated sea water. We used to say we are The largest in america, but san diego came back and we now we are saying the largest in the east coast Which still works So the challenge is that 20 years ago We are actually celebrating our 20 years now 20 years ago. Everything was coming from groundwater Because of that shift to diff to diversify our portfolio. We actually increased our risk profile Now we have to depend on climate forecast hydrology to be able to supply water So from 100 ground water today, we are about 65 groundwater about 30 35 surface water And then 5 to 10 percent desalinated sea water now We have a multi-scale decision support tool. So i'm not going to go there and if you guys want I have a reference here That starts from week to week operations and then monthly operations Seasonal to multi-decade So we try to come up with all these hydrologic models at a different time scale and make decisions the one on the left you see is Typical skater operations. So my group typically what they do does is it forecast the next week's demand and supply Optimize from where source we come provide and send it back to the operators who actually Make that happen The table which is very difficult to see but i'm going to show you what gets in is a seasonal allocation level decision support tools So in the morning, we heard about some of the probabilistic forecasts which are available how we get there for us is As you know, most of the time what we get is a large-scale indicator So that bias correcting downscaling and making it to something that's useful local That's what we have been spending our time. So typically what we do is we use this large-scale ENSO or probabilistic Forecast and then we generate some rainfall which is consistent with our area and that feeds into the stream flows And then that will be a decision-making every month We get together and the first thing we talked about is about climate. What's going to be what's the ENSO state? What do we expect for next two three months and so on and on the right side? You can see actually a Uh A report so this is from november 1 and this is what comes from november 1 and this is like Summer in christmas time. It doesn't typically happen like this for us But this is because we are expecting a lino or we are told so So we have to use that information to come up with forecasts for stream flows So in addition to stream flows, then it has to go to other works of the agencies if this doesn't play out I'm going to blame you guys because it comes from all those climate information um, the next one I want to show you is and then We also use there was a great question. How seasonal operations is tied to infrastructure investment This is key. I want to mention. So we have what we call a residual risk management tool The idea is that we know 20 years from now. What is the demand? We'll forecast at least As much as good as what they currently economists say that's another by the way Uncertainty for us. There is economic uncertainty where we forecast our demand Supplies forecasted also and then you try to mix that supply gap You wouldn't be I wouldn't be working in time of a water if I if I say hey, we have to build For 99.9 reliability of water So what we do is that we select start some point of reliability And then we try to match the rest as we call it a residual risk management or water shortage mitigation plan Now this tool actually looks three months ahead. This is a typical Flow we generate through our reservoir and based on this is just an example from 2007 So the green and the red are certain Alarm levels if it passed actually below the red, it's called the water like crisis So everybody has to conserve water and so on now we put ourselves in a way That we want to use this nine months ahead and we are declaring actually before this happened nine nine months Nine days ahead. So as you can imagine, we are using this tool And if this tool doesn't get buy-in Because the people watch every day what we do every month what we do And we cannot just trigger the whole area is in shortage and suddenly it's not the case. You don't want to lose the trust from the the consumers, but that is how we tied up a short term Operations into a long-term planning. In fact, we are trying to do we are doing research right now How we can see our risk of failures and we we have some planning risk of failures So when we reach that risk of failures, maybe a trigger to bring a new supply source So there is a lot of things going on with this kind of model But the issue is how good is our projection? So we so any advancement that we heard in the morning will be useful for us So another typical example, I'm giving one example here also Last year the operation came and they told us, you know, we want to draw down the reservoir Sometime in July and we want to do these operations and then tell us we will be okay by the beginning We can go for the next water year. That's at the end of the summer Whether we should be full reservoir or not Well, Akademika, this is exciting for us. It's whoa. We can crunch this number You have probabilistic forecast and we told them what you see here actually is the greens are your probability of feeling the reservoir by the end of the summer And we are starting at May So as you can imagine, there's a lot of challenge on getting this kind of information We told them, hey, you have about 18 percent to get there. So you guys want to move this or not So and that's the challenge first We have to come up with something that to give them some actionable So the right side is is an example of we have these huge pumps of operations So depending on flows condition, they want to to know am I going to operate? 30 days 90 days this huge pumps and there's a lot of cost and the one so all these are based on What we do at the seasonal level all the projections So what's important for us is not to lose the trust of those people. There was a question in the morning I'm almost done. I'm going to give it back regarding the 2014 El Nino So I can give you an example of the 2018 La Nina so 2017 it was perfect. We forked the forecast was good. We told them we will be in trouble Everybody was ready. We were good But the 2018 as some of you know was actually at the beginning it was an El Nino then it turns out to be a La Nina So I was there without my group say we are expecting this after two months to say, what is that El Nino? I don't know Ask somebody else. So those are the things and in a way we like to use all this information But when things may not play out then we have to be careful on that so that we don't lose a trust from them So it's not all me. I have a bunch of people there actually dedicated staff who are doing all this work So I would like to give them a shout out to all the people who are in my department. That's all So Dave is up next some bureau of automation Good afternoon and thanks for having me This is an exciting time. I think in this area And what I hope to communicate is a variety of things But yesterday I had the opportunity to speak to a smaller assemblage of you all and one of the things I pitched then that I'm going to pitch now in a slightly different way but similarly Was again a focus on the need to bring your community or the community of research Into a way that is more directly informing practice or operations types of activities Not a new concept of the bridge from research to application But I think there is an opportunity in this organization is the right one to do that That's a vague concept. So let me pitch something more specific based off of what I've heard today As well as just thinking about how I wasn't helpful with that concept yesterday And that is that uh, I think in a short term one way to do that is simply to have another meeting Or workshop or conference whatever you want to call it Whereby the federal community and states and locals frankly Can can share real world decision making from policies to tools because I think that there Is a lack of communication and that would hopefully better set a target for research practices to inform those things I don't think it's fair for the federal community or others to simply say we're not being informed We need to educate better. Uh, so that would be my pitch as a as a quantitative outcome from from this meeting Whereby we can tell you which tools we use where we use them how we use them Which policies go into these things? Where are the constraints and and helps set the target for your future research? And to that extent, I think that's what I hope to share here Today to a certain to a certain I'm going to start at the end And move backwards Now is an exciting time for this topic Um as you already heard this morning from sonoma Fero forecast informed reservoir operations or the use of sub seasonal to seasonal forecasting to actually inform Water management in the united states now is the time lake mendicino is one of the first In the western united states It's something that ties together Aspects of flood control the core of engineers primary responsibility and water supply, which is a local in this case state local type of responsibility But reclamation at federal reservoirs, which mendicino is not Not a reclamation reservoir We have that same type of goal And f2s is really the bridge Whereby we can work with the core Or the constraints from supply and demand in one unified way So mendicino was talked about i'm not going to take some of my precious time on that Uh, here we go Another a good example of the time is now is fulsome. It was mentioned earlier The joint federal spillway project at fulsome is essentially complete in terms of construction Operations are taking place Anticipated the water control manual May be signed as early as january of 2019 Just this morning. I got a copy of the october 2018 Preview of that document And this is probably the largest forecast informed proposed operations in the united states Depending on where you're counting how you're counting that sort of thing, but it's a big deal And what's being proposed here is a very static rule curve That the core of engineers Have been using forever And if you're if you're a engineer within the core, you've been doing that very successfully Very I don't know of a lot of examples when you have evacuated a flood A flood pool per core guidance and how to flood that caused damages or significant damages The way that they've done it is super great if you're interested in flood control if you're interested in water supply Anytime that there's empty space at the end of that flood control season. You're not doing a good job So at fulsome, uh, the the concept here is that there's a static Flood control pool and then there is this variable And then there's this variable space that's shown there between 400 and 600 That allows for a significant amount of storage capacity that's informed through Forecasts not necessarily season or seasonal or sub seasonal in this case. This is informed directly by um River forecast center no a product looking out at um a number of days to a week that informs that type of thing But again, uh, the time is now. This is a significant um I don't know if improvements are right word But approach that the federal government is taking particularly these agencies and the On these timescales in terms of forecasting are informing real-world decisions right now. Um, and I think that's great Going backwards doesn't help when you have a picture coming after the slide, but that's uh fulsome in the jfp Again, uh real-world needs on this timescale the colorado river basin Is experiencing historic Uh, not tree ring historic, but historic drought Many of you focus on that here The colorado river is informed by real-world management is informed by Outlooks two-year outlooks a 24 month study that uh has official release dates twice a year You guys are all Uh, I don't know all of you, but you all seem smart enough to do the math that If you're looking out 24 months two times a year, uh, you need a forecast that has uh some range to it Currently and historically There's a switch from where we think we have knowledge to climatology within that 24 months Uh informing those processes would certainly help manage that system If we improve outlooks and timescales on the colorado river Right now There has never yet been a shortage declared on the lower colorado There is a significant chance of that happening for the first time in history since Hoover and me have been built Looks like there's a picture being taken i'll wait The last thing I wanted to talk about again We recognize the need for these types of improvements and i'm glad that you do as well One thing that i'd like to highlight with the remainder of my time, which I have no idea how much there is Five minutes three minutes Five minutes five minutes. Thank you I take it out of rogers time by the way Is uh something that we started um last This project we started last year, but in the past four or five years reclamation Has embarked on prize challenges um prize challenges if you think The x project x prize An award to for the first private entity to put a rock into space We're not we're not talking that scale But the concept here is that the federal government puts out a purse For somebody to achieve something you only pay the purse if somebody is able to do it Um and From an economic standpoint. It is a great investment to federal dollars We get asked at all levels of the budgetary process. Why is this good? It's good because if you count the number of intelligent people spending hours working on our projects It's far outweighs the investment that we're making far outweighs So one thing we focus on our biggest prize challenge to date. We did last year We called it the forecast rodeo. It was a year long real-time sub-seasonal forecasting Contest the total number of prizes available for this were 800 thousand dollars of federal dollars It seems like a lot of money And what what what the requirements were are these bullets, but essentially Competitors had to produce a one by one degree grid for the west in the united states projecting temperature and precipitation At two different outlooks three to four week and five to six weeks They had to produce these forecasts every two weeks for 13 months And again the domain in the 17 western states basically west of the mississippi To be eligible to win a prize A competitor had to beat Two different benchmarks one being a damp persistence model and the other being noah's cfs version two so state of the art Weather or climate model depending on what you want to call it and a very fundamental basic damp persistence statistical approach To win the prize as well competitors had to submit their code documentation and produce the hindcats verification So it was a complicated project for a competitor to be a part of But our hope was that by that there was enough incentive there if that people would want to compete And what we found is a complicated slide But so this completed in june of this past year june of 2018 that was the end of the 13 months of the competition and Just to show Kind of what was out there is again So there's four variables temperature and precipitation for two different outlooks So week three and four temperature precipitation you see in bold the two benchmarks To damp persistence and cfs version two. So there were three competitors that beat the benchmarks Which I think We hope to learn quite a bit from but we're very happy for this outcome It means that there are very innovative people out there that can beat The big dogs in the room basically, I mean that's cfs version two. That's a significant federal investment in time that goes into that Similarly for weeks five and six temperature, we had one competitor beat cfs version two for weeks three and four precipitation there were three competitors that beat Benchmarks and for precipitation There were a number of competitors that beat precipitate beat the benchmarks So this was our prize challenge We can talk a lot about this and dig into it But the pitch I'd like to make again to all of the smart people in this room Particularly the academics who can compete in this is we're going to launch the same prize challenge again And we'd love to see more competitors This next round Interestingly, we had a competitor A part of this that felt that they Were better off not taking a significant portion of the potential award money because they They believe that the monetary value of their product Is more outside of what we offer in the prize And we did not take ip intellectual property associated with this prize challenge But we took the right to see it And that even that was enough That the value was more to this This team or individual To seek privatization which from our perspective is a fantastic outcome to I mean our job in the federal role is to Encourage the development whether it be in the private sector or in the federal government So for those of you in this room That seek grants seek opportunities We think that there's a lot of economic value to these things And we encourage you to participate in the competition in the next round I believe that's my five minutes And I'll end with my opening comments, which is We would like to help set the target for research And I think there's a fantastic opportunity To discuss exactly what the target looks like in federal water management I think it would be educational perhaps disheartening to some of you But certainly educational in terms of what the real world practice is Throughout the united states. So thank you very much for this opportunity Great. Thank you, david and we all love a good competition. So Next up is jenny jones from california department water resources Okay, good afternoon everybody. I know that this is the after lunch time slot So I will make a point of being provocative enough to hopefully keep you awake Actually, when I talk about that this subject, that's not hard So we ask this question every year about this time of year What's the water you're going to be like because this is very very important for those of us in the western us There we go. So The question was asked earlier about can they do better than the old farmers almanac? Well, here is cpc's Report card on their skill score for their seasonal outlooks two-week lead precipitation for djf Which is a very the most important part of our water year in california in terms of precipitation um Well, the good news is that some parts of the country have a an enzo signal not california Not of anything of any significance from the colorado river basin So we think of el nino as el no show and basically This is a skill of zero realistically speaking. It has no value for water management at this point So that's not a good thing So we've wanted better forecast of an s2s time scale for a long time. We did a great report at dwr Following up a big drought in the 1970s desirable to develop additional skill in forecasting the weather a month tense What's really needed for on m of a complex water supply project is a long-term projection at least here in advance With a high degree of reliability So, you know, please get on with it But even as recently as 2015 when noa published its first ever service assessment for drought for the california drought in 2014 They went out and interviewed a wide cross section of stakeholders. The number one comment they got was Hey, we need seasonal precipitation forecasting capability. Well, hello. It took you that long to figure that out You know, this is basic and fundamental and something that we could really use for our decision making So taking a riff here off of noa's research funnel, which Seems to resonate With them. This is the seasonal water management funnel for decision making in california The top of the funnel the beginning of the wet season is where decisions have the most value We're talking about decisions on the order of hundreds of millions of dollars particularly for the drought Should we go to the legislature and ask for major infusions of emergency drought money? Should we do a state water banking program drought water bank program as we have done in past droughts? Should we put a whole lot of money into water conservation programs do local agencies negotiate temporary water transfers? But as the season goes and at the beginning of the season You have quite a bit of discretion in terms of being able to put something in place with enough lead time for decision making Lead time is golden and that's why we're really focusing on precip here is we need the lead time runoff forecasts are good We actually do relatively well on them Particularly in comparison to those longer term precip forecasts by the time you get down to the end of the wet season You have no discretion left to play with and you're down to business as usual in terms of the value in your decision making So grant and his remarks was talking about just the value of getting something out to two weeks You know, that's a weather time scale Imagine if you could get that to two months three months Now I know obviously your forecast certainty tends to get much better as you get into shorter lead times But we don't want you to have the easy challenge. We want you to have the stretch challenge Which is give me these answers at a seasonal schedule And we were asked to talk about challenges opportunities and research needs in these remarks So my challenges well, obviously the federal government needs to put more research money into this S2s has really been the poor stepchild of research in my opinion You know a lot of investment at the longer term climate change time scales through the You know work over several decades In that area, you know money has gone into weather forecasting, but not a whole lot into s2s So we were delighted to see when the weather research act was passed in 2017. It was a good first start However, it needs to be reauthorized now because it was only a two-year authorization Another challenge given the what was teed up in the weather research act is Well, how exactly does that fit in Noah's organizational structure? One of the challenges for us has been figuring out how we readily engage Noah where you have little pieces of s2s sort of Scattered across a variety of different places than Noah on the research side as well as on the operation side And frankly, we need a really focused effort within Noah something similar to the hurricane Forecasting improvement project, which I think is probably one of the best models out there that we could look at And so I said that working with Noah was a challenge. Well, it's even harder to work with academia All of the work that we have been doing in california to try and advance the cause of s2s forecasting I almost put in a slide of the cartoon with a big game hunter Socking something with a dark with a tranquilizer dart gun the target for that tranquilizer dart gun would be an atmospheric scientist It's been very difficult to find folks in the atmospheric science community who are interested willing and able To work with us to do useful things as opposed to oh, well, let's publish a paper on something So the social scientists folks use the term push versus pull So from our perspective on this it's been all full and it also appears to be a really small research community because Hello researchers follow the dollars and a lot of the dollars have been going in other areas Opportunities I mentioned the weather research act. That's a huge one We need to get that reauthorized and we need to use that act to get going on some geographically focused pilot projects You saw the cpc map of skill scores that I showed Some areas in the country have a little bit of skill some don't well Hey, the u.s. Is a big country you would expect that some phenomenon Give you more predictability in parts of the country versus others for us in the west coast atmospheric rivers So we think of geological things to start doing some specific pilot pilot projects to get skill in those areas where there is Not skill such as winter precip in the western u.s. Or you know for the ag folks in the midwest summer precip for them Perhaps is important Noah we would suggest that in order to take advantage of implementation of the weather research act We need Noah to develop a some kind of cross line office organizational structure That brings together a very specific focus within Noah on this because for example We just executed a contract with Noah for work in week zero to four So we go out two weeks into sub seasonal 750k a year for five years to fund Noah to improve some of its Basically weather-ish stuff as opposed to s to s stuff But it was quite a discussion to figure out well who and Noah do we contract with I'd initially approach Dave DeWitt and I want to give a big shout out to Dave for his willingness to work with us and to Be our partner in this but you know a lot of that work was actually being done in s-roll So we ended up doing the contract with s-roll But you know there's also a relationship with gsdl which i'll touch on in a minute So you know how do we bring this together in one place and have kind of a one-stop shopping and decision point for s2s and Noah Um, I mentioned you know from our perspective one thing we can do as a state even though we are not a research funding entity Uh, we can fund folks to do specific projects for us that may add value So we have a contract with Wayne for example at uh on behalf of nasa jpl To develop some experimental forecasts of atmospheric rivers for us going out week three week four And we're now amending that to do experimental forecasts of west coast ridging Because if we have a strong ridge in place in the west coast that probably means we're not getting rain And that may have more prediction skill than trying to forecast atmospheric river probabilities And that was actually analogous. I was very interested to see the slide that andy showed this morning in his talk We have a contract that we're negotiating now that will involve scripts and suny One component of which since this is a question that came up this morning was about the use of limb One of the tasks in that contract will be about the use of linear inverse modeling to look at a six-week Experimental forecast for atmospheric rivers or the ability to do so So we are you know trying to um Come up with as many things as we can particularly the other question It was asked this morning about bridging the gap between qpf's and the seasonal outlooks. Well, maybe it sees experimental Forecasts three four five six weeks out on ars. That's something that we've been tracking at this point So, you know, I'm not going to tell you what my science research needs are because you guys literally wrote the book That report was a 10-year vision for um the forecast on s2s timescale being as useful as the weather forecast today So I just need the research that answers those two questions accurately And could we please do this within five years because we do have water management challenges in california and we you know Like to get this moving along just saying And to point out this is we have many atmospheric scientists in the audience I you guys always have to have the cool graphics So this is a cool graphic that I stole from dwayne And this is actually an example of one of the work products that he's doing for us under our contract And he actually showed another version of this in one of his slides this morning But just an example of the kinds of things that we're trying to Pursue in working with the research community in getting very targeted products that could be useful And another example of something that I think is an Interesting tending opportunity that's coming up some recent research that gfdl published Regarding the ability to seasonally predict western snowpack, which would be so hugely useful for us and Dave and sarah catnip and Tom hamill and others at my request work together to put together a perspective So what would it actually take to do this not just quote-unquote study western snowpack? But develop a target with a plan a process a schedule and an idea of cost Of what would what could you do in five years with this and at my request? They presented at our recent western states water council fall meeting This is the kind of thing that we need to see from noa from the research community Specific products specific time periods specific tasks. What does it take? It makes it much easier to sell the research in our world So with that I would just to point out not just a california perspective I'm here also on behalf of western states water council We see a huge need for improving s2s forecasting and we're very interested in working with folks Including the folks who signed noa's budget to try and get resources into this task. So that's where I wrap up Great. Thank you, janine And last rogers up About 15 minutes hopefully yay greetings, uh, so i'm roger for already i'm going to talk a little bit about the issue of drought and What a supply and demand there's a lot of work a lot of partnerships We have with the western governors with janine who helped us lead a lot of this stuff But the whole idea is what's the breadth of the kinds of questions we're trying to deal with Cross time scales and then specifically S2s and why that's the what kind of challenges that poses a lot of these fit into very Direct networks that we've developed for early warning around the country that's made up of watershed managers a very diverse group that i'll point to in a second but watershed managers ecosystem managers certainly Even broader issues such as lower and upper basin challenges in the colorado river And then some international aspects So i'm going to mention the big challenge we have in the drought world on this time scale, which are flash droughts I'll talk about the focusing event the thing that really brought the attention of short-term Drought expansion and severity Into our thinking then something that happened in the upper midwest that leads to some of janine's questions That we did not predict which is the upper midwest drought in 2017 And then something that's beginning to challenge a lot of our stakeholders and our partners the issue of compound events droughts to floods and back and then the combination of drought and heat And why that matters and we're getting a lot of attention from the health community on exactly that question And some the cases i'll show our combination of national and international applications in ag and the famine early warning system network And all of these are work we've done to develop through not only in the national integrated droughts information system with the international drought management program At wmo and the scientific basis therein Finally it'll be where the research and information products advances actually needed So there's a big list. There's the billion dollar droughts affecting the consumer price index done by ncei And why do we care well across timescales? You can see where we have drought related risks and where they tend to impact The consumer price index this is the first time an analysis like that has ever been done for a whole Long time since the 1930s drought We've always thought of drought as an agricultural impact and boy You could get the insured losses and when you look at the billion dollar disasters You almost see nothing in the west from a billion dollar disasters from drought yet. We know That the cost as it filters through the water systems and everything else are in fact in the billions in fact We estimate that the 2012 drought when you accumulate all of the risk and we this work with munich re was actually 100 billion dollars An hurricane sandy was about 60 to 70 billion. It's just all happened at once So we have to keep in mind these ideas of the cumulative impacts and cumulative risk And of course as people mentioned won't revisit the other issue that this is central to wildfires And we can go back we did a full analysis of why we're seeing some of the Explosions not just in the long-term trends, but what the short term means in the context of explosive events So drought is typically the largest driver of crop disaster assistance And indemnity payments we estimated in 2012 about nine billion dollars was paid out Even though the cumulative impact losses were about a hundred billion dollars. So what does this mean for us? What actually happened this focus event the reason why I call it that that led to the creation of the national drought resilience partnership A multi agency group that is functioning Now chaired by the epa head of water davros and bill northy who's from the usda conservation authority And here's the drought. There's 2011 There's may 2012 july 2012 january 2013 And the jump we can see from around may to july Went from about 30 it actually ended up being 66 by september of the aerial extent of drought Now that's pretty dramatic from the standpoint of the evolution of a drought event I always tell people when dealing with drought the onset is not quite the issue the onset we have got Markets we've got storage It's when we've used up those buffers and a drought has intensified that you actually have the risk and where we're seeing The importance of the s2s timescale is in terms of the intensification in short periods relative to drought So there's that big leap and this was done through the no drought task force the percent of us experience monitoring to say severe drought suddenly increased But even a perfect sea surface temperature prediction would likely have captured much less than half of the total variance And what is the rest? A complete explanation of droughts and both not just the ocean boundary forcing But also the particular sequence of internal variability And this is what we focus on in the context of trying to get the sense of why those leaps have occurred One of the indices we've developed and it's a parallel to the evaporative stress index is The thirst of the atmosphere how much water does the atmosphere pick up? There's the two-week evaporative demand drought index There's the us drought monitor next to it as you can see the atmosphere is trying starting to pull water out of the system by june by july and there's august And so due to these land oh atmosphere feedback the evaporative demand actually reflected the surface moisture conditions much ahead of even the drought monitor and it happened over this time frame that is so critical to whether or not We saw the ballooning of this drought event. But why did that matter? For a lot of decisions and we worked with usda and others on this the reserve Forward supply going through the year people were reducing herd sizes weaning calves early Purchasing feed renting additional pasture one of the strongest adaptive mechanisms We have in the united states is actually the conservation reserve program 2.4 million acres that were put aside after the dust bowl to give us some buffer in the system the application for government assistance incorporating yearly the yearling livestock And then stocking conservatively these are actual decisions that people make and then it matters a great deal To where they get the loans to do these things so the banking industry has taken now a big interest on whether or not Loans are actually Viable can they prove the premium on them and this is actually a critical thing from the time frame of how these decisions are made But something else was going on as people in this room No, the grain and oil seeds dominate the salt bone traffic on the on the Mississippi Accounting for one about half a million Half of the 80 million metric tons if we amortize all of the traffic that goes up and down Each year and including repeat traffic just the cost of things that travel on the Mississippi back and forth It's over a trillion dollars a year Just things that move all the way along the Mississippi It is the most economically viable is the most economically important watershed in the world and During that event we had huge impacts both from dredging in the short term and most significantly In the very short term the inability of grain elevators to load things onto barges Even though it had been low coming from may to july everything just dropped altogether So as a result the mississippi river cities and towns initiatives the coalition of 70 over 70 mayors is working with us very closely on What does it mean for their communities when a large-scale event like that happens? What it matters to the trade footprint of the united states and the usda is helping to fund this and how this then filters into the activities for relief and for alternative sources of Not just employment, but recreation On these 80 towns that's actually happening and in progress right now came out of this event And it's all because of that Surprising jump at that time So we're talking about why we need to know this stuff for a little bit Why we need to know those time frames and of course another one of those and we saw in california as well in 2016 2017 Was this issue from too much too little and back From 2010 2012 those were three years in which corneal fell for the first time since the dust bowl We have little sense of how things shifted From very wet to very dry so quickly and that transition from wet to dry Is actually one of the most critical things in the context of planning. We've seen other things like that in 19 early 1990s We had events like that on the colorado river. It's just that 87 to 92 was a dry period And so the reservoirs refilled we saw it again in 1983 When the when lake the reservoir on Lake powell almost broke the idea here is what changes as we see droughts to floods and back again And that rapid transition actually occurs on this time frame and we have little sense of how that happened So in the nightest reauthorization and in the new bill that has been approved by the senate and is in the house right now There's strong and actually quite honestly and give janine a lot of credit for this as well A lot of language on including research related to the role of extreme weather events and variability in ameliorating drought or In making us think we've ameliorated drought as I always like to tell people early warning is Like taking your car to the mechanic and she says I couldn't fix your brakes. So I fixed your horn It's a warning. It doesn't mean we're doing anything about it, right? And so the idea though is putting this kind of information into our early warning system But we have a predictability problem the upper midwest drought onset was not forecasted lead time sufficient for early warning To lead to early action. No mistake on our parts everywhere. CPC psd. All of us were using the best available science but we could not pick this one up and it led to a lot of impact and barley and wheat production on In this part of the world, right But the real issue that happened In there was this rapid soil moisture decline And we do not yet know why that happened Look at the time frame on which it happened in a matter of weeks to about two months in which you saw that very rapid Decline in soil moisture and that's a question for us to think about in terms Of the types of questions we want to answer So In another area of work that we do we're very closely with the famine early warning system network We help create the international drought management program that deals with drought risk and drought impacts When we go from emergency and response We're usually behind the curve by the time the drought appeals occur And by the time a drought response happens it's too late and the question is What does Research on the srs timeframe give us in terms of being able to manage this process And I can go in great detail about this particular case This is one of the successes of the use of a forecast on that time frame Understanding the and so signal and then what that meant not simply for a wetter or drier season But what events the number of events within that season that led to being wetter or drier There's the food security map In february 2016 and there's the food security map in june 2016 a lot lower risk for food security The whole idea Was that this drought in ethiopia was actually the worst drought in 60 years worse than the 1984 drought that was You know live aid and band aid and everything else right which killed the 400 to 600 thousand people in this case This humanitarian crisis actually did not occur Because of the work of usgs nasa noa And partners on the ground on mapping household food security being able to get food in place on this time frame We did not see A collapse in ethiopia as a result you didn't know about this It happened in somalia because it did not become a crisis And so that occurred actually on the time frame that we're talking about So widespread acute food insecurity was avoided in ethiopia in 2015 2016 because of early warning of sub seasonal potential food insecurity And the social safety nets that had been put into place as a result of fuse net and so on the coordinated information system and services Actually led us to be able to assess food insecurity and support Resource prepositioning and that link Occurred the new director of fuse net was actually my my deputy at night There's a strong link between these two framings and how we think through these issues Something else we're beginning to see and this is a really cool study from a mirage kushak and others is exactly the droughts Warming faster than the average climate But what we're seeing are more heat waves when we do have a drought And so the heat wave question becomes a big issue for the health community And we're seeing it throughout the united states that idea of the temperature shifts When you do have a drought is actually not simply a health issue as people in the room know It affects what you see in vegetation. It affects what you see in evapotranspiration We estimate Dave we lose about what one one and a half to two million acre feet of water from just heat on all the reservoirs on the colorado basin And how does this impact us? the outside of tropical hurricanes The combination of drought and heat is actually a mortality risk in the united states that we don't fully appreciate All those three thousand deaths from those the percent frequency and the costs are much higher than we actually think when we add these two together $240 billion calculated over that time And we're seeing this There's the southwest for the temperature changes that you see all throughout this period And then when you have drought clearly a link between drought and heat Yeah, that's obvious until you begin to think in terms of heat waves being the heat that you're actually constructing during that time There's just an example. There's a fifth precipitation for For 2014. It was the fifth on record but the thing that happened was the warmest November to April mean temperature that combination certainly played a role in exacerbating the california drought There's an increasing set of things. I sit on something called the national drought resilience partnership across that group homeland security Energy and others are beginning to look at how drought-related critical infrastructure Hazards are affecting us on the water energy food nexus And I can go through the list of things on infrastructure And this is all new because for a long time We thought of drought as simply affecting agriculture We're beginning to see it affecting the functioning of infrastructure Not just transportation, but the functioning of our electric lines things like that when you get the combination of heat waves and drought So what are the research needs for monitoring and a lot of this comes from work from dave de witt and a whole host of others Using new observations soil moisture getting the land surface initialization. I'll mention that in a sec Identifying the sources of predictability on these timescales given a changed base state and improvements in in By using multiple models and a set of initial conditions that I'll mention in a second And there it is our case is really beginning to get a better sense of the not only the systematic biases in the models But how the land surface initialization is occurring because once that feedback starts happening We see these ballooning of large drought events So from the operational climate prediction monitoring products This is from dave and a whole host of others certainly anso and mjo monitoring the link between the seasonal and how that modulates The week three to four temperature and precipitation outlooks the drought monitor But really the monthly and seasonal outlook and the role That evaporate transpiration and evaporative demand both plays and the land surface conditions that affects our outlooks So taking that from that standpoint, there's the sources of predictability the way we look at it But the main p thing is the fault is brutus is not in our stars, but in our boundary conditions Thank you. Thank you. Thank you Great. Thank you. Roger. Um, so great Presentations there and I think a lot for us to think about for uh discussion. We've got about an hour Assuming we go till 3 p.m. Um, so I'll take questions. But before um, We do that. I wanted to say I contacted a Colleague at NCAR and there's two papers that are out on the national water model The early papers and they're I guess they're in the process of drafting more but I sent them to ravi and kathy So we'll make those available. So the ones in journal of hydrology And one is in jarra. So there are two papers kind of from early development of the national water model. So those will be out there Um, so with that, I guess we'll open it up for questions I do have some questions that I kind of came up with there's a few of my colleagues on the on basque And they're put up there. Um, I think that third bullet We heard a lot about kind of timeframes at least from uh, genean and what's kind of needed for her Agency and group But these are some things to keep in the back of the mind A lot of the first ones are kind of like what are your what are your needs? In from the agencies and stakeholders from us A question about uncertainty quantification and the products that you're using um modeling capabilities and observations needed Currently that you think we can provide or help develop And then I I kind of wanted to end hopefully with a little bit on workforce development And what do you see as kind of needed in your future employee those of us that are academics in the room? You know What skills do you see that your employees need what's lacking? I mean something for us to think about if we go back to our institution So with that I'll open it up for questions And maybe we need a coffee break All right, so rabbi you can start us rolling This is absolutely a fascinating session Um, not just because roger spoke Yeah I had a kind of system question It occurs to me that the way you manage water Maybe it's akin to the way you manage uh other utilities in some ways Multiple sources of water multiple Issues and how you deal with it do you See that as a model for dealing with it, you know Let let me be more specific for example Tampa Bay Do you have enough capacity in desalination? To take up for any loss you might have in other systems Now yeah So right now it's only five to ten percent of the The total that come from desalinated sea water In the future one of the planning we're looking is expanding that source as well as other sources do But I will tell you right now. It's almost four times expensive than the groundwater And that's the most expensive source. So we have to figure out how to Balance and people doesn't want to pay anymore Yeah, the last seven years we haven't increased one thing And it seems that people are getting used to this no more increase and that's the challenge I'm sorry to push on this but you know people seem to pay for gasoline Past changes much larger than that and we can do without gasoline for significant amounts of time But not without water So it seems to me there's something that's missing in that equation In other words in all the different water management systems Do you have redundancies and multiple? kind of Sources of water that you manage You know the saying you get what you pay for Large water utilities have the capacity because they have large rate payer bases And typically a lot of staff resources to afford higher levels of reliability of supply In california where we see our immediate public health and safety impacts with respect to drinking water and droughts Tends to come from small water systems Particularly small water systems on fractured rock groundwater That may only have a few hundred or a couple thousand connections Because they don't have a large enough rate base to be able to afford reliability And that's where we end up going in as the state and perhaps doing emergency response actions during a drought such as water haulage bulk water haulage Even drilling wells etc of four local agencies. So it's very much a locally specific impact Other comments from the panel on that or David Just to reiterate janine's comment again and At a lunch conversation on the opposite end of the spectrum are things like the southern Nevada water authority Which has a very large rate base has recently spent close to a billion dollars in terms of ensuring some reliability through adding a An additional intake into lake meat such that they saw the risks of Of lake meat elevations exceeding their risk tolerance and had the rate base to Increase reliability through that standpoint And i'll just add speaking to this morning's conversation on the Importance for lack of a better word of aquifers is that the federal government has provided subsidies for decades? um on In times of drought, what are we going to do? We're going to drill more wells um, it wasn't until recently that policies have shifted to discourage additional well during times of drought and shifting the funding for drought preparation That doesn't solve the issue But the historic response has been to build reliability by tapping into those aquifers Robbie, I think there's a fundamental backdrop, you know the base question that I think you're asking which is The effort to which we have Redundancy in our system and the effort to which we have pricing in our system And and it's pretty clear That whenever we have created redundancy in water systems We have then developed enough to use that redundancy in other words Whenever we think we've saved something development has pushed forward to use that saving One of the things we sort of need to get a little bit as you're leading us to think through here Is as people know everyone in the audience we have in fact Lord the amount of water that we use in total since about 1975 across the united states and it has not shown a blip in gdp or in productivity From that standpoint if we can ask the question That the idea of reclamation or the idea of efficiency Is not simply a question of using the last drop but actually You can do upfront savings to give you upfront benefits Then you get over the hurdle of what's the barrier to the upfront cost That is the major barrier to adaptation that we have Sure, absolutely. Okay. The reason I asked those questions If you were to think from the academy's perspective of if you want to study Of any kind related to this issue Who are the other players that have to be involved in this? I mean, it's not just the water studies or you the economy remember the economist The economist of water So water is a heavily subsidized entity in the united states and it's not managed by any one Thing It's in the west it's First in time first and right in the east it's Adjacency There are crop subsidies that affect demands There are repayment repayment Differences with respect to the federal infrastructure in the united states that dates back a hundred years This is The questions you were asking about the water systems and their reliability Are an economics question So so I want to bring it back to why the s2s timescale matters To a question like that because if you're asking a long-term Resilience and sustainability question we should ask whether or not the s2s timeframe offers an entry point For us What do we need to get ready to be act to act in that time frame, right? And and within the work we're doing with the world bank and elsewhere There's a feature called the sovereign liquidity risk Which is how do you do an investment that gets you the research you need On the near term that also helps you with long-term planning So I just want to bring us back to why this time frame matters to both the boundary as well as dealing with the extremes and responses to those that there's There's a criticality for the types of science and partnerships that you heard from the panel and from people earlier That needs to be in place to act during those time frames I should also add The reservoir for example, we have Is and within the year so we drain we fill within the single year which is completely different from the west So going back to writer's point the the ability to forecast and managing that resource is huge and and that will Also increase the trust of of decision makers operators on our ability to forecast and manage that resource if not They will just say hey, let's go and invest and we are trying to figure out We can we manage as we go through and we bring infrastructure only when we have to Because the last thing you want to do is you build something and that's an extended asset Maybe you're going to use once in 20 years So that's the the balance that you're reliability on one side and then the That you're willing going to have to pay for that reliability All right, we'll move on to the next question david and then I have dav and then david so it's the three davids Wait Yeah, i'm gonna On jonathan i'm going to sneak two in here because I can't imagine i'll get another chance for a question We'll let you do that, Dave. You have one question with too far The first one is about the s2s and who's missing from this discussion that might be useful if the academies thinks forward about a study and two groups that come to my mind are the u.s. Military and then people who think about farm commodities prices or insurance risk and i'm kind of curious if people have ideas of other federal agencies or people in the private sector who would want to Be involved in a future study or or research of this nature and the other question Is for roger because you made a comment about economic damage associated with droughts and I think a hundred billion dollars or something like that and I don't think that's Generally well understood or accepted and I wonder if you could amplify on that and and tell us what the Naysayers who disagree with that might might say to that assertion Yeah, then you can add So the answer to the first part of the question on defense and on financial services Definitely, I just didn't want to make it the comment just about economics because part of the issues so much of our values Are tied up in water that is beyond just that the Case I was pointing to in Ethiopia actually had a lot of repercussions back with our partners in defense Because in in staving off a humanitarian crisis, you may have staved off a conflict When it comes to that large number and my reason for putting it out there as a in it was um The way munich recalculated What in fact had been lost Not simply by the insured loss to the crops But the fact that barging started to lose money That recreational systems started losing when they put it all together and there has to be double counting in it Because we have no models that actually can get us A full sense other than in plan and which is only state level a full sense of the cumulative impact of a drought risk over time We don't actually anybody that makes gives you a number like that is wrong They we do it and when they took the insurers took the numbers for what was lost in the markets internationally They came up with a hundred billion dollars over that time But but flipping it around someone else made more more money on crop right? So when you do so when you do a net and that applies everywhere right if you build next to levy you've lost The where people would have built so when we amortize risk over a large region In in a certain input output sense we're adding up pluses and minuses all over the place the question is Why and to what extent is something locally significant that drives our understanding of risk? Because we could do pluses and minuses and we do this all the time for the especially the reinsurers That can end up to be wherever somebody loses somebody else benefits But what i'm interested in is whether or not people suffer as a result of that benefit and you can't amortize suffering That makes a I mean Okay, david over here one I have a question for Janine and others may want to Jump in on this so janine you were talking about the difficulty in finding partners and tools and I Suspected at least part of that challenge is the need for very local forecasting you know a lot of projects take place on specific sites in specific locations and I can think about the kind of resolution we seek for urban rainfall on trying to address Issues related to a combined sewer overflows and the like So I the need for very local forecasting and the role for empirical models Based on historical trends if we have data in particular locations versus physics based mechanistic models over large areas and You know you find both of those in the literature and I The empirical approach Doesn't get as much attention, but may be more appropriate for the kind of local forecasting needed and then and janine if you'd comment on that and and for janine or anyone side-by-side comparison of models for very localized forecasting it's there a need for that how much of that have been done Well keeping in mind the present accuracy or lack thereof of s2s forecasting And the fact that when you're looking out longer term, you're really not looking event-based You know you're looking at a probability of having something happen over a fairly large space because You know as water managers, we like to think in watersheds But the reality is the climate system operates in a much broader scale So from perspective of say state of california and looking at drought I would be delighted if they could get it down to northern california versus southern california at an s2s timescale You know, uh, I have no illusions about Getting a probabilistic event based forecast at six weeks out. Let's say, you know, it's not in the cards But generally the will it be wet in this winter We've gone through halfway of the winter now. What's the rest of the winter going to look like? So I can make decisions about managing groundwater storage putting into place late season water transfers Those kinds of things. So I am much less worried at an s2s scale about localized Just regional would be very good if they could get the the skill on that And you know statistical or empirical models versus numerical weather models There are folks in the room who know the long history of that better than I with statistical models eventually Me coming shall we say displaced by the numerical weather models, but When I look at NOAA from the perspective of an outsider Due to their budget limitations They really haven't kept up on some of the statistical modeling or statistical post processing that would support or compliment the numerical weather models And frankly some of the little research projects that we're going after Are more statistically focused for example or in conversations with a researcher at university of california urvine about What is essentially a statistical seasonal model for precip for california as an experimental product because it is a different approach than that used by the numerical weather modeling folks and I probably shouldn't say this in front of a bunch of atmospheric scientists, but given the the skill of the outlooks at this point Which as I said was non-existent. Well, okay, so you have a multi-model ensemble I could do as well with a multi-model ensemble of magic eight balls at this point given the skill at that longer lead time So I am very interested in considering any tool that offers a promise to skill Including the statistical ones And I should add I cannot emphasize enough in terms of Doing localized Information that's exactly what we try to do time of day water Florida has a very unique conditions, you know, we have both sides and convective rents and so on We try to work along when these here with university of Florida as well as Florida state in Climate scientists One of the things I can tell you a big challenge is that since we get almost more than 60 percent of our rain During the four months of summer When that rainy season start is huge. It can be anywhere from May 26 to 22 to July Once the rain starts the climate scientist tells me that the way the convective system works Even people we have no idea my wife can't tell you tomorrow at three o'clock It's going to be tender and and it works like that during summer That predictable once you are into that rainy season, but the challenge is how can I predict? Way before a few months when that summer start and we don't have so much time to To harvest that like I said, we have only three to four months to feel the entire reservoir That we deplete during spring So I can't emphasize more in terms of localized knowledge and how you can use And maybe if something you said triggered a thought with me a follow-up, so You know, one of the other areas we're exploring is So that's one thing to say take a weather modeling approach, which is an initial condition problem and trying to Torture that to the limits of predictability there The other aspect is to take the climate side and try and bring it down and get the two to meet in the middle Because we say as to as is the bridge between weather and climate Something that we're very interested in starting to explore in california Is can we do something to predict regime shifts or transition within the wet season? And that's why as part of our our task with NASA JPL we're asking them to give us experimental predictions of west coast ridging Because if we have a ridging condition that sets up for Weeks to months at a time that is the big trigger for droughts for us. We have a ridge there It's not going to be raining most likely and it may be easier or more skillful or more Meanable to other tools to be able to forecast those transition states And one of the tasks that we have for our uc Irvine researcher is we've actually Given her a set of years in which we had a significant flip in the climate system during the winter time and say Can you as an initial step diagnose what happened and then let's see if we can tease some predictability out of it Let me just follow up really quickly. Um, if any of the morning speakers our modelers want to follow up to Janine's comment about Can the models be better right or how are we first statistical versus our deterministic versus our probabilistic? Forecast should we are you going to address that? No, I'm not dressing it, but I I would like to Get some input from you. I mean, I'm really delighted to see how much of these interests in this it is in some ways that prediction community has struggled for a long time And the statistical model or you know, you will say parametric a model based on statistic correlations Hurricane forecast has told us that failed right so over so many years They tend to try to tell you this year going to be Bob normal and And you know As the end of the study recently show it's no better than toss the coin Over so many years because it still is irrelevant To this s2s so now to s2s components of this is that It is a multi-school phenomenon You have a background of insult or not and so what faces And the triggering time and things so in a way that I don't know if we know enough to Tell what is a predictably on that because all of these are still kind of rare events If you would have to say I like to do a daily weather forecast So in that sense we're accumulating that knowledge But my question to the panel is that how Much you see the future is that a dynamic model is coming online The s2s database Sort of a store in the european center now with multi model world model reforcast the last 20 years or so That can be used as a training site So right now many of the research community go into that world Try to see what kind of application we can go with But this requires a local statistical downscaling using that dynamic forecast model So do you see that as least the next few years experimental product can be tried out? I'm going to give you 30 second answer to that But first I just need to apologize. I'm going to have to run and catch a plane here in just a second But any follow-up I'd love to communicate with any and all of you in the future My answer to that goes back to part of the discussion this morning is I don't care You know, I won't tell you how to do the research I'll set the target you guys Give me the answer. That that's my perspective You You know your job far better than I know your job. You probably know my job better than I know my job So Yeah, I don't care I just need the the the forecast So yeah, and I have to say that I too am totally agnostic as to how you get the answer just as long as you get the right answer But you know, I think the the problem with the dynamical modeling right now Really harkens back to a lack of sufficient investment You know why for example is the european weather model so much better than the u.s. Weather model I think if this suddenly saw a whole lot more resources Going into it and I think one of the things I've learned from my conversations with dave over the last couple of years is From his perspective the critical need for more supercomputing resources for the research to support the research on s2s and You know, I didn't realize that was going to be a limiting issue But so I think it's as much a resource issue as anything and you know one of the Things about statistical models is in a warm and climate. We are pushing the boundaries and Stephanie could you hold up the brochure with the rocks on the cover? Yes. Thank you So that's a handout that I put out in the table. It's our wrap-up of our last water year The subtitle on that brochure is hot dry and on fire Which very much summarized california's Water year last year But in there there's a graphic that shows if you plot precip versus temperature by climate division in california All of the 21st century data are one standard deviation off of the observed historical means So, you know, clearly we are going forward in a condition where we're At the bounds of our observed data set. So, you know, that's a risk we have to think about when we look at statistical models I just wanted to add one quick thing. I mean if we're dealing with the idea of extremes across not just extremes from Subseasonal to seasonal but the number of extremes in that setting And we think in terms of the spread around the pdf around the initial conditions And whether or not that can actually produce extremes outside of the pdf it started with started with I don't think dynamically That's possible. I think as a result you're going to have to use both To understand how the distribution is shifting as you're moving through time So I think it's unavoidable to use not only the stochastic parameterization around the the The processing but also from the standpoint of saying how do these anomalies track during time? And I think you have to do that statistically Question just follow up this My point is that this is a new area that needs to join learning process Because right now a lot of the things that academic do is a sort of some exercise The end that we think it is to do but then at the same time there are things belong to what predictable Large-scale dynamic pattern is more predictable than precipitation itself So we would let them learn from your experience locally. What kind of things that would drive that joint the learning curve But but I think that's technically a really good question And and what the question is but what Janine was also offering is what space should that happen in? How is that organized supported and funded because otherwise we will come back to this discussion as to you know Is it limb or is the dynamic and and so I think where we make those decisions The help from the academy is creating that brokering to try to get at the organizational model janine pointed at We kind of have to move beyond the sort of um, I know it's it's seductive to say just tell me what you want And I'll go back and do the research. Well, that's about a 40 or 50 year old model about how science really occurs So we kind of have to get over But first we have to find some academic some folks in academia who are willing to work on this. That's the hard part Okay, I've got david johnson and annie. Do you have a response on the modeling or? Okay, why don't you come up to that'd be great Yeah, just just a quick comment on the statistical versus dynamical So from our experience at iri, I mean we've always Come from the standpoint whatever whatever works because we're trying to be trying to improve You know seasonal forecast for decision making and what we found in the end is well It's normally it's a mix of the two So it's it's hybrid approaches where we often call that post-processing So it's developing statistical techniques that will use some predictors I mean those predictors might be antecedent ones coming from a purely statistical model or they may be coming from the dynamical model So I think you know is resonating from many things has been said this afternoon about You know large-scale dynamical patterns and so forth But how can you make use of the information in the dynamical model through statistical methods? so the the development of statistical empirical methods is is extremely important from from You know our ongoing work and our experience and from the seasonal forecast Great, thank you your response So just hit the right button there speaker Yeah, I should add I should add that so what we are talking from from the climate side But there is a cascade of Uncertainties that will be going through by the time we as the weather utility You know managers make a decision. So for example At time of the water we use this largest scale forecast of enzo and so on How enzo translates to the probability of rainfall is another question It's not a slim dunk 70 80. Yeah, we'll there will be rain by how much that's a different story So once we get that rain and then we translate into another hydrologic Which has its own uncertainty and it too which we propagate Then we are using those to make a decision. So it's uh, sometimes, you know, when we We speak we tell them. Ah, we think it's a weak La Nina or a weak El Nino But at the end of the day what matters is the rain that comes associated with that large scale signal Okay, I've got david then jonathan and and please uh visitors along the wall too sitting in chairs you're welcome to ask questions We've got a microphone. So uh, just flag carly down. So we'll go to david Thank you and thank you panel and the one from this morning. I think you guys have really brought to bear for us The amount of data and information that's out there the need to do a better job of integrating that data and finding ways to build the tools necessary for the For the public and the private community to use it more effectively My question more goes to so i'm going to take a little bit Of a deviation from the current line of the discussion and go more to your bullet five up there great On how do we are the agencies ready for big data, etc? I have in my career I've had to spend a lot of time trying to interpret science For policy makers and for legislative purposes And an example and roger. Thank you for drawing to the reference to the drought in 2012 on the mississippi river You've never lived until you've had a line of Iowa soybean farmers show up at your door Demanding that you go give direction to the core of engineers to go blow up rocks in the mississippi river So they can get the barges upstream to get the soybeans and downstream to get it to market to the ships in norlands general peabody came in and he Asked specifically can congress give me some cover so I can do this because I don't feel I have enough Ability just to go out and blow rocks up in the mississippi river. The point of all that is is that policy and how our federal and state governments Utilize science Is often a two-edged sword they will use it as far as they think they're Um covered to use it, but they also are in demand so my question is and it's for roger for you as a Both a science agency and as a regulatory agency through the national marine fishery service Dave if he was here for an operations perspective and maybe janine You could reference it because your state does a lot in terms of water management Is it a function of we don't have the right science or maybe we don't have an integrated that science correctly Or are we lacking the ability for the agencies to use that science in a policy format? Say yes roger I want to take on the last Right because that's a critical thing when ravi and others were asking who else do you need and you know We like to rush to say economists. Well, look, we released a femur report That's what I thought right see that's what I was going to say not right But but what i'm getting at is not that there's something so wrong with that It's just even when we show the value of upfront action or actions still don't aren't commensurate with knowing that value We've shown that from disasters What you're asking Is a fundamental question in adaptive management From the standpoint of yes, we we spent the morning and we have a lot of reports And we have the s2s report telling us what the science is what we might know We're coming to the terms with the fact that yes, we need both You know the stochastic as well as deterministic aspects But when it comes to what legitimizes a piece of information for use with the public That's where the academy needs the partnerships of people who study the organization and legislative aspects of what allows us to get at How can uncertainty quantification not simply be accounted for but be used in decision making It can be accounted for but it'll never be certain. So how it can be used What we do not have is an agreed upon mechanism that says Until we get this right there is usable until we we figure out what the the limits to predictability are on this time frame That we can't act using the information. We do not have a mechanism that allows the agencies to use this because We have fixed rule curves. We have fixed and you know, there's better than anyone. We have fixed deliveries to me and the question is How do we get at working with janine with grand with others on what is an acceptable outcome If we've used all the best available information and how is this part of the best available knowledge? And I think that gives you the sort of decision making leeway That is not just an idealized Bayesian approach So from Tampa Bay water perspective And I should caution that Tampa Bay water is not an example of a utility at all Maybe a couple of utilities in the u.s. That kind of uh, you know tools and academy So we when you say big data, yeah If if that big data is for the whole u.s. No, but if that big data is covering our area, florida We can use those kind of information probabilistic. We have our own Internal cloud computing using a lot of machines running parallel the things I was showing you I've tried here Runs like 80 or so uh parallel computing. We run the whole thing a maybe Three hours if it wasn't a simple computer, it would be like a couple of years So the the agency make a conscious decision on investing. I'm talking a few million dollar Allowing us to do this kind of work So that we are demonstrating that We may be able to push some of our investments in infrastructure. We demonstrate already if I push two two years That's a 40 80 million dollars of investment So our my agency did that, but how many of those in the u.s. That's you know, that's the problem how we can Translate what we learn on this scale for other utilities who are not fortunate enough to have the Computing power as well as the skill You know professional like us One follow-up comment The folks up on the hill here they they deal in the issue of risk How much risk am I being exposed to are my constituents being exposed to by the decisions I make? And I think that's where I'm trying to to get a sense of Using the science to reduce risk and make better decisions But also making sure the agencies have the tools and policies To do the right science to make sure we're getting the right information for the public. Thank you worth pointing out that um Often a forecast or something along those lines is only one small piece of many things that go into a decision You could have statutory things coming into play that have nothing to do with the forecast regulatory considerations operational requirements for endangered fish species many many other things When I showed the research funnel or research funnel the weather management the water decision funnel relating to forecasting you know some of the Decisions that have the biggest economic value or the biggest cost better put Are ones that are essentially Really no risk decisions. They're not public health. You know, it's not a life safety decision to say that Well, gee, we will we we it looks like we will have a significantly dry year Therefore we will invest in spinning up a drought water bank to mitigate impacts You know that can be a very expensive decision to take but there's really no downside risks to it other than You know from public safety perspective. It's always better to over prepare For a disaster let's say than it is to under prepare So I am less worried about that aspect. I suppose then perhaps you might be Great. Thank you Jonathan thanks for your patience Sure I'm intrigued by a lot of what I've seen today It's really been very interesting. So thank you for your for your presentations And roger, um, I wonder if you could enlighten me. I hope this isn't too naive a question I'm certain there's a level of naivete in it But what's the state of dynamical understanding of the flash drought event of 2017 and through so What's the state of a dynamical understanding for the variability of the onset of the rainy season in the Tampa Bay area? So aside from models, how do we understand how these things even work? I think the fundamental question What what you're asking on 2017? We have a paper coming out on what we understand about what happened But when it comes to that, um, what drove the fast drop-off and soil moisture We saw the precipitation shut off But the classic sort of setting up of a, um, you know, dynamical high over the region wasn't identified early It wasn't there and so as a result the extent to which this becomes a driver from internal atmospheric variability Is really the question yet my answer to your question is We're not sure about the process based understanding as yet. We've been able to identify post hoc some of the drivers But I think you're on point and it's one of the reasons why I raised it after showing something that we did do A job on was to be able to say that, you know, without that sort of process based understanding We're going to constantly need to be able to do both the stochastic and deterministic And then be able to deal with the piece of uncertainty that neither captures Okay, so my point is we don't understand it fully So and that's thank you. That's frank answer and from my perspective as an academic research scientist And this goes to janine's comment a couple of times. I'm very interested in trying to understand how those things work So that's a place where we could work together If there's any possible way to make that happen and I don't know if there are ways to do that. So thanks Can I just I think you're asking as well the question Ravi is coming back to which is I Appreciate your question We've tried to create groups like the Reese's and others to make help this work the regional integrated sciences assessment But how we get at fundamental questions like that in a partnership With the academy with the private sector with the the fed is not something we've worked out organizationally as well So rather than it not be just a one-off either Okay Yeah, but from tampe water, I think I'm the wrong person to to answer that I know the challenge and in fact, there is a paper coming in nature one of our partners from climate scientists We know that when that Season starts we you know historically we can define we can define its uncertainty But the challenge is predicting it when that transition from Dry season we call it from october to may Transition so that really I cannot tell you in terms of the process driving that but it is important for us Okay over here. Can you introduce yourself? Uh My name is Andy Miller. I am with the american meteorological society For a long time today, we talked about how Forecasting of temperature and precipitation affects humans and how we can manage that I was wondering about the opposite side because it struck me that it would be basically impossible To forecast the river flow in sonoma county unless we knew that they were going to release A ton of water over a few days So how much do we need to invest in forecasting human behavior and understanding that because it seems to be a really critical part of the flow in any any one of these spaces forecasting human behavior The analogs with forecasting the physical system and the word forecasting in human behavior do not mean the same thing And do we need to know how the decisions? That are being made influence what's in the stream and and you know how you do releases such as You know being able to store for fire or on others to me. Those are decision making Questions I think you have to take into account the types of things they wrap and they do which is What's the withdrawn water what's being put back and so on but I'd rather not go out on the forecasting human behavior components So water agencies invest very large amounts of water as Tampa Bay is done and have we is have done in california on all kinds of water operations models that work at different timescales so if we Given what we think is coming down the pike we can run that through our system that simulates You know the legal requirements for industry flows for water rights Expected demands all those kind of things. That's our bread and butter job and we do that all the time Okay, another question over here Great, and if you could please introduce yourself, that'd be great Hi, I'm Ari Dursman with uh yukar And I I think it's self-evident, but it's worth mentioning And I just wanted to respond to Janine's point about how difficult it is to access NOAA vis-a-vis S2S forecasting because it seems to be all across the agency and and you mentioned how it would be helpful to access it in a manner that people might access h5th and I think it's worth talking a little bit about the fact that Hurricanes are individual phenomenons and that there are researchers who address just hurricanes whereas S2S is a scale And that you're looking at a number of different phenomena with a number of different drivers of predictability across that whole scale and that Saying that it would be valuable to access an agency for that information is A difficult thing for the agency to respond to and so what I think I'd really encourage the board Both boards to to do is to Isolate some phenomena Specifically and and I thought shui's point on qpf Is is really it but to to isolate the phenomenon and then build An improvement program around the phenomenon at the S2S scale and that might be Valuable and I'm just curious about you know yours and Roger if you'd respond to that from the NOAA perspective it would be helpful So what I was speaking to is not so much at the Down in the weeds research level but at the organizational level a little bit higher because we're not going to tell NOAA How to do its job? But I know there are many pieces of NOAA in the room here from math to cpc to the labs Etc that all have a piece of some kind of S2S And the question is how do we get those organized internally within NOAA so that then they can come together and Let's say design an h5p type Experiment whatever that is I'm focusing at that organizational level not at the the science level And I understood that. Thank you. Great. Okay. I think the last question is going to go to margaret Unless somebody has a dying need to ask another question. Yeah, I just can't Can't not comment on the behavior issue So it is a nascent area, but there are groups working specifically on Dynamical models that bring human behavior decision making it in line with some climate change models Is it a very early stage? One of the issues is there are dozens of theories on Behavior change so things like theory of plan behavior Certainly going beyond the rational actor etc etc And you know, I have maintained and brought up several times that these aspects I think that the National academies could play a really important role in trying to bring some of those groups together It's also true with respect. I believe they refer to it as or you refer to it. Roger as adaptive management, but Really, this is there's a lot of work really good work going on around adaptive governance structures And in what conditions do we see changes made that get beyond some of the impediments you made and I think us beginning to think about Bringing biophysical scientists like us together with those groups really makes a lot of sound Can I can I can make a quick response to that because I do I do want to mention that when I you know Words like adaptive governance or anticipatory governance are to me redundant because good governance is both of those But the reason why i'm saying this is Is the reason why i'm saying this on the operations research aspect We could look at what janine said about how um, you know, the shape of Of flow down the river filters through the system But I think even more Than the rational actor standpoint We need folks who understand the values behind why we choose some outcomes over others And that's not necessarily something that I would just say the answer is to add a human model to a physical model We've failed in doing that an integrated assessment And so I would really we were to add another component. There is how do people perceive risk? How do people act on it? Yeah, we know that about decision making But how does that then matter to the usability of the s2s forecast? And I think we need to target it to what's that people like david and they were into data before it was big All right. Well, I think roger got the last word On janine, you know that never happened So with that I'd like to thank the panel again for their time and energy put into this and I thought it was a really great discussion