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'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.  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.  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.  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