NCCC 2022 — Emerging Technologies for Coastal Change

good nice um and Ryan's going to  talk to us about lidar scanners   for continuous based continuous  Beach morphology observations um so can I just get a confirmation that you  can hear me over there we can hear you yes okay   fantastic so thank you in front of you what you I  think we're looking at though sometimes video does   not work so well and PowerPoint was a sort of time  lapse of the field site where we did a deployment   earlier this year uh on the Outer Banks to test  out some new lidar scanning technology during   storm impact um and I don't think the video  is playing but uh let's see if I can at least   Advance here we are it probably doesn't look as  good um over Zoom I'm sure it's quite jumpy but   rest assured uh what we're seeing here is a wave  run up during high tide which is impacting a a   dune or what I like to call the air quotes Dune  Dyke system um in front of this house here so   I'll just talk today about a deployment we did  on this particular house and in this particular   region as sort of a miniature small scale test of  some pretty cool new 3D lidar scanning technology   um so the reason we chose this particular field  site which is denoted here in the red circle   along the Outer Banks but because this stretch  of coast is eroding quite rapidly uh in in this   circle area the field site was located in Rodanthe  North Carolina the average latest average yearly   annual erosion rate is about five and a half feet  per year but over the past couple decades that has   been accelerating from about 20 years ago being  about two feet per year uh as most of you probably   know OverWatch is a common thing that occurs  along this stretch of a highway particularly   the s-curves on nc-12 and so recently The Jug  Handle Bridge as it's called right has been   open to bypass this over washing region so that  may have more significant impacts down the beach   so there was a storm uh that was forecast to  approach and pass near the Outer Banks of North   Carolina as indicated by this forecast screenshot  showing a forecast predicted simulated radar   um and so it was going to be a pretty large low  pressure system so the near shore extreme event   reconnaissance or near Association decided to  deploy a field mission in The Weather Channel   termed this winter storm Kenan our report for this  and our preliminary data report and uploaded data   it can be found on the natural hazards engineering  research infrastructure online portal this is a   picture a few days prior to storm impact here we  can see a pretty steep beach and a scarp here as   well as a dune or Dyke scarp which existed  from a prior storm about two months before   this study this is again the region showing here  some forecast water levels so there was about two   days predicted of higher water levels not quite  necessarily up to what was predicted to be the   elevation up here in that area so it was going to  be an interesting storm to investigate because it   was not expected to be major overwatch but perhaps  there would still be lots of coastal erosion   uh we also looked at a lot of the data from a  lot of sensors around the region to get sort of   a larger macro scale view of just how the entire  region was being impacted so these are some water   level observations I just want to put into context  here sort of what this region of the coast right   had been experiencing throughout January and  February of this year so these are plots of   water levels from three different stations along  the coast one is Oceanside and two are more sound   side so to speak um and uh here are three events  uh where there was a pretty significant say   offshore storm uh impacts in the month of January  and then you have highlighted winter storm Keenan   so we see over the month of January particularly  on the South Side locations the water levels were   almost always higher than the forecasted water  levels uh being blue measured being green if   we take a look also at some of the wave data at  this buoy and the wind and meteorological data   from this station near Oregon Inlet where our  field site is sort of right between those two   stations we see that these three storm events  consisted of about 30 to 40 mile per hour gusts   uh knots of gusts and about 25 to 35 not sustained  wind speed um and it's notable that these two in   particular occurred right before winter storm  Keenan so that this area was already really   vulnerable because there was a large waves  almost 20 feet in significant wave height   um just about a week or two prior to winter  storm Keenan uh impacting this area I also   just want to point out the wind direction here  so here during the large events in the middle   of January with the largest wave heights we see  the wind direction coming around that time out   of the East which would be onshore um whereas  during winter storm Keenan for the most part   the uh wind speeds were from the north so sort of  from north to south in the Long Shore Direction   so here is our deployment schematic it was quite  an extensive deployment I'm only going to focus   today mostly on lidar data but we installed two  lidar scanners on this house here some pressure   gauges on the pier to measure waves and water  levels we also had cameras one two and three   monitoring um in real time the impacts of the  area and the water level sensor here installed   on this house here's a picture of the wave gauge  installed on the uh up here and then this is one   of the two lidar scanners that was mounted on  the deck on the house South adjacent to the pier um this is just the wave and water level of data  that was recorded by the rbr uh solo D wave gauges   um and so in the future we will process this  data to sort of uh time correlate the impacts   in culsa erosion uh from the Morpho Dynamics  and tie that into the hydrodynamics however   for the sake of time in this presentation I  will not cover any of those details further   we captured the morphod Dynamics of uh the beach  in this particular area using two compact low-cost   3D lidar scanners this is the Blick felt Cube Cube  one model they're about five thousand dollars each   I just mentioned that because that's relatively  inexpensive compared to some of the lidar scanners   in the hundreds of thousands of dollar range  uh they're also really cool because they can   log be logged autonomously and so we just run  python code or some other other API scripts   uh to log the data and control the sensors  continuously so we captured high resolution   scans every four minutes for about 3.5 days which  consisted of before during and after the storm uh   we would have had two weeks of data but I made a  silly mistake and bought cheap USB drives that I   thought were two terabytes and turned out they  were about eight gigabytes uh so we ran out of   data after about three days so this is just a  picture of the setup that we had on the thing   of course there was a lid over this um and so I'm  going to focus on analysis just on L1 today which   was on this corner of of the house but this just  gives you a quick sort of overview or picture of   what the data kind of looked like before I jump  into Data analysis um and so here is the pool   wall looking out over the deck and then in the  lidar scans here if you look closely perhaps you   can sort of see the outline of this pool wall  there and so this is what we're capturing with   those two lidar scanners and so we get this high  resolution field of view every four minutes we   get a scan like this but by the way it only takes  about uh 10 seconds to complete this entire scan   uh and then this is what the beach looked like  after the storm so uh we need to geo-rectify the   data uh so in order to do that we installed or  laid out six Ground Control Point targets which   look like these little white cylinders here  uh and across and throughout the you know the   view of the lidar scanners and then we survey  those with rdk GPS and then we can georectify   the uh XYZ Point cloud data into uh stapling  coordinate systems and then we need to validate   um whether or not our georectification was  actually done appropriately um and so we also took   after the storm seven post storm beach profile  transects we ran out of time to do those pre-storm   um but we still have the Prestone lidar data and  so then what I'm going to show you today is a sort   of just an overall comparison of how that survey  data and georectified lidar data compare with each   other by the way it was very cold as you can see  by these images here during this rapid response   um so we carry out this sort of sequence of  filtering so we filter the lidar point files   we have to remove the waves water non-land objects  like people fence posts and birds and so while you   did see this very nice large triangular field of  view from the previous lives of the lidar point   clouds we end up removing a lot of that data  to just give us the actual dry beach we also   removed the scar from beach profile surveys as it  was not so reliable and then we removed the Dune   as I call it the air quote "Dune" from the lidar  point clouds as well as anything landward of the   dune then we just interpolate the lidar data and  the beach profile survey data onto the same grid   with about a half meter grid spacing dxy and then  we use inverse distance squared method to give us   an interpolated grid so this just shows you sort  of a 3D view of the beach profile transects that   we measured um and then the L1 lidar field of view  with the magenta dots are the filtered point cloud   data and then here is our lidar surface the  interpolated survey surface is not pictured   here but would overlap across this entire span so  then we're going to look at transect four here to   see how well the lidar data compared with the  surveyed uh grid interpolated grid so on the   top what you're looking at here is on the x-axis  you have cross short coordinate and on the y-axis   is the longshot coordinate so we're looking from  above and here is the region where the lidar and   survey data overlap with each other and this is  transect 4 again just to orient you so offshore   is over here to the right so this is just a  difference map of the overlapping survey in   lidar interpolated grid and across this entire  region we have an average mean square error of   about seven centimeters and a mean bias of about  negative five so that's just the difference   between the lidar and the survey now if we look  just at transect 4 which is plotted here on the   bottom now in profile view and we evaluate the  statistics just in the region or the span where   we have lidar data overlapping with survey data  we have an improved average root mean an improved   root mean square error of about four centimeters  and a mean bias of around three and a half   negative centimeters so I think that's relatively  good given that the overall error between the two   different survey methods uh vertical error  is in the range of a couple of centimeters   and I just want to wrap it up and end here uh  we've been now working on analyzing the filter   data but prior to that it did make an animation of  the unfiltered uh gridded surfaces so here you do   see a bunch of noise from the waves coming in but  there was the erosion of the beach uh uh profile   during storm impact and then during the next high  tide we see some accretion here and then the next   higher high tide we see additional erosion and  landward transgression of the beach profile those   were some people walking through the stand that  we had not filtered out yet and then if we take   trans X4 and look at the time stack what we can  start to see is with this kind of data sets and   with this technology uh of course this is just  three and a half days you can imagine here we   see one two three four five six high tides coming  into our field of view we also see this land where   rapid landward transgression and erosion of the  beach profile and the start and then during the   next higher high tide again we see little bit  more erosion and this grid each grid is half a   meter so there was only one meter remaining of uh  this dune before the pool will at that particular   home was going to be impacted by waves so this is  uh uh a three-dimensional view of that same time   set just to give you a sense of what we're able  to resolve and if you look here after these uh   second higher high tide where the additional  erosion occurred you can see in subsequent   high tides slight accretion on the beach just  uh at the landward edge of the beach of course   this was sort of the beginning of what became a  accretion over time uh and if we look at images   that I took in the end of February uh versus the  end of August both photos were taken at low tide   um here we see people way way out in the ocean so  it's quite shallow that far out versus here which   was a couple of weeks after the first house  of what became three houses that collapsed   um and so there was quite a significant  difference it doesn't look like this today however   um and so I would love to move in in the  future try to apply this technology to more   continuously monitor Beach morphordynamics  over more than just three days because as we   all know the beach is a very dynamic system  so thank you and I will take any questions [Applause]   Ryan you did an excellent job on your time  and you've got seven minutes for questions   so um we have a mic set up back here but I'm going   to bring this to Barbara Doll so that she  can answer her question ask her question Ryan I'm wondering if it  would be good to just maybe   have like a summer snapshot and then  a winter snapshot on a non-storm day   and kind of look at this over time because  it seems like you're going to generate a   lot massive amount of data so like if you have  continuous over time I'm just kind of thinking   of what the use application of this would  would be and how to do it like practically that's a great question and I think uh part of the  initial goal of this was really just to prove the   concept uh and approve the sort of methodology  and analysis methods uh but but moving forward   um the fantastic thing about this  particular scanner model is that it's   very very configurable uh and so depending on  what you're interested in if you're interested   in just a single transect you can program  it just to conduct a scan along a single   transect uh you can also program it to do  a scan once a day once a week once per hour   um or you can even remotely talk to it and say  it set it to do a scan every hour or once a day   at low tide but if this storm is coming then  you can reprogram it to to start scanning once   per hour once every 30 minutes and so there's  a ton of flexibility because sure if you're   scanning every four minutes for an entire year  it does become quite an extensive data set but   um yeah maybe hopefully kind kind of answer your  question so I I think there is tons of room for   like practical applications it just it comes  down to having partners who have power supply she said that did answer her question we have  plenty of time for question two we have to wait   until um and we get to like the 20 minute mark  before our next speaker goes yeah right uh this   is Ling I'm from Coastal Studies Institute and  we are also using lidar and we have grand prix   lidar from regal is called basic 400i so first  um for those lighter sensors you use what's   the range limit I say it's a short profile so  what's the range limit for your lidar sensor? Ah that's always a loaded question right uh so the  manufacturer states a range of uh 250 meters but   that's rarely the case particularly when you're  outdoors in sand and water uh we've uh a student   of mine has also that this past summer at the FRF  tested the entire summer different configurations   um on one of their lidar towers and I I think  during lower low tides there was a range out to   about 50 to 75 meters um it's it's a little bit  less dense at that point but uh the actual point   cloud on this particular slide um uh would kind  of come back out and then sort of re connect over   here um but it just doesn't get any return off  of the non-white water that far away so here it's   around 25 to 30 meters in the crossword but that's  what the reason you're only seeing that is because   the water at this particular location was  only 25 meters away from this uh person's   house uh but I think up to maybe 75  meters is what we've seen at the FRF   okay yeah thank you and you can even  go uh to the website of blickfeld   um and there are a couple other graphics and  figures on a case study that I did with them   um which shows some graphics uh from the FRF  which will give you maybe a better visual sense   yes uh thank you but uh I  have a lot of questions okay   because let's see if there's another  are there any other questions okay yeah so um since you was so  curious about the accuracy of those   um uh lidar sensors I'm I'm also  interested in like we can bring our   Regal visit 400 I understand the same area  and let's see how does it work what's the   uh what what does the result look like because  we also have a very interesting site in your   density now in the it's in most part of the  United States there is a very uh frequently   overwatched area we bring our scanner to  scan those overwatch area again again so I I again I think so over this summer at the  FRF um maybe you're aware that they have uh I   don't remember the model of Regal lidar scanner  that does a full scan across the entire FRF they   have multiple and so part of that study was  also to look at how accurate this model was in   georectification versus the previous model we're  still improving the georectification methods right   now we're just using a sort of camera based  uh rectification approach uh and skipping the   intrinsics so we just calibrate for intrinsics but  I'm sure there are better approaches to improve   the accuracy as far as you know Point Cloud  registration and all this other kind of stuff   um but certainly more comparison data sets between   this model and other models in all sorts of  different environments would be a good idea all right thank you so much yes hi I am Logan   Howard I am a senior undergraduate at  the University of Nebraska in Lincoln   um but this was a research project  I conducted as a part of my Noah   Hollings internship at the weather forecasting  office in Newport Morehead City North Carolina   so first to start with a little bit of background  um so for weather models currently they are   ingesting sea surface temperature grids that are  a very coarse resolution and that is of particular   importance here in North Carolina because um just  off our Coast we have the Confluence of the cold   Labrador current and the warm Gulf Stream current  that creates a very sharp sea surface temperature   gradient that the models are not capturing  effectively this gradient is particularly enhanced   in the cold season and here I have an example from  February where we can see that just off our Coast   in the Marine zones here that there is a nearly 30  degree Fahrenheit temperature difference across a   distance of just 60 miles and this is pretty  significant and another thing to note is that   this is of particular uniqueness to the Newport  region which I have here in the the black squares   um so right in our region is where we have  the confluence of these two ocean currents   so the objectives of this um this this project  was the overall goal was to help improve our   marine wind forecasting and of course that will  help improve our marine hazards like small craft   advisories and Gale warnings as well as eventually  improving our wave and rip current forecasts   and the way this will be done is we built  a tool that will incorporate winds and   sea surface temperature observations to help  correct and make better Marine wind forecasts   what this means scientifically is that we are  going to determine how well winds are mixing in   the Marine boundary layer and just for context the  boundary layer is the lowest approximately one to   two kilometers in the atmosphere and this is the  region where the winds are under the influence   of the friction by the surface so in it we have  mixing nuts occurring throughout the day and we   can approximate this based on the lapse rate which  is the rate of change of temperature in this layer   by using the sea surface temperature and the  temperature at the top of the boundary layer so we will catalog all these relationships  and any additional variability by other things   and put that into a climatological  catalog that is put into this tool   so quickly going back to the Sea surface  temperature maps here on the left I have an   example of a sea surface temperature grid that is  being input into a weather model and on the right   I have observed sea surface temperature which is  a blend of buoy observations and remote sensing   satellite observations so the first and most  important thing that you can see is that the   gradient is much larger and spans a much uh larger  space than uh what was observed another thing to   note is that the sounds are actually 10 degrees  warmer in the model than it was in observations   and that's going to have a big impact uh one last  thing to know is that the um the bay is Onslow Bay   um these are really shallow continental shelf  Waters and thus these waters are a little more   influenced by seasonal variability and thus you  can see in the model they're just a touch warmer   than what was observed and one last thing to  know is that um this one is one example from   one weather model but all high-res American  models are using a sea surface temperature   grid that is of this quality or similar so this  problem is not unique to just one weather model so the observations that were collected um we  took sea surface temperature and as well as   surface wind observations from two buoys diamond  shoals which is located just off the coast of Cape   Hatteras and also Onslow Bay to the South and this  was obtained from the national data buoy Center   and um characteristics from Aloft we're  taking from a radius on soundings those   are occurring twice a day at the Newport  Weather Service office which is located in   blue and this data is stored in an archive  on the Iowa environmental Mesonet website   data was collected over a span of four years  and if any sounding or buoy data was missing   the entire time slot was scrapped just to make  this easier to work with and while we did have   to remove quite a bit of data we um still ended  up with between 1500 and 2000 data points and   just to clarify this it does span all four seasons  and since soundings are taken twice a day we have   morning and evening observations so that's twice  a day so the first thing we wanted to make sure is   um that there is a relationship between the  wind speeds at the surface and the wind speeds   Aloft at the top of the boundary layer we chose  two different um layers to represent different   heights of the boundary layer and what we found  is that across the board we do have a very nice   even normal distribution which is good that means  there's a pattern to this and that this can be   easily predicted but you will know that there is a  very large tail that extends out to the right here   on the on the x-axis and you'll see that this  ends at 2.5 and that was a deliberate choice   um because these wind speed ratios  that are very high so 2.5 or greater   um these wind speed ratios could be attributed to  small scale things that were not representative of   the overall flow of the atmosphere these could  be a lot of different things these could be   fronts this could be related to storms tropical  cyclones there are a lot of different things but   the point is that these are weather events that  another tool is not designed to capture so these   um these instances were not used in this tool I  have a couple examples here just to illustrate   so here we can see that we have a stationary  front bisecting the region and what's important   here is that the sounding location on the land  is on one side of the front while the buoys are   on the other side of the front so as you can  imagine there are major differences here in   the wind speed and direction on either side of  this front and so that's going to affect that's   going to affect how the mixing is occurring  in both of these regions and we uh the tool   is just designed to look at the whole region the  region as a whole with similar characteristics   here I have an example of a nor'easter so you can  see there's a lot of boundaries a lot of fronts we   have a large cyclone occurring and this is just  not um not what the tool is designed to look at   so now that we have all of these wind speed ratios  we want to tie that back now to temperatures   specifically the lapse rate so  I'll explain this diagram so on the   um on the y-axis we have lapse rate or the the  rate of change of temperature in the atmosphere   on the top we have instances where we have  an inversion or where temperatures aloft are   warmer than at the surface and then as we go down  we are having the atmosphere increasingly cooling   or instead another way we have an increasing  lapse rate on the x-axis we have greater wind   speed ratios so what you can think of that as  is um better mixing in the boundary layer and   what you can see here with these diagrams is that  with an increasing lapse rate we are seeing that   mixing is getting better or more efficient in  the boundary layer and we can see that here for   both of these these different heights that we  chose and now here's just the same thing for   the other buoy and we have the same relationship  here where we are seeing that mixing within the   marine boundary layer is uh becoming more  efficient as our lapse rate is increasing   so with that we wanted to see if there was  any other variables that would otherwise   interrupt this relationship we looked at a couple  different things here I have season time of day so   separating all the morning and evening soundings  and then we also wanted to repeat this lapse rate   um calculation with the air surface temperature  so before we were looking at the water sea surface   temperature and this is the air temperature just  above the water and what we found is that all of   these um all these variables do not greatly  affect the relationship that you saw before   they were all statistically insignificant and  also the air temperature does not change as   radically as the sea surface temperature so  these relationships were not as meaningful   but one thing that we did find  was important was wind direction   so we divided a wind direction into two different  bins uh representing northerly winds or cold air   coming into the region and southerly winds or warm  air coming into the region and what you can see   here on these graphs is that when we have cold air  coming to our region we have a greater spread of   possible wind speed ratios or what we can think  of this is that when we have cold air we have   an increasing chance that mixing efficiency will  be increased the way that this was incorporated   into the database was that the maximum and  minimum possible mixing efficiencies are   going to be determined by wind direction rather  than just having one set for the entire database   so now that we have all of this information I  want to take a minute to explain just how this   tool is going to work and what this looks like  so the tool ingests four variables we have sea   surface temperature observations we have the  temperature at the top of the boundary layer   and we also have the wind speed and direction  at the top of the boundary layer it's just a   simple subtraction to get our elapse rate and  then with that and the wind characteristics   um the database will take this and look and figure  out what wind speed ratios and what range goes   with these variables and so actually you don't  just get one you actually get five possible wind   speed ratios two of which represent our lowest  end possibilities which is quantified through   our 10th and 25th percentile our most likely  situation which is also our median and then   our highest and reasonable uh situation which is  quantified through our 75th and 90th percentiles   it's just multiplication to get our wind speed we  reincorporate our surface wind direction to get a   final uh wind grid a wind forecast grid so with  these grids the forecaster is able to pick and   combine and blend these grids as they see fit and  once you have that then you have your final wind   forecast grid through a previously established  tool called The Marine Wind Gusts tool you are   also able to make a wind gust grid based on the  Wind grid that you just made with my tool as well   so we'll go through that one more time actually  using a couple numbers here's an example from   January 16th just a random day and then on here  I have the characteristics that are being input   into this tool so once again we have our sea  surface temperature we have our temperature   and wind at the top of the boundary layer we  calculate the lapse rate and then with that and   the wind we the tool goes into this database  and picks a range of wind speed ratios that   based on these observations should approximate  the amount of mixing that is occurring that day   so again we have our two low end scenarios  our our most likely scenario and then our two   high-end scenarios so you'll see between our 10th  50th and 90th percentiles we have about 10 to 15   knots of variability on either end and while that  is a lot we wanted to do this on purpose because   as you saw in those histograms it's a pretty  decent amount of spread on both ends and so   um instead of just giving you a most likely  scenario we wanted to also give you a high   end and low end scenario so that um based  on the conditions that you're seeing you   can pick and blend and choose and um based  on the current scenario you can make a more   educated guess on what you think uh the amount  of mixing is going to occur as I said before   the surface uh wind speed direction is uh just  reincorporated and then you have your final grids   so as I was saying before the forecaster can  pick blend and combine these as they see fit   um to get your final wind grid though in this day  actually going with the most likely scenario would   would have been a pretty good scenario as uh what  was observed at diamond shoals that day was a wind   speed of 21 knots and then using the Marine Wind  Gusts tool you get your wind gust grid and uh so   what that would generate is a 25 knot forecast  and the actual observed value was 28 knots   so what this looks like in the computers  for the forecasters to use it is labeled   the empirical Marine wind tool though  this is subject to change and when   you run this it produces the five wind  grids that we have been talking about   so um here's an example of a sea surface  temperature grid that you can input on the   left we have again a model and then on the right  we have the satellite derived observations and   as you can see the um the big change here is in  the northern waters where we had some upwelling   and you can see that the temperatures are about 10  degrees cooler than the model thinks is occurring um so what this looks like is you just hit  populate and then and then it will uh produce   these grids currently it just runs but um as we  make changes we want to uh insert a uh a user   interface where um one you can pick which model  that you are importing as there is many different   kinds you can select the height of your boundary  layer as we study the two different options   and then we want to also be able to include  preset edit areas so for example in this choice   in this day you might only want to select the  northern waters or you might want to only select   the sounds where the model is doing okay and  then where the model needs a little bit of help   foreign so as I said before these five grids  will appear based on the tool that we have   just discussed and just to compare here I  have the 10th and 90th percentile values and   um you can really see here where the tool  is doing its magic you can see here in the   northern Waters where we have slightly lower  temperatures you can see here where the tool is   adjusting and producing lower wind speeds that  the model would not be able to do on its own   so just to restate our results we found that there  is a normal distribution in the ratio of the wind   speeds at the surface and the top of the boundary  layer and that there is a relationship with these   ratios to the lapse rate in the boundary layer and  thus what this means is that we can predict mixing   efficiency based on sea surface temperature  observations um all these relationships were   stored in a database which was then inputted into  a tool for forecasters to use in real time to   create probabilistic Marine grids which they can  use to forecast I would like to thank the entire   Moorhead City staff for our journey and the office  a particular thank you to my mentors Carl Barnes   and Ryan Ellis I want to thank Donnie for helping  to code up the tool and also David Glenn for the   use of the Marine Wind Gusts tool I'd like to  thank the Hollings undergraduate scholarship   for providing the funding and resources  for this um for This research and I would   also like to thank Seagrant North Carolina  for hosting This research conference I have   all my references here and if we have time I'm  available to take a couple questions [Applause] yes we do have time for questions about a little  over three minutes great great job with your   time great job with the energy um for coming from  Nebraska to talk to us about the gulf um questions have you tried comparing the  your results with this one the   one that is used in you know in  terms you know of wind direction um we have not compared these results to  the swan model although this is something   we can do once we start um once we  start operationally use with this   tool once we get a little more into the  cold season and this tool it's a little   more effective and forecasters can provide  their input and see how the tool is doing I've been sitting all day so this is great walking  around thanks great job I was wondering with uh   some of the work um Inland water bodies with water  temperature being a signature for groundwater but   I was wondering if there's any influence you could  see in the estuaries related to the amount of flow   coming in like moderating temperatures and that  sort of thing um so uh they um currently the   operational use of this tool is just a little bit  limited uh since we developed this over the summer   but um once we have uh more use with this uh with  this when this upcoming winter we'll be able to   um we'll be able to see that and have  a little more answers for you thank you uh I just wanted uh to ask the question  Logan because we know the answer but how   much Marine forecasting experience did you  have when you came into this internship   absolutely none so he did a lot of work over 10  weeks we're very proud of what he was able to do   yeah absolutely a round of applause any  more questions one okay what's next for you um so currently I am uh exploring a couple  grad schools but I would eventually like to   apply and join the Weather Service really  good answer thank you thank you again [Applause]   so we have another friend from the National  Weather Service here to talk to us uh   Matthew Scalora oh nice um and he's going to  talk to us not about New Wave information but   New Wave information um included in the National  Weather Service Coastal Waters forecast thank you let's do this if we can't find it we could  switch up the order of the speakers this is all I have access to is it possible  in one of the jobs I remember seeing those   three and mine was right next to it  they updated there's a date I put mine   in on Sunday for the first time and I think  they had done theirs on Friday and Saturday I'll take responsibility for this find it I can't find it let's just switch if  you're okay with switching speakers well let   Chrissy go and then we'll find yours unless  you have it on a drive no I don't have it   um I could we'll find it I could send it to you  from my account wow okay yep let's just let's   just do a little switch you know um Chrissy it's  here there's Chrissy um from the U.S Geological   Service another Federal agency that we love  um is Chrissy Hopkins uh her presentation   is well you can see the presentation title right  there thank you Chrissy for being adaptable yeah yeah sticking it out and um excited to talk  to you a little bit about uh green storm water   infrastructure and some monitoring that we've  been doing especially in Maryland but I think it   um is a good example of when we Implement  these practices across a whole watershed   um what are the impacts that we see so we  don't have a lot of places where we have   a really dense installation of storm water  practices like rain gardens and infiltration   trenches and these types of newer stormwater  practices um so I think this has some useful   information that people can use along uh in  coastal areas as well uh so here's a webcam   uh video here showing a stream in Charlotte  uh combined with USGS stage data so we can see   um you know Urban streams are really flashy they  rise and fall very quickly um and when we combine   things like webcam data with uh you know discrete  measurements of something like stage we can more   easily visualize kind of the impact in the scale  at which um flooding happens in our urban areas uh so what my research has to focused on is  looking at comparing a few large centralized   stormwater practices so things like large  Retention Ponds to what happens when you install   a a bunch of smaller practices distributed further  up in the Watershed to a range of different   uh stream functions so I'm going to call these  large centralized storm water practices versus   many small distributed storm water management  practices so we've been looking at changes over   time as an area goes from agricultural land use to  Suburban neighborhoods so here's just an example   of the land cover changes that we've seen in the  study areas that we're working in Maryland you can   see the agricultural fields here and some of the  forested areas that were converted into roadways   and houses for people's neighborhoods and then  a school in the northern part of the image here um so this is in Clarksburg Maryland  which is a suburb of Washington DC we   have been monitoring this area since  2004 so it's a pretty long term study   that we've been doing over a decade  and comparing control watersheds to   um three watersheds that went under went  suburban development so you can just see   kind of where those watersheds are and show the  watershed boundaries there and then the diamonds   indicate where USGA has been monitoring uh stream  flow and then the county has been monitoring a   range of different functions for a water quality  and the benthic community within these watersheds   so I'm just going to zoom in a little bit so you  can see what these watersheds look like these are   the control sites we have a forested control  site which is the county park these are small   watersheds so you can drive from one end to the  other in about 15 minutes um you can see like a   forested site here that only has about two percent  uh impervious cover so things like roads and uh   parking lots very limited here because it's a  park but it also was formerly agricultural land   so it's kind of secondary growth for us it's hard  to find anywhere in this part of the country that   hasn't been disturbed in some way in the past so  it's kind of like the least disturbed area that we   can find within this small study area the second  control is um an urban control so this has kind   of the development style that was uh happening in  the 1980s so large detention ponds are kind of the   primary uh form of stormwater management within  this watershed and then the newer development   that's gone in has more of these green storm water  infrastructure practices so there's a few big box   stores that went in recently that have micro  buyer retention so small rain gardens installed   in the parking lots that's what all those little  purple circles are there um and then you can see   the stream and um where we monitor downstream  is the diamond and then um the retention ponds   are shown as little diamonds in that um far  map there and then the county has monitored   um stream cross-sections in these watersheds  so that's what those little um crosses are and then the treatment watersheds these are the  ones that went from agriculture to development   you can see all the storm water practices there  as the little circles so hopefully you can see   that in the image there but they also designed  the roadways to have swales rather than curving   gutters so trying to further disconnect those  impervious services from the the stream itself   so you can see things like drywalls that were  installed behind houses so that takes water from   the rooftops and tries to infiltrate it into the  ground right next to the houses not like directly   next to the house but close by uh the houses and  then um some other practices so these all of these   storm water features are arranged in a treatment  train so one uh water from one practice goes   to another for sort of redundant uh storm water  treatment now this is the second Watershed that   um underwent development it has 44 impervious  cover so it's like pretty uh built out within   this watershed it's a mix of single-family homes  and townhouses so a little bit denser development   and then it has almost twice the number of storm  water practices so this is even like smaller   practices things like tree box filters which  are just look like kind of a a tree but it has a   um gravel area underneath the tree that  provides storm water storage and some treatment   along the roadways and then there's also things  like infiltration trenches near the roadways   and then the last site that we've been monitoring  this is the one that's kind of currently finishing   becoming suburban neighborhood so you can see  some construction activities in this image here   this one has 20 impervious cover and a fewer  number of stormwater practices because they're   still we're being built when uh when we finish  this study um and then it has a larger sort of   forested buffer area so the bottom part uh is  still fairly forested so what we've been doing   is looking at a suite of different string  functions we're looking at hydrology water   quality geomorphic changes and then changes  in the benthic community itself within the   stream so I'm just going to highlight some of  the the main findings that we found for these   um four general categories of sort of stressors  and stream functions that we've looked at um so USGS has been monitoring streamflow in  these watersheds since 2004 like I said before   so we monitor flow every five minutes uh so  we've taken that entire streamflow record and   identified every single um storm event that  happened and then matched that up with the   corresponding rainfall event and this is showing  you the peak stream flow during each of those   different events matched with the precipitation  amount during that event so you can see for small   events these treatment watersheds function  fairly similarly to the forested watersheds   they have lower peaks than the urban control  site which is that older type of development   so for these small events we're talking about  like 10 millimeters of rain the stormwater   practices seem to be doing a pretty good job  at mitigating that flow but then as you get   to larger and larger events these practices  are only designed to manage about an inch of   rain so you see that benefit sort of filtering  off for these larger and larger storm events   but the um treatment one watershed still has  significantly lower peak flows than the urban   control site even for those um large events even  though it's not quite as good as the forested   site we still see some you know benefit of these  practices for some of these larger rainfall events I also compared the peak flows since these  watersheds are so close to each other you know   we can assume that rainfall rainfall patterns are  fairly uh similar across this area although in the   summertime that gets a little messy but um we can  compare an event that happened in in one of these   watersheds to the other one so each of these dots  here represents one rainfall event that happened   and then the peak flows from the two different  treatment watersheds during that event so anything   any dots that are above the line here indicate  higher peak flows in the treatment two watersheds   and you can see that most of the peak flows  were larger in that treatment two Watershed than   the first one now 86 percent of the events were  higher in the treatment two watershed and we can   just think about the differences between these two  watersheds the treatment to watershed has like 11   more impervious cover so there's a lot more water  to manage in that watershed which might be why   um those peak flows are higher even though it  has a denser uh higher density of stormwater   practices so it's not just looking at you know  the amount of impervious cover or the number of   um it's not just looking at the number  of stormwater practices but like the   um you know the relative contribution of that  storm water to the number of practices that   are implemented so because there's so much  more impervious cover and these are smaller   practices they're not necessarily  able to handle that amount of water uh we have a little bit of nutrient  data so we've been looking at   um base this is base flow nitrate  concentrations in these streams over time and   um we can see the nitrate concentrations are  elevated in all of these um watersheds like   above one milligram per liter because of the  agricultural legacy of this area so groundwater   um get them up here uh groundwater uh  concentrations of nitrate in this area   are elevated due to you know the agricultural  legacy so this map here is showing just the   areas where you would expect groundwater nitrate  to exceed three milligrams per liter so but we can   see that you know after development happens in  this treatment one watershed which is the blue   um circles here nitrate concentrations decline by  half for base low in these in the stream and in   the groundwater which is the triangles there we  don't have a ton of groundwater monitoring data   but we have a little bit of groundwater data  and we can see that those concentrations have   declined over time after construction has kind of  ended but they still remain fairly elevated and   even though concentrations have gone down we've  actually seen um base flow in these streams go   up so the overall export of nitrate has remained  above roughly the same in this watershed over time   um and it still remains higher than at the  forested um reference site and the urban   control site we've also looked at um specific  conductance so this is just showing uh changes   in specific conductance in the stream so that's  kind of like how salty the stream is and you   can see that specific conductance has increased  in um all of these watersheds uh the treatment   watersheds during development but at different  sort of rates because of the timing of when that   development happened and the urban control site  has the highest specific inductance concentrations   now you've also been able to look at changes in  the stream itself so the particles within the   stream channel and you can see that during  construction in both the treatment 1 and   treatment two watersheds you see that sand and  silt and clay increased uh during construction   or after construction in these two watersheds  this has which has important implications for   the habitat that the bent the community relies  on we've also looked at changes in stream cross   sections so here's just a a gift hopefully that's  working um showing changes in the stream channel   itself so the county has gone out and monitored  uh cross sections within these watersheds since   about 2003 so we've been able to look at how much  stream bank erosion has happened and then how much   down cutting has occurred within these channels  and we find that the channels were in size like   prior to development and they're continuing to  widen and deepen after development has ended which   might be related to that increase in peak flows  within the watershed it's great to have this like   really detailed information about one site but we  wanted to look more broadly across you know whole   watersheds we have repeat Airborne lidar data that  we can look at changes over time within the whole   watershed so this is showing a hill shade from  a digital elevation model within the watershed   in 2013 um and then we can look at how much change  has happened since 2002 before development happens   you can see how much uh the landscape was  changed to um you know grade the landscape   to make room for roadways and um houses and we  can actually look at uh you know differencing   this to DEMs to see areas that were filled in  which is the brown um areas on this map so you   can see streams and springs that were buried  uh due to development and then also areas that   were excavated to flatten the hilltops to make  a room for the houses and we can try to estimate   the total amount of Earth that was moved across  um the whole watershed using these types of data   um and then lastly I wanted to kind of give  you a sense of what's happening in the streams   themselves so that's what the county was most  interested in was like you know trying to preserve   the biotic Integrity of the streams within this  area because they're and were deemed to be pretty   um high quality and sensitive um habitats  before the development ended so they wanted   to try to promote uh you know development  that minimized the impacts on the actual   um benthic community within the stream so they've  been monitoring um the benthic macroinvertebrates   within the stream over time so we can look at how  something like the index of biotic Integrity have   has changed over time so this is just going to  show you a series of different plots here any   any numbers that are higher indicate a healthier  stream and lower numbers indicate poor scores   so we can look at the forested side and the urban  control site and the forested site has remained in   good excellent condition over the whole monitoring  period whereas the urban control site has remained   in threat to poor condition and then we can  look at changes over time as these watersheds   underwent development so the box the gray box in  these figures shows that timing of of development   um so this is before kind of when the  development's sort of starting you can see   all three of the treatment watersheds are in good  condition and then as development progresses you   see a drop in the scores during construction  within the treatment one watershed but then   it sort of rebounds to good condition um at the  end of sort of the phase of development and the   treatment II watershed kind of starts to become  developed and then starts to degrade during that   construction period you can see kind of the land  cover changes that are happening at that same   time and has kind of remained in um poor to fair  condition after development and then the treatment   three watershed which is just starting to um  you know undergo construction is is still kind   of uh bouncing around but has uh fallen into the  sort of fair condition in the more recent years um so just to summarize here some lessons  learned from using this sort of distributed   stormwater control that this type of stormwater  management can attenuate peak flows and runoff   volumes but you know the storage capacity  within those practices really matters and   we can see differences in the performance  of those events based on you know how long   ago the previous rainfall event had happened  they can also improve water quality we've seen   reductions in sediment export within these  watersheds in one of the watersheds but we   don't see those benefits for all constituents  particularly salt in this these watersheds   and that it can reduce the impacts on biota  but the sensitive families haven't been able   to recover in these streams even though you're  seeing kind of a rebound n those benthic scores   the sensitive families uh still are in pretty low  abundance in these streams and that distributed   storm water control isn't always better if you  have more impervious covers you need to think   about you know trade-offs between allowing  more development within the watershed but   also creating more storage to go along with  that development and then lastly we found that   the construction phase is pretty important  in sort of triggering some of these impacts   to the watershed this is the time period when you  see substantial changes in the topography and kind   of how the flow paths within the watershed get  messed up and moved around within the watershed   um and we also see an increase in in  fine sediments within the stream channel   um during the construction phase so even though  there's sediment erosion control practices uh you   know going along with construction we're still  seeing the impacts of that construction in the   stream channels downstream of that disturbance so  that's all I have um if you want to know more chat   with me I'm excited to talk to you more about this  and thanks so much and happy to take questions   [Applause] questions uh we do have we do have some temperature,  we haven't actually analyzed it yet, so   uh we're actually trying to  incorporate that into a model   to kind of look at how these different  stressors combined impact the benthic community. any more questions okay okay have you looked into the characteristics  every Watershed in terms of a slope average slope   or Channel slope because that can be you know  a very good Factor actually yeah so we actually   see some impacts with um differences in slope  especially with the processing data you can   you know make one or two profiles communication  models both very uh in the channel and also like it increases due to the grading and  just kind of a rebounding of like where how all of these different somewhere else the differences that we're seeing between higher slopes in this area here  especially when there's roadways division was based a lot of decreases um so we're seeing like longer falling  lands within the hydrocraft and those   kind of remaining elevated afterwards  so that might be one one reason uh some   other similar practices there all of them are  supposed to be kind of infiltration tokens but   they all have like under drains so  a lot of that water is like that is yeah so um it's um you know we've mostly looked  at this one Watershed which we have fun for the   longest uh Stream flow records we're curious  to see like each other for watersheds over time I'm gonna see that so you can see that you'll choose the record you know it's a time period and it's not working no no  it's not as far as I can so uh   foreign well thank you again Christy I  look forward to being able to see your   presentation online um later because  I do believe I didn't get a chance to   see it and now we have Matthew we found  his presentation New Wave information but I guess there's more pressure on me now  that I have to go last but I'll try yeah   um so yeah my name is Matt Scalora I'm a lead  meteorologist at the National Weather Service   forecast office in Wilmington North Carolina  I'd also like to recognize Mark Willis the   meteorologist in charge of my office as well as  Darren Wright from Weather Service headquarters   in Silver Spring Maryland for definitely being  greatly uh involved with this project as well   and you can see there it's going to cover  note the new wave information included in   the NWS Coastal Waters forecast which I'll  commonly refer to as the CWF in the talk and in terms of all the folks that went  into this work and this project itself   there's definitely a lot of contributors  over a couple dozen people on the National   Weather Service National wave team  with representation from all around   the country every coast and even a couple  folks from the Hawaii office as well so before we get into how we're inserting the  wave forecasts into our products themselves   let's take a basic overview of waves waves  have three very fundamental variables that   being hype period and Direction height is the  distance from the crest to the trough of the   wave measured in feet and direction is where  the waves are coming from sort of like what we   do with winds not where it's going to and period  uh definitely one of the more important variables   that we measure in seconds is the time it takes  for two successive crests or troughs to pass a   fixed point in the ocean and that could be for  instance a buoy or some other Standalone object   and it's very common to have coexisting waves  at any point in the ocean and that can be near   shore it can be way out in the ocean and all  these locations are going to have high unique   height period and Direction and each of them  will likely be interested particular Marine   groups in the next couple slides I'll give you  two examples of that and for the time being in   our Coastal Waters forecast we're definitely  oversimplifying the forecast by just providing   a significant wave height significant wave  height being the highest one-third of all   waves so we definitely like to add that wave  detail value because we think that providing   it is definitely vital for Mariners to decide  if it's safe to venture out on any given day so two examples to start off here currently we're  just providing a forecast saying C's eight to ten   feet uh even though the picture looks a little  taller than that you get the general ideas these   are big big waves so a big boat might think  that's all right they can handle it however if   they saw a period forecast of 22 seconds they  might realize that a Perry that long is going   to generate s

2022-12-19 20:12

Other news