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Steph Shepherd  (she/her): Welcome everybody i'm glad there's a few people here already sorry it is taken me 10 minutes to login.

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Steph Shepherd  (she/her): But i'm here now, and we should be able to get started on time.

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Kimberly Takagi: Hello.

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Steph Shepherd  (she/her): Yes, hi Kim I can hear you.

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Kimberly Takagi: Oh sorry um.

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Kimberly Takagi: Can you hear me now.

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Kimberly Takagi: Yes, Okay, I apologize sometimes my speakers come up and I sound like a chipmunk so I was testing it.

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Kimberly Takagi: And I see that we're co hosting together.

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Steph Shepherd  (she/her): yeah did you not get any of my emails.

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Kimberly Takagi: No.

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Steph Shepherd  (she/her): i've been emailing you for days and you never responded.

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Kimberly Takagi: i'm so sorry i'm my email here is very strange like I can't get emails from somebody from middle Georgia State University, for whatever reason, and it's just me.

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Kimberly Takagi: My colleagues get it.

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Steph Shepherd  (she/her): And I tried, I tried your other email, and it told me it didn't work so.

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Steph Shepherd  (she/her): Really yeah.

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Steph Shepherd  (she/her): I, it was an invalid email.

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Kimberly Takagi: Really.

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Steph Shepherd  (she/her): So we weren't sure you were going to be here so i'm really excited to see you.

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Kimberly Takagi: I did not get any of those you know i'm so sorry anyway um so.

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Kimberly Takagi: um so what would this do you mind bringing me up to speed.

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Steph Shepherd  (she/her): i'm sorry yeah so I met with Lisa and Shannon I haven't heard from any of the guys.

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Steph Shepherd  (she/her): That are on this at all.

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Steph Shepherd  (she/her): But i'm gonna try to steer the ship this morning and least is going to try to steer the ship this afternoon.

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Steph Shepherd  (she/her): Okay, and whatever help you can be is great, because I do have to do this from home i'm gonna.

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Steph Shepherd  (she/her): Have a background.

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Steph Shepherd  (she/her): So there might be a moment where like the dog barks.

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Steph Shepherd  (she/her): Or you know, I have to change a diaper or whatever it's.

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Kimberly Takagi: Totally understandable, please do.

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Steph Shepherd  (she/her): So yeah.

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Steph Shepherd  (she/her): Well hey.

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Steph Shepherd  (she/her): I have, I have the schedule, right here yeah.

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Kimberly Takagi: cool I I was gonna say that if you would, if you would like me to steer the ship from this after the break I can do that.

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Steph Shepherd  (she/her): That would be great yeah we can trade off.

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Kimberly Takagi: Okay cool.

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Steph Shepherd  (she/her): And let's see there's one other thing I do know one person this morning is not going to make one presentation did not make it.

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Steph Shepherd  (she/her): Okay, that is number five.

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Steph Shepherd  (she/her): cali and Andy from the Department of environmental quality division of water reasons why they contacted me there's something came up and they were not able to participate.

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Steph Shepherd  (she/her): Okay contacted me last week.

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Steph Shepherd  (she/her): My ask them if they wanted to go ahead and upload a.

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Steph Shepherd  (she/her): Presentation they were unable to do that.

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Okay.

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Steph Shepherd  (she/her): yeah auburn got has gotten real strict with emails as well, and so things do sometimes disappear and it's super frustrating i'm like I was expecting stuff.

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Kimberly Takagi: Like I was like well I don't I haven't heard from anybody i'm not in touch with anybody and a little bit I was a little bit in the dark as to what's going on, and now I know why.

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Kimberly Takagi: Sorry, no that's I guess that's on me um.

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Kimberly Takagi: I wonder what email address you had for me then.

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Steph Shepherd  (she/her): I have your work, email, and then I think the Yahoo email.

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Kimberly Takagi: Both of which should have been working.

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Steph Shepherd  (she/her): yeah cuz I reached out to katie brown at Article who's helping coordinate from RN and I was like do you have another email address.

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Kimberly Takagi: yeah and she was like yep okay.

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Steph Shepherd  (she/her): Let me just make sure the one thing I wanted to be sure of is that everything is what what make sure I have the timing written down correctly real fast for this morning.

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RISE Justin Samuel, he, him, his: stephanie Kimberly good morning hi this is justin Samuel on with GSA and I just wanted to introduce myself and let you know that I was going to be sitting in on the session.

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RISE Justin Samuel, he, him, his: To make sure that you didn't.

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RISE Justin Samuel, he, him, his: Have any problems and forth back to the meetings department, if you did.

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Steph Shepherd  (she/her): I never got the thing that i'm seeing this slide i'm supposed to show from rise.

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RISE Justin Samuel, he, him, his: OK, I will let them know that immediately.

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Steph Shepherd  (she/her): Because I would sure love to share that.

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yeah.

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RISE Justin Samuel, he, him, his: And I just wanted to give feedback that when I look at the schedule for the session the fifth one I don't I can't access the recording, but it does say recording available.

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Steph Shepherd  (she/her): For me, I have a recording for number seven.

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RISE Justin Samuel, he, him, his: For number seven.

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Steph Shepherd  (she/her): Not for number five.

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RISE Justin Samuel, he, him, his: looks like becky got to the.

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RISE Justin Samuel, he, him, his: slide there.

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Steph Shepherd  (she/her): Oh great Thank you, thank you.

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Steph Shepherd  (she/her): yeah and I had asked them, I mean because they let reach out to me last week and I said well you know the you could just upload they said no we're not prepared to do that I was like Okay, thank you.

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Steph Shepherd  (she/her): So, last night I checked again and had I only have the number seven downloaded and there's one after lunch, that I was able to download.

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RISE Justin Samuel, he, him, his: So it's interesting I don't see a recording for seven on the schedule and so that just to just to clarify this is to dash seven travis simmons.

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Steph Shepherd  (she/her): No that's not.

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Steph Shepherd  (she/her): that's yeah I don't have a recording for travis simmons at all.

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Kimberly Takagi: I have a recording for unveiling.

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Steph Shepherd  (she/her): aka for response that's what.

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Steph Shepherd  (she/her): I have.

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yeah.

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RISE Justin Samuel, he, him, his: yeah and that's what i'm showing so that's two dash five, at least as the way that the scheduled presents.

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Steph Shepherd  (she/her): yeah that we don't see that I don't see those numbers at all.

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Steph Shepherd  (she/her): yeah I see so wait okay.

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Kimberly Takagi: Well, on the so on the schedule.

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Steph Shepherd  (she/her): yeah so like i'm trying to schedule, it looks different that you don't get those numbers.

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RISE Justin Samuel, he, him, his: I see i'm just looking at the like the Web APP you know the site that you joined through.

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Kimberly Takagi: yeah, though, the one that they sent to presenters or the link that I have for presenters that has the recorded presentations on it that's the one that it says seven on.

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Steph Shepherd  (she/her): yeah they look different on that the numbers don't match.

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interesting.

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Kimberly Takagi: yeah.

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RISE Justin Samuel, he, him, his: joy alright well, let me.

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Steph Shepherd  (she/her): So I see what you're talking.

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RISE Justin Samuel, he, him, his: About but.

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Steph Shepherd  (she/her): I see what you're talking about to dash for there is no presentation to dash five there is.

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Steph Shepherd  (she/her): I can see at yeah because I pulled up the website.

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yeah.

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Steph Shepherd  (she/her): So so i've led sessions before like at big meetings and at regional meetings, and so this whole online thing feels very familiar and very strange.

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Steph Shepherd  (she/her): Oh absolutely excellent i'm just glad we're doing it.

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RISE Justin Samuel, he, him, his: me too.

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RISE Justin Samuel, he, him, his: me too.

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RISE Justin Samuel, he, him, his: um so this rise slide is this gonna work for you and then.

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RISE Justin Samuel, he, him, his: becky Maybe you can chime in and.

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RISE Justin Samuel, he, him, his: And you'll just.

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RISE Justin Samuel, he, him, his: take over the screen sharing presumably.

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Steph Shepherd  (she/her): Well, and it also, if I can find it in my email, will be able to put it back up easily at the break or you know what the brand yeah.

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RISE Justin Samuel, he, him, his: Great Okay, let me, let me go ahead and.

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RISE Justin Samuel, he, him, his: step away and make sure that.

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Rebecca Sundeen: hey it's becky sorry just to test it here got.

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Rebecca Sundeen: got a couple going on at once, but i'll stop screen sharing just let me know whenever you're ready i'll just I know we were supposed to start here in about four minutes so probably right at seven i'll stop my screen sharing and you can put up whichever.

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Rebecca Sundeen: Or, if you want me to leave it up during the five you know I think there's like a few minutes for introduction, so I can leave this.

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Rebecca Sundeen: do that so.

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RISE Justin Samuel, he, him, his: Certainly becky could you.

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RISE Justin Samuel, he, him, his: Also, just email it to stephanie so she has it and she can restore it during the break.

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Rebecca Sundeen: Yes, mm hmm.

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Steph Shepherd  (she/her): I can just send you.

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Rebecca Sundeen: should be able to just put it in the chat so let me pull that up real quick about the chat.

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RISE Justin Samuel, he, him, his: Thank you becky.

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Steph Shepherd  (she/her): Thanks so much Okay, make sure I can see what parameters, we are almost.

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Rebecca Sundeen: I think i've got about three minutes till hey.

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Steph Shepherd  (she/her): Kim just so I say it right, how do you say your last name.

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Kimberly Takagi: To corgi.

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Okay.

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Steph Shepherd  (she/her): Thank you.

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Rebecca Sundeen: And one other thing I was going to mention, we do have a tech on this session with us.

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Rebecca Sundeen: Okay, and he is monitoring.

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Rebecca Sundeen: A couple different sessions, so if if you do need tech support you can privately.

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Steph Shepherd  (she/her): messaged yes.

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Rebecca Sundeen: And the they already tell you that are.

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Steph Shepherd  (she/her): And then that way, though.

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Rebecca Sundeen: Okay yeah just shows up and read on their screen so then it just it alerts that a little bit.

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Steph Shepherd  (she/her): more alert back over so.

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Okay.

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Steph Shepherd  (she/her): And I think.

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Steph Shepherd  (she/her): The one other thing I need to do last moments.

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RISE Justin Samuel, he, him, his: Likewise, if you if you need anything on the GSA side you know, please just shoot me a message in the chat as well i'll be here through the whole session.

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RISE Justin Samuel, he, him, his: Thanks.

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Steph Shepherd  (she/her): There was one other thing we got like a minute.

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Steph Shepherd  (she/her): Where is college a coastal Georgia.

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Kimberly Takagi: It is in brunswick.

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Steph Shepherd  (she/her): Georgia okay.

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Kimberly Takagi: So we're about an hour north of jacksonville and an hour south of savannah.

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Steph Shepherd  (she/her): part of Georgia, I haven't been to yet.

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Kimberly Takagi: Oh, you should come it's pretty.

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Steph Shepherd  (she/her): yeah it's on it's on the list we've actually been planning a vacation over there and then.

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Steph Shepherd  (she/her): So yeah we got to put it back on the list.

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Kimberly Takagi: Hopefully, hopefully, you guys can be free again soon.

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Kimberly Takagi: to travel.

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Steph Shepherd  (she/her): Okay, I think we got about a minute.

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Steph Shepherd  (she/her): This is great.

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Steph Shepherd  (she/her): so glad to see all the participants.

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Okay.

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Steph Shepherd  (she/her): It is right at eight o'clock.

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Steph Shepherd  (she/her): So I might just give it one minute, because if people are like me, despite the fact I was trying to log in on time.

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Steph Shepherd  (she/her): I got stymied by for getting my GSA path ID.

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Steph Shepherd  (she/her): You would think I would know it i've had the same GSA ID for like 15 years.

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Kimberly Takagi: Is when you're under pressure you're like all.

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Steph Shepherd  (she/her): Right i'm like Oh, I know it's 905 but what do I do after that I was like I gotta go find it in my email.

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Steph Shepherd  (she/her): Okay, if it is eight o'clock i'm going to.

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Steph Shepherd  (she/her): share my screen.

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Steph Shepherd  (she/her): Okay well it's Ada one so let's get started welcome everyone to the two one of the first sessions, I think there was a few things yesterday for our south eastern section GSA meeting hosted by auburn university and auburn Alabama but apparently taking place everywhere.

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Steph Shepherd  (she/her): i'm stephanie shepherd i'm a human biology professor at auburn university and the Department of do sciences and so have been very excited and looking forward to today because i'm we have a great list of presentations.

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Steph Shepherd  (she/her): i'm going to be co hosting this morning with Kimberly to got to God, she is that almost correct.

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Steph Shepherd  (she/her): Carnegie Carnegie Thank you college and she's from college of coastal Georgia.

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Steph Shepherd  (she/her): And just to remind everyone of the kind of the general guidelines as a group, provided to us by GSA through the rise program to be respectful and inclusive and the information is on the screen also.

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Steph Shepherd  (she/her): How this is going to work is that if you're here to make a presentation, almost all of our presenters are we have made it possible for you to share your screen.

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Steph Shepherd  (she/her): So you will when it's your time you will share your screen and get started and we'll just make sure that your microphone is off and all of that, please everyone else keep your microphones off if at all possible, so that we.

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Steph Shepherd  (she/her): don't get some of the weird background noises, myself included, because I am at home, today, and you will probably at some point hear my dog.

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Steph Shepherd  (she/her): So you know we're all very comfortable with zoom these days so just just remember to keep that microphone off if you're not talking.

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Steph Shepherd  (she/her): Each presentation, we have a 20 minute window for each presentation this morning and so i'm hoping that presenters will present for about 15 minutes and leave us for about five minutes to ask questions.

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Steph Shepherd  (she/her): If, as long as you are not going over that 20 minute time I won't cut you off, but I will try to get your attention, through the window.

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Steph Shepherd  (she/her): And let you know if there's only two minutes when there's only two minutes left of that 20 minute time window.

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Steph Shepherd  (she/her): I also suggest that you if you are good at this and can multitask use your phone and set your own timer that that would be helpful, and I will as well um.

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Steph Shepherd  (she/her): Other things oh so we have you know about, I think we have about 20 people in here right now it'll probably fluctuate through the day, but if you would like to ask questions you can.

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Steph Shepherd  (she/her): do two things you can put your hand raise on the screen, you know the little thing you clicked raise your hand, or you can type something into the chat and I will hopefully.

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Steph Shepherd  (she/her): Kim and I will hopefully help monitor those and make sure.

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Steph Shepherd  (she/her): that people are able to ask their questions and, as I mentioned before, I know that we are missing a presentation right before the break so if people have other questions that go beyond that 20 minute time we will have a little time to have a discussion as a group.

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Steph Shepherd  (she/her): Any big questions concerns 804.

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Steph Shepherd  (she/her): Okay we'll get started we'll start the first presentation i'm awake right on time and this moment in this minute i'm gonna get my timer set up on my phone.

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Steph Shepherd  (she/her): Oh yes, and I was going to give myself a background I haven't gotten to that yet today.

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Steph Shepherd  (she/her): timer.

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Steph Shepherd  (she/her): Okay.

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Steph Shepherd  (she/her): So it is now 805 and I am excited to introduce our first speaker kristin steel and she was speaking about the 4000 year drought history of the southeastern us constructed from lake sediments.

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Steph Shepherd  (she/her): or excuse me from lake low stands interpreted from sedimentary logical and geophysical proxies in Lake Jackson Florida, so I need to stop my share for you so that you can share your screen there we go.

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Kristen Steele: All right, let's see here.

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Kristen Steele: there's one on.

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Kristen Steele: me try this again i'm sorry.

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Steph Shepherd  (she/her): There it's getting started for you.

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Kristen Steele: how's that.

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Steph Shepherd  (she/her): We see it.

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Kristen Steele: Okay.

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Kristen Steele: Okay cool alrighty well, thank you for that Nice introduction as Jeff said i'm kristin steel, and I am with the US geological survey in the Florence basket geoscience Center.

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Kristen Steele: And i'm excited to get this session kicked off, I agree, I think it looks to be a really diverse and an interesting session of presentations for sure.

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Kristen Steele: And so, before I get started, I did just want to make a quick request to GSA and any affiliates or anyone in the audience that my presentation, not be recorded or save for any archive purposes or anything like that.

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Kristen Steele: So, again i'll spare you the my long title again because i've already said it, but there it is again.

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Kristen Steele: So first i'd like to acknowledge, those who helped, both in the field and in the lab funding for this project was provided by certain grant as well as the usgs climate research and development Program.

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Kristen Steele: So our intention with this project here is to reconstruct a high resolution Paleo record that captures the hydro logic variability in the southeast that extends much longer than the instrumental record.

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Kristen Steele: Because you know I think everyone knows here that it's no secret that the region se it's very familiar with droughts.

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Kristen Steele: And the lower 48 Delta, the second most costly a natural disaster in terms of dollars and deaths and they're only second behind hurricanes.

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Kristen Steele: More recently, the region experienced a benchmark drought between 2006 and 2008 and this map here is showing the moisture deficit in 2007 and the white stars are indicating the five most populous cities in the southeast and each one of them experience this benchmark drought.

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Kristen Steele: The stakes are really high when these highly populated areas experience, drought and prolong jobs can lead to legal battles over public water resources, during this benchmark drought and Georgia Alabama and Florida argued over the rights to water resources provided by a reservoir.

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Kristen Steele: And this legal battle continued in 2020 as a battle actually made its way to the Supreme Court.

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Kristen Steele: Under IPCC quote worst case scenario regarding feature feature fossil fuel use central southern and the panhandle of Florida are the three regions of the state that have the highest risk for feature drought.

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Kristen Steele: And in these three regions, there are especially vulnerable populations who are at a disadvantage.

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Kristen Steele: About 3.2 million floridians in these regions are socially vulnerable and 720,000 are medically vulnerable, so this is demonstrating that the effects of drought aren't felt equally amongst all the members of the affected communities.

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Kristen Steele: The CDC has identified drought as a natural disaster that has a negative impact on human health, so, for example, during a drought, those with respiratory conditions like asthma.

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Kristen Steele: may be negatively affected due to the dryer air and increase airborne particles, so in that example, they would fall under the medically vulnerable, and they would be at a disadvantage.

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Kristen Steele: Because there is so much at stake for these jobs, instead of communities governments have published drought response plans.

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Kristen Steele: So the Alabama drought response plan was published in 2014 and it outlines the states for levels have dropped declaration using the Palmer job Severity Index or PDF.

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Kristen Steele: PDF size and metric that considers both air temperature and precipitation to calculate a relative dryness value or negative PDF site indicates dry conditions and positive indicates what conditions.

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Kristen Steele: We compiled instrumental PSI data from sources near are courting site, you can see, the region has experienced periods of significant drought, since 1950 CTE.

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Kristen Steele: And that all four levels of these declarations were hit with 1955 just being a hair away from a drought emergency.

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Kristen Steele: And because there's so much variability and instrumental record, it is imperative that we can start hedge logic records to figure out how frequent and how sustain jobs have been over the last few millennia.

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Kristen Steele: So Okay, what are the forces that are in play when we consider the occurrence of drought in the southeast well one major control is the strength and location of the loop current.

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Kristen Steele: On the loop cramp brings warm Caribbean water through the yucatan channel moves clockwise around the Gulf and then exit through the streets of Florida, and then joins the Gulf Stream in the Atlantic.

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Kristen Steele: We completed a series of time series correlations among instrumental data to better understand the mechanisms of modern Joe.

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Kristen Steele: Gulf of Mexico see surface temperatures are directly related to the loop crunch strength were stronger loop current which typically occurs in summer months is associated with warmer estes.

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Kristen Steele: We found that Gulf of Mexico sst are significantly negatively correlated to PSI, meaning that we're SS to lead to dry conditions.

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Kristen Steele: Further, we found that Gulf of Mexico ssh keys are significantly positively correlated to the land, air temperature over Alabama and Florida.

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Kristen Steele: The measurement the mechanism that we're proposing here is that a stronger loop current causes drought by warming the air temperature and then driving enhanced evaporation conditions on my hand.

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Kristen Steele: So you're the modern precipitation and temperature data from our site, and despite the high precipitation and the summer months.

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Kristen Steele: When the loop current is most influential the increase in air temperature of results in enhanced evaporation which then results in a more negative PSI so dry conditions.

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Kristen Steele: So, moving on to our sites specifically lake Jackson, it is dissected by the Alabama and Florida state line it's located in the yellow river drainage basin and has a drainage area of a little over an acre.

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Kristen Steele: lake level and precipitation data collected by the Alabama state geological survey demonstrate that the fault that following large precipitation events which has shown with those purple bars.

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Kristen Steele: Excuse me, following those lake level increases, which is shown with the blue dotted line on top of the purple bars.

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Kristen Steele: Thanks Jackson is a close based on like, so there are no significant inflows outflows, meaning that lake level is driven by fluctuations and precipitation and evaporation.

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Kristen Steele: The lake shore line is composed of course screens and green size decreases and the deeper parts of the beasts and where the finer Greens are found.

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Kristen Steele: So the essence of our reconstruction here is that during drought, the course sands on the shoreline migrate and words as a result of a lake level drop.

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Kristen Steele: The course shoreline faces would then be found towards the deeper parts of the lake so to capture the shoreline changes, a 6.7 meter long vibe record was collected in 2011 from a 7.8 meters deep spot in the water.

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Kristen Steele: And this Korean site specifically was chosen, because this was the spot, that was most likely to have the longest and most continuous record in the lake.

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Kristen Steele: After the course collected we completed last on a mission to measure organics green size, which was done at James Madison university it using a little laser particle size analyzer.

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Kristen Steele: We also did X Ray for fluorescence which was collected on an eye tracks CT scanner at woods hole and additionally seismic data were collected by the Alabama state survey in 2004.

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Kristen Steele: here's a good example of one of the seismic profiles that were generated from the seismic data, and we can see original truncation and on that features in the sub bottom strata.

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Kristen Steele: And these features are evidence that in Lake Jackson, we can see that it has experienced lake level fluctuations that are recorded in the cemetery record.

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Kristen Steele: So it is possible that coarser sediments may be transported during high precipitation and run off events, but with the seismic profiles were able to delineate the core sediments from one single runoff event versus sustained low stands.

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Kristen Steele: here's our age bottle as produced by bacon and bacon takes a bayesian approach to age to age modeling.

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Kristen Steele: Our age model here is derived from lead to 10 activities in the core top and for macro fossil radiocarbon ages, we also had to bulk sediment radiocarbon ages, but those were rejected in favor of the macro fossil ages.

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Kristen Steele: bacon gave us a base wage of about 3600 years before 1950 CE and the model produced a record that has a linear sedimentation rate.

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Kristen Steele: So just for a quick fun little history ever site and lake Jackson got its name from the first seminal war when Andrew Jackson briefly set up camp at the week of.

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Kristen Steele: The neighboring city of morality, the north was subtle more permanently following the civil war, as people migrated South for opportunities in agriculture.

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Kristen Steele: Here aerial photos that illustrate helpful when you says change in the 20th century and just by I test i've identified with the rectangles here are some areas that change between 1946 and 1973.

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Kristen Steele: And because of these changes, the landscape, the catchment sentiment is destabilized and it can move more easily during weathering events.

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Kristen Steele: So what interpreting the most recent part of our record, we need to consider the anthropogenic signals that may be recorded so here, I have our organics mass accumulation rate in grams per centimeter per year, which we calculate from the loss on ignition.

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Kristen Steele: The organic mass accumulation rate is steady over the last 500 years, except for 1973 to 1988 where we see a five and a half time in Greece.

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Kristen Steele: This increased suggests that the accumulation of organics in the lake increase because of an influx of organic material being delivered to the basin and or increase productivity.

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Kristen Steele: We have interpreted this signal here to be anthropogenic and hesitate to include it in our climatic interpretations as the climate signal could be over printed by human influence.

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Kristen Steele: or interpretation of this increased organic accumulation is supported by documented floods, such as the April 1975 flood.

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Kristen Steele: Which is described as the heaviest rain and 50 years and as much as 17 inches of rain fell in 48 hours so with this newly clear landscape that we saw in the aerial photos.

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Kristen Steele: destabilize materials in the catchment could have been easily transported by co heavy rain falls, as this one newspaper headline describes it.

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Kristen Steele: So these are the results of our Green size analysis for the entirety of the record.

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Kristen Steele: With the 90 and medium sized plotted so the 90 is a great insights measurement that represents the 90th percentile bring in diameter and it's useful because it captures most particle sizes and a single sample.

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Kristen Steele: Well, excluding the largest ones media ingredient size represents the 50th percentile.

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Kristen Steele: i've also highlighted at the top, the average D 90 values for the last 100 years were D 90 grand size reaches its maximum value in the record, we also interpret this interval to be influenced by human activities so we're going to exclude it from our climatic interpretations as well.

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Kristen Steele: The vertical bars here highlight a few course beds in the core where we see increases in both median and 90 and although the two data sets are different in terms of magnitude of change, we see increases in both data sets that occur around the same time and for roughly the same duration.

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Kristen Steele: Additionally, we see green size magnitude increasing between the older and younger halves of the record.

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Kristen Steele: The average D 90 grand size from 2000 to 3000 years before President is 45 microns the average D 90 from 102 1100 years before President is almost 60 microns.

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Kristen Steele: So this is telling us that there's a marked increase in D 90 of 15 microns between the older and younger halves of the record the average grade size is increasing through time.

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Kristen Steele: here's our DNS record with brown bars that are placed here on D 90 increases that we have interpreted as droughts, so these jail intervals occur throughout the record with varying frequency and duration and in the earlier part of the record.

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Kristen Steele: Jobs are not as frequent as the brown bars are like space further apart in time, then the later part of the record jobs, become more frequent and some are sustained for multiple centuries.

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Kristen Steele: We also looked at the ratio of xrs derive I into manganese which this proxies interpreted as reducing conditions in the lake bottom due to a lake level rise and we're stratified water column.

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Kristen Steele: So during these reducing conditions manganese becomes more scalable which then makes iron more comparatively abundant which does increases the ratio.

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Kristen Steele: So periods when this ratio is high, so just wet conditions, the blue bars are highlighting high periods in the iron manganese ratio, which would indicate awake high student.

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Kristen Steele: or interpretation of this ratio is supported by the presence of microscopic sitter right which incorporates the reduced form of iron.

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Kristen Steele: So here's the result when you put the brown bars and the blue bars together and the white arrows in the middle, are indicating how lake levels behaving by either increasing or decreasing and these bars match up to give us a broader idea of how like levels changing throughout our record.

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Kristen Steele: We see many league level changes that are indicated by our proxies which suggests that legal has been highly variable throughout the late policy with numerous sustained droughts that lasted decades or centuries, as well as periods of deeper light conditions and reducing bottom owners.

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Kristen Steele: We also completed a multi proxy principal component analysis or PCA to confirm the relation to confirm the relationships between our proxies.

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Kristen Steele: This analysis statistically supports our interpretation of the lake level proxies that we used our grand size proxy loadings plug in the negative space for the second principle component.

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Kristen Steele: And the iron manganese ratio loading plots in the positive space, so this is consistent with our interpretation that these two proxies are behaving opposite of one another, so, in other words.

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Kristen Steele: When one proxy is increasing, the other is decreasing and notice how all of the proxies have positive loadings for the first principle component.

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Kristen Steele: here's The first principle component time series which explains 93% of the variance you can see, the values and the principal component one are generally increasing through time.

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Kristen Steele: Which is consistent with the increasing frequencies in magnitudes of both the iron to manganese ratio peaks and the peaks in green size, the highest frequencies and magnitudes occur in the most recent part of our record.

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Kristen Steele: Here is the second principle component time series which explains 19% of the variance the second principle component is illustrating high and low lake levels were positive values in PC to are indicating high lake level and negative PC two values are then indicating low lake level.

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Kristen Steele: it's put our record in the context of other late Holocene records we compare our records who a Gulf of Mexico sea surface temperature record and complete correlations between the two.

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Kristen Steele: We found a positive statistically significant correlation between Gulf of Mexico SS to increase the 90.

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Kristen Steele: This relationship is consistent with the instrumental correlations between sea surface temperature and PSI.

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Kristen Steele: When we compare these two records, we can see that, when SS ids are warmer we see drought and like Jackson considering this sst record is interpreted as showing blueprint shrink, we can infer that warmer SS team would then lead to more evaporated conditions on the Gulf coast.

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Kristen Steele: We have many more proxies that we'd like to add are planning to add on to our record, and this includes continuing work on the seismic data, adding additional radiocarbon dates to better refine our age model.

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Kristen Steele: working up diatoms and Paleo ecology any carbon hydrogen nitrogen analysis which will help us to untangle the aquatic versus traditional sources of organics.

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Kristen Steele: And that's all I got and with that i'll take any questions.

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Steph Shepherd  (she/her): Thank you, we have about three minutes left for questions so if anyone.

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Steph Shepherd  (she/her): has something pressing.

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Steph Shepherd  (she/her): i'm a green size person, so I get excited when I see all the green size data.

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Steph Shepherd  (she/her): And I also do a lot of.

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Steph Shepherd  (she/her): I do both the old fashioned tight soils type and the PCA, have you found that comparing to can you compare your data that you did on the instrument with other data records easily do you feel.

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Kristen Steele: yeah so one thing that kind of drew our attention to this area, specifically being the panhandle of Florida is that it's kind of like an underserved area in terms of records, I mean there's.

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Kristen Steele: Work that's done in central Florida southern Florida, and all of that as well and good, but there's a big difference between.

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Kristen Steele: The work in that part of Florida in the panhandle of Florida because of other different controls going on.

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Kristen Steele: So there are records that exists, I mean that's kind of why we're more drawn towards the the Gulf of Mexico reconstructions because we think that there there's a.

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Kristen Steele: closer relationship there, but then we've also been able to compare with a couple.

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Kristen Steele: of records, we have a cave record from de Soto caverns which is I I believe in Alabama Florida i'm not sure I must be compared those um and we it's kind of hard to because our record is very high resolution it's kind of also hard to find a record that overlaps.

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Steph Shepherd  (she/her): With the same.

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Kristen Steele: as ours, and especially like in terms of being in the same neighborhood of resolution.

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Kristen Steele: And so that's kind of why this is a little bit you know uncharted territory, we don't have anything to closely relate our marker to but that's what kind of makes it exciting, is that there hasn't been a whole lot of work done in this part of the US.

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Steph Shepherd  (she/her): Great any any other questions.

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it's.

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Steph Shepherd  (she/her): it's so much harder on zoom to look and see where everyone is raising their hand so much easier in a room um.

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Steph Shepherd  (she/her): Well, if the if there aren't any more questions I don't see any in the chat box or any raised hands again thank you so much.

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Kristen Steele: Thank you.

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Steph Shepherd  (she/her): I look forward to digging into this research more.

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Kristen Steele: me as well there's a lot of work that we like to do so i'm excited I think that everything is going to come together quite nicely.

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Steph Shepherd  (she/her): Okay well i'll ask you to share your screen so Benjamin can get himself setup.

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Steph Shepherd  (she/her): hey Benjamin.

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Steph Shepherd  (she/her): So.

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Benjamin Webster: doing a decent job getting set up.

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Steph Shepherd  (she/her): Okay, well, you should check and make sure you have the green share button, you should, but I will go ahead and get you enter do so then since it's 825 so our next speaker is Benjamin Webster.

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Steph Shepherd  (she/her): I he also knows a little bit about grain size, because he has had to do some of that work in my lab at various points.

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Steph Shepherd  (she/her): But today he's going to be talking about sediment transport and stoke geometric transformations in a six reservoir sequence on the chattahoochee river utilizing Paleo lemon illogical records.

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Benjamin Webster: And can you see my screen all right right now.

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Steph Shepherd  (she/her): We can.

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Benjamin Webster: Okay wonderful just wanted to make sure hi everyone Good morning, and thank you for reading off my.

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Benjamin Webster: know.

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Benjamin Webster: Dr shepherd and my co authors will be Dr Matthew walters and start Dr Stephen gold day.

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Benjamin Webster: So before we jump into it, I want to spend just a moment talking about reservoirs.

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Benjamin Webster: i'm actually really passionate reservoirs I love them reservoir constructions and extremely prolific for the past seven years now, and this originating that in states, because we were coming out of the great depression, we were creating a bunch of jobs, we needed these.

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Benjamin Webster: new infrastructure and ecosystem services which they provide a lot of in terms of water storage hydro electricity flood control fishing navigation recreation, they just find a surplus of things for people to use.

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Benjamin Webster: And we're actually still going through a little bit of reservoir boom on the global scale right now in.

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Benjamin Webster: Southeast Asia and South Asia East Asia and the subtropical region so actually still going through this boom.

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Benjamin Webster: Creating a multitude of reservoirs on a singular river system fragmenting it along the way, but they don't just provide a bunch of ecosystem services.

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Benjamin Webster: They also have dramatic changes to the environment reservoirs are extremely efficient at capturing materials and sequestering them for the long term.

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Benjamin Webster: In fact, the reservoirs have been modeled to store 26% of global river sediments 12% of global river phosphorus and 7.4% of global river nitrogen.

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Benjamin Webster: And you can see right here in this is submit ski at all figure that wins before reservoirs there's a lot more material swimming downstream, and as we included more reservoir the trapping dramatically increase what can downstream.

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Benjamin Webster: So we're actually talking about this one watersheds a little bit closer to home, one that this subject will be on.

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Benjamin Webster: The ACF watershed we heard a little bit about that a moment ago about the litigation going on between these states.

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Benjamin Webster: The ACS stands for the apalachicola chattahoochee flint we're going to be focusing on the chattahoochee today, and you can't not talk about the water wars for these systems.

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Benjamin Webster: As you can see the ACF watershed is a trans boundary between Georgia Alabama and Florida, and especially in drought years.

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Benjamin Webster: There, a lot of our disputes and arguments of who has what rights to the waters.

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Benjamin Webster: And another very note, where they think about the system in this red area up here in the headwaters.

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Benjamin Webster: it's a really urbanized large metropolitan area that went through a huge population boom in the 1960s, or so.

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Benjamin Webster: You can see down this figure from the US census data between the 1950s, in the 1970s, we had almost a million people growing in this area in that time.

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Benjamin Webster: So from the 1960s in 1972 there weren't a lot of water regulations minimal regulations at the time phosphates detergents we're still being used in wastewater treatment facilities weren't being as tightly controlled.

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Benjamin Webster: So there was this enormous amount of Bosphorus loading into the chattahoochee river and flowing downstream.

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Benjamin Webster: And this continued all the way until 1988 where we had our longest recorded year on record from these wastewater treatment facilities entering this risen river.

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Benjamin Webster: Until 1993 when it finally came under Regulation and phosphorus concentrations were reduced again so this big question of this 30 years of phosphorus loading, how did it impact the system and honestly where did it go so that's what we're going to be investigating today.

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Benjamin Webster: So what happened at 30 year period of phosphorus loading where did it end up is terminating wasn't able to flow through these downstream reservoirs or were they sequestered kind of a question of.

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Benjamin Webster: How they were moving through the system and what was driving this what was controlling that mechanism there.

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Benjamin Webster: But in addition, are also really curious about nitrogen, phosphorus is really well known to be a primary driver for primary production in these freshwater systems and nitrogen as well, so we looked at six downstream reservoirs along the chattahoochee.

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Benjamin Webster: West point lake lake harding go rock lake like all over West point harding go rick all over you follow and seminal.

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Benjamin Webster: And they're all built in different times of periods, in fact, they were not just going down chain, there are moving up and down Shin go rock being our oldest and West point of like being our youngest system.

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Benjamin Webster: And you might see these super scripts every now and again wp one ha to the super script represents it's ordination going downstream, so one would be the most headwater six the most downstream.

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Benjamin Webster: And we collected all the sediment course from each of these reservoirs we try to do our best to pick a location of according sites that would be the most representative of the core over time.

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Benjamin Webster: because, unlike a natural lake system that goes through these major hydrological changes we tried to call more closely to where the dam was itself but going.

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Benjamin Webster: A little bit of a distance back to be a little bit safer from scoring at the bottom of the reservoir.

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Benjamin Webster: And we measured for nutrients isotopes pigments and heavy metals, so one of the field really fun work to get to do it.

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Benjamin Webster: here's a picture of us of the gravity core just dimension that allows us to go to the bottom of the lake drop the core and collect the two of sediment.

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Benjamin Webster: And if we have more challenging materials we sometimes use Icelandic piston core, you can see that there's actually two metal plates right here.

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Benjamin Webster: And they can collide with each other, acting as a jackhammer to push the poor barrel into the settings.

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Benjamin Webster: and ideally we get a color that looks a lot like this with this light brown color with this dark brown organic top.

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Benjamin Webster: And the separation of color is actually when the construction, the reservoir occurred.

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Benjamin Webster: So this is all collected within less than 100 years of time, so very high segmentation rates and it gives us to known points, the day the rise for was constructed and the day we collected it and using bacon are dating models, who were actually able to also reconstruct.

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Benjamin Webster: The deposition over time we brought it back to the lab cut them into sections put them in the bags analyze them and we're able to develop plots similar to these not this plot itself.

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Benjamin Webster: And you'll notice that the y axis has depth or date that's just a very traditional plot viewing of these settings profiles, so we went on these reservoirs we collected phosphorus analyzed it let's dive on into it.

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Benjamin Webster: So the first one you'll notice is go rock lake in the middle of our reservoir sequence didn't really have much for variation all somewhat hovered around this one milligram per gram.

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Benjamin Webster: Which is not really what we were expecting we were hoping for interesting that 30 year philosophers pulse increasing in the 1960s and decreasing in the 1990s.

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Benjamin Webster: But we didn't really see that at all here a little frustrating okay we'll go upstream a little bit to lake harding and it's it's directly upstream and it's a little bit younger, so it was built after it.

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Benjamin Webster: We have this general decrease over time, but we do see that noteworthy increase in the 1960s all so we can kind of start tracing okay population increased and we send increasing Bosphorus concentrations coming downstream.

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Benjamin Webster: But it was prematurely cut off in the 1970s, or so, which is a little odd, but we can keep coming maybe we'll get more profiles.

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Benjamin Webster: Like seminole hard most downstream reservoir here, and it has a very unique profile compared to the rest of the reservoir systems it doesn't really have this decrease it's showing more change.

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Benjamin Webster: And one very important thing to recognize my lakes seminal it's actually the conjunction for the chattahoochee and the flint meet each other, so this could be a profile that's might be partially.

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Benjamin Webster: dominated and controlled by this other river system coming in, so it kind of makes sense, why would look a little bit more unique compared to the other systems so we're going to go upstream again.

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Benjamin Webster: To like Oliver if you'll notice it goes harding go Oliver they're really close and local proximity.

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Benjamin Webster: And it actually tracks really well with like harding, especially in this region here and even joining go rock lake so if we look at harding and all over together harding was decreasing.

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Benjamin Webster: Has this really know where the increase, and then a subsequent decrease so some event happened here.

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Benjamin Webster: And now it's acting more similarly twits a local reservoir so this could be a local reservoir watershed influence right here so just an unknown event.

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Benjamin Webster: And something that allows it to act more similarly to its other closer proximity reservoirs Okay, we have two more to go.

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Benjamin Webster: Like you follow like you follow is a really frustrating when we first collected it, this was a kind of in the middle of our system, a little bit and it's the only one that just increases over time.

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Benjamin Webster: We weren't sure what was going on, when we first saw we were we questioned our core a little bit.

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Benjamin Webster: let's bring in the last record we're going to talk about why, like you fall and go off like kind of break the mold a little bit.

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Benjamin Webster: And immediately bring in West point lake and makes a lot of sense was pulling like was constructed 1975 so the MID 1970s.

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Benjamin Webster: And it actually comes down in the 1990s so between this increase in phosphorus loading from the increasing population growth and a decrease from the subsequent.

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Benjamin Webster: phosphorus management improvements upstream we encapsulate that 30 year pulse it really didn't make its way as much downstream was completely dominated in these first two systems.

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Benjamin Webster: So also know where the I forgot to point it out to what's been like again is more most upstream system, so the construction of West point like cut off the sediments supply and is now being stored in the most upstream reservoir.

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Benjamin Webster: So why is you follow a different, why is this one, increasing well, everything else is doing different patterns.

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Benjamin Webster: Well, on the local watershed of just this area rounding follow and downstream of the upstream reservoir to it it's actually predominantly a forested.

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Benjamin Webster: watershed with a growing amount of agriculture, over time, so this increases most likely due to this increasing amount of agricultural and urban productivity in the local watershed to it.

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Benjamin Webster: However, go rock link has no changes it shows no variation it's just this moderate concentration throughout time.

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Benjamin Webster: Well that's actually more of a morphological trade of that recipe for.

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Benjamin Webster: Their reality is is a really short water retention time and our water retention time is the amount of time it takes for the resort fill up.

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Benjamin Webster: and lose the water in the system, so the water retention time for go rock lake is point six days so in a single day, you can imagine, go rock lake fills up and empties almost twice.

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Benjamin Webster: They are water is moving so quickly with such high flows there isn't time for sediments a deposit okay.

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Benjamin Webster: So, all this is true, and when West point lake was constructed a cut off the sediment and phosphorus supply coming to like harding we kind of had to prove it.

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Benjamin Webster: So we did that with usgs stream gauge data.

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Benjamin Webster: So there's a couple lines, here we have this orange line, which is a stream gauge of total phosphorus directly upstream from West point lake.

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Benjamin Webster: And we have this light Gray line which is directly downstream of West point lake and this black line indicates the construction, it was pointless.

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Benjamin Webster: So, if we look at the big before construction, we see kind of a higher variability of upstream total phosphorus and a higher magnitude of total downstream.

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Benjamin Webster: Total foster's downstream was point lake and, unfortunately, have a little bit of lack of data that happens with to engage data.

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Benjamin Webster: The construction of was more like occurred, and we have this continued high concentrations upstream that really increased in the 1980s, which was reported as the largest concentration.

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Benjamin Webster: loading of that time and then a subsequent decrease kinda in the night early 1990s 1993.

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Benjamin Webster: Online downstream of West point lake it's really, really, regulated and a lot lower than it was before construction.

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Benjamin Webster: So we can right here, see that the construction of West point lake is regulating how much phosphorus is able to come through this system.

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Benjamin Webster: So let's visualize a little bit differently with some box plots.

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Benjamin Webster: So our phosphorus drivers were being completely dominated by geographic order these reservoirs are in terms of order from upstream and downstream Atlanta would be right here.

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Benjamin Webster: The closest reservoir has the highest concentration decreases going downstream, and we have the secondary component at play of retention time of why we don't see as much variation and go rock lake over time okay.

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Benjamin Webster: Geographic order, we got it, but what about nitrogen.

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Benjamin Webster: Well, we understand that nitrogen and phosphorus have really different pathways and fresh water system nitrogen is able to enter the atmosphere through Dean education.

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Benjamin Webster: and nitrogen fixing bacteria, so it just we were not expecting them to be similar so he ran a PCA with some primary producers involved as well, to try to see how their alternating with each other.

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Benjamin Webster: And I really want you to notice where phosphorus and nitrogen alternate they have almost an 89 degree ordination so practically 90 degrees.

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Benjamin Webster: And this 90 degree ordination or PCA indicates little to no relationship whatsoever was opposite hundred 80 degrees I don't have a negative relationship.

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Benjamin Webster: We see no relationship between these things okay good that's what we're hoping for went back to the literature and we saw that nitrogen does have.

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Benjamin Webster: um was depicted to have a potential relationship with retention time, so we did this, we measured this by doing some correlations between you know reservoir retention time and some organic content.

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Benjamin Webster: i'm so sorry the nitrogen retention time with the mechanism here would be as retention time increases, there is more time for primary producers to grow and deposit into the settlements.

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Benjamin Webster: So we pick three different our groups that would all be different types of primary producers.

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Benjamin Webster: diatoms crypto Mons and cyanobacteria so diagnose anything else anything campus anthem, as our accessory pigments.

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Benjamin Webster: And we ran these correlations between them, and you can see our retention time and the diet is Anthony ALS and the canvas anthem, and then we had these run with.

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Benjamin Webster: correlation and we have very strong correlation between retention time and nitrogen deposition so we had this extremely powerful relationship of how nitrogen was moving throughout the system.

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Benjamin Webster: So, if we look at that again for box plots we have a nitrogen we have phosphorus being driven by geographic order.

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Benjamin Webster: And the nitrogen being driven by retention time as we have an increase of retention time we have this increase in nitrogen deposition so it was kind of a surprise to us when we had these really stark differences in terms of.

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Benjamin Webster: In terms of our industry ratios.

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Benjamin Webster: So it was kind of a mystery to us, we were We saw this general decrease, and he saw this sporadic retention time we were kind of expecting something a little bit more sporadic and we came up with three possible mechanisms that might be causing this.

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Benjamin Webster: Our first one was the presence of hydro verse a lot of high drill is this rooted tuber aquatic invasive species macro fight.

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Benjamin Webster: That it's able to grow very quickly in the systems and the two most downstream systems, like you, following like seminal are completely dominated by this macro fight.

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Benjamin Webster: So potentially the growth of it over time and the rapid degradation might be driving this into P E ratio.

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Benjamin Webster: Another really strong positive relationship we saw with her into P E ratio was a reservoir surface area, the mechanism here is there might be a more available space for like to enter the system.

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Benjamin Webster: which then would allow more primary production and when the loading would exceed degradation rates and have this again hiring up ratio.

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Benjamin Webster: And the last one, we saw a very negative relationship with population densities, to the local up shooting reservoir compared to be integration, so it could be just one of these things, or can be one of the three.

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Benjamin Webster: So thank you so much, special things to Georgia power and Columbus waterworks are funding this project and collection materials and.

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Benjamin Webster: Of course, the auburn pale environmental lab and the Jones dinner each way for funding my continued research of the subject and my co P eyes and my fellow graduate students.

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Steph Shepherd  (she/her): Any questions, thank you Benjamin.

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Steph Shepherd  (she/her): question.

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Kimberly Takagi: And Benjamin Could you clarify really quick the, the last thing that you said was that something about the negative an inverse relationship between nitrogen and.

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Kimberly Takagi: I hope yeah.

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Benjamin Webster: Sorry, the inverse relationship between the population density in the industry ratios.

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Kimberly Takagi: So that as population went.

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Benjamin Webster: As we saw the decrease in our population density, we saw an increase of our industry ratio so as you're moving more and more to these agricultural forced watersheds we saw an increase in our interview loading into the sentiments.

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Kimberly Takagi: All right, yeah sorry that that makes sense.

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Benjamin Webster: Sorry, I stumbled over my little bit at the end there.

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Kimberly Takagi: No, no worries I was looking at the nitrogen and phosphorus and the ultimate river water said so like as you were talking, I was kind of trying to relate it to the work that I had done so absolutely very cool.

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Steph Shepherd  (she/her): looks like Dr Lee has a question Benjamin.

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Ming-kuo Lee: Benjamin enjoy your presentation, you mentioned, you also analyze trace elements.

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Ming-kuo Lee: So do you see any trends, is the correlate to nitrogen or phosphates.

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Benjamin Webster: um so the because it was so many reservoir is it took it such a bulk of time to do some of the analysis there so we're beginning that process of going through some of the other materials.

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Benjamin Webster: For certain heavy metals, we are definitely seeing a correlation of if they lack this atmosphere component and they lack pathways to move through the system, it seems to somewhat follow.

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Benjamin Webster: phosphorus at the presence of a reservoir seems to immediately capture materials lake harding, which is the second reservoir in the sequence has some.

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Benjamin Webster: can't remember which element is off top my head, right now, but it has an elevated level of a nutrient concentration of one of the heavy metals and we believe it might be from the key a plant that's in its particular watershed.

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Benjamin Webster: So, in terms of these trends movement of the reservoir system, it seems to follow phosphorus in terms of whatever the media reservoir downstream of the sources is where it's primarily deposited.

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Ming-kuo Lee: Thank you.

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Steph Shepherd  (she/her): Another question.

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Ann Ojeda: i've been, this is an Ada I don't the end of this but it sounded great so congratulations, but I want to tag on to what Dr Lee said ask about anthropogenic sources of all of these.

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Ann Ojeda: Any heavy metals, that you try to correlate so are you doing, are you thinking about land use analysis or you know, specifically with our coal fly ash empowerment and impacts that those could have on concentrations and confounders for any relationships within NP.

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Benjamin Webster: Oh, as in term, just to clarify the question just the presence of some of these altering some of our nutrient ratios.

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Ann Ojeda: and not so much nutrients, but the heavy metal inputs.

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Benjamin Webster: Okay.

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Right so.

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No.

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Ann Ojeda: That was it.

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Benjamin Webster: Oh um.

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Benjamin Webster: that's a really good question I.

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Benjamin Webster: The systems are so young and reservoirs have such a dynamic deposition period there's a lot of argument about the reservoir filling time.

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Benjamin Webster: Of where there's increased segmentation loading in the early years.

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Benjamin Webster: I, we are very interested in that it's a little early for me to say anything very strongly on that, but it is something we're looking into and curious about.

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Steph Shepherd  (she/her): Oh thanks okay.

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Ann Ojeda: catch up on that later yes.

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Steph Shepherd  (she/her): So we are now ready getting ready for the next presentation chloe are you ready to share your screen.

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Steph Shepherd  (she/her): You gotta unmute to.

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Steph Shepherd  (she/her): Yes, okay so as you do, that I will introduce you.

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Steph Shepherd  (she/her): This is chloe eggert she is talking about the ag cultural influence on biogeochemical storage in geographically isolated wetlands and the dirty plane right.

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Chloe Eggert: Thank you and good morning everyone, my name is chloe eggert i'm a first year masters student at auburn university and the Johnson or an Hoa.

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Chloe Eggert: And my co authors on this are Dr Matthew waters of auburn and Dr Steve holiday of the Jones Center so I just wanted to first get started talking about what exactly a geographically isolated wetland is So these are wetlands that are surrounded.

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Chloe Eggert: By uplands all around and they lack a high geologic connection to nearby waterways, therefore, they are not protected under legal protections from the clean water act and they're not considered a water of the United States, despite their.

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Chloe Eggert: Lack of legal protections they do still provide many beneficial ecosystem services to the surrounding areas such as flood mitigation nutrient storage and they host a variety of biodiversity, especially migratory birds.

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Chloe Eggert: So here is just a heat map of area density of geographically isolated wetlands throughout the US and here's our study site in the dirty plane.

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Chloe Eggert: And so the dirty plan has upwards of 11,000 geographically isolated wetlands and they're mostly small and they vary in wetland type, as well as hydrology vegetation and soil.

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Chloe Eggert: And this area is actually has karst geology, as you can see, in.

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Chloe Eggert: This image here.

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Chloe Eggert: The dirty plane is right in this section and our study site is at the genome Center which is highlighted in red and the surrounding agriculture fields.

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Chloe Eggert: So this area has to mainland types that we're going to be looking at one is for us predominantly along with pine forest with wire grass understory.

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Chloe Eggert: and low temperature prescribed burning is a typical forest management technique used to control the growth of the understory and promote the growth of long with pine trees, the other land use that we're looking at is agriculture, because of the presence of.

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Chloe Eggert: Groundwater for irrigation there's a great amount of agriculture and crops being grown in this area, such as peanuts cotton soybeans and other crops.

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Chloe Eggert: Now this project is a big project funded by the usda it has multiple P eyes and multiple.

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Chloe Eggert: partners.

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Chloe Eggert: And it has many different goals, we want to understand how geographically isolated wetlands function in an agriculture landscape, by looking at the original dynamics nutrient storage, also the hydrology and run off the auburn engineering team is using high geologic models to better understand.

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Chloe Eggert: The system, as well as looking at land use the difference between agriculture and a forecasted.

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Chloe Eggert: surrounding area how that influences the geographically isolated wetlands and another big thing is.

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Chloe Eggert: At the end of this project, we want to promote conservation and restoration of these weapons that provide many beneficial ecosystem service to to the area and also outreach so we want to involve various stakeholders and educate farmers on the benefits of their wetlands on their properties.

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Chloe Eggert: So my part of the project is looking more at the runoff and the settlement mechanisms of sediment entering these wetlands, so you have your organic carbon nitrogen and phosphorus nutrients in your.

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Chloe Eggert: Water shed that will eventually make their way to these wetlands um, but we want to understand what is being transported and deposited in the systems and what nutrients are being stored long term in the settlement.

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Chloe Eggert: Another main goal of this part of the project is understanding, where the sediments eventually deposit.

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Chloe Eggert: Do they deposit all the way in the Center of the wetland or do they get stopped and only deposit around the edges and one thing we're looking at is the effect that vegetation has on these.

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Chloe Eggert: sediment transports were forcing reference wetlands we've seen have more vegetation that may be impeding the transport of these segments where we've seen that agriculture wetlands are more characteristic of an open body system that may allow for further transport.

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Chloe Eggert: Of the sediments.

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Chloe Eggert: So with That being said, the objectives of my part of the project are to investigate the biology chemical impacts of agriculture run off on geographically isolated wetlands and we're doing that you can pour oil storage.

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Chloe Eggert: Using Paleo women illogical techniques to see through time how the sediments are being stored and we also want to look at where they're being stored the spatial distribution and then the third goal is comparing.

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Chloe Eggert: The two systems so comparing how these mechanisms work in an agriculture landscape versus a forest landscape.

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Chloe Eggert: So to do that, we had to put our readers on and get in the wetlands.

476
01:03:15.350 --> 01:03:24.650
Chloe Eggert: We collected sediment corps both long term spit sudden marine corps which are longer and spatial sediment course which were actually just the top eight centimeters.

477
01:03:24.950 --> 01:03:29.870
Chloe Eggert: in various locations and resection those in the field at one to two centimeters sections.

478
01:03:30.710 --> 01:03:39.650
Chloe Eggert: Then we brought it back to the lab we did percent organic manner book density elemental analysis, we will be doing stable isotopes.

479
01:03:39.980 --> 01:03:53.540
Chloe Eggert: And will will be dating our core i'm using lead to 10 as well as running photosynthetic pigments as a proxy for primary producers throughout time and will also be looking at charcoal.

480
01:03:55.040 --> 01:03:56.660
Chloe Eggert: So this is one of our sites.

481
01:03:57.710 --> 01:04:10.940
Chloe Eggert: it's our reference wetland it's forested around the wetland and it has quite a bit of vegetation and this was our 50 centimeter core that we collected, as you can see it's slightly darker.

482
01:04:12.380 --> 01:04:21.410
Chloe Eggert: And then, this is our other site our agriculture wetland that you can see in this map is right in the middle of a pivot ag field.

483
01:04:21.890 --> 01:04:31.820
Chloe Eggert: And you see in this picture here on the Left that small patch of dark vegetation is actually the wetland in the middle of the field.

484
01:04:32.300 --> 01:04:38.930
Chloe Eggert: And then I just want to draw attention to the bottom of the core where you see this shift from lighter.

485
01:04:39.380 --> 01:04:51.860
Chloe Eggert: sand and klay to a shift of dark organic matter is what we think so i'll be talking about that a bit more um, so I am in the preliminary stages of this project.

486
01:04:52.640 --> 01:05:07.730
Chloe Eggert: there's still a lot of fields work to be done and lab work, so I just wanted to share a few of my early on findings with you, but there is still a lot to be done so, so far, this is percent organic matter with.

487
01:05:09.080 --> 01:05:29.990
Chloe Eggert: depth on the left and percent on the top and, as you can see in the reference wetland the top of the core has quite a bit of organic matter, and then a quickly you see rapid degradation of that organic matter down core after 10 centimeters.

488
01:05:31.190 --> 01:05:50.480
Chloe Eggert: Conversely, in the agriculture wetland you see a more consistently higher percent organic matter down core, then you see a spike and we think that's indicative of the change in coloration of the core from lighter color brown to the dark rich Brown.

489
01:05:52.220 --> 01:06:01.130
Chloe Eggert: Moving on to our percent organic matter percent organic carbon and we see a similar trend with reference wetlands pretty consistent.

490
01:06:01.910 --> 01:06:15.110
Chloe Eggert: With the percent organic matter to the carbon where it's higher at the top and then rapid degradation and and as for the ag wetland another similar trend about 2%.

491
01:06:15.590 --> 01:06:39.200
Chloe Eggert: Organic carbon down core and then a spike at the bottom nitrogen follows a similar trend, and we also see slightly low percent nitrogen in ag wetland and one thought we have is that possibly the presence of irrigation pretty consistent irrigation could allow for consistent.

492
01:06:40.850 --> 01:06:50.360
Chloe Eggert: water levels that could allow for dinner education pretty consistently, which would us produce nitrogen levels um so that's something we're going to look into.

493
01:06:50.930 --> 01:07:04.070
Chloe Eggert: um and also here our carbon nitrogen ratios, which could tell us the source of these nutrients whether it's low you'll have more of an algae dominated system medium levels to maybe 10 to 15 you'd have.

494
01:07:05.570 --> 01:07:10.220
Chloe Eggert: More macro fights in the system and much higher you'd have more of a terrestrial signal.

495
01:07:11.240 --> 01:07:30.110
Chloe Eggert: But right now it's a little hard for us to decipher anything that is why will be running our carbon nitrogen stable isotopes to really understand where these nutrients are coming from especially looking at maybe the difference in signatures, we may see from.

496
01:07:31.130 --> 01:07:34.160
Chloe Eggert: Agriculture wetlands to reference wetlands as.

497
01:07:35.240 --> 01:07:37.340
Chloe Eggert: nutrients and fertilizers.

498
01:07:38.600 --> 01:07:41.810
Chloe Eggert: may appear to be different.

499
01:07:43.610 --> 01:07:56.480
Chloe Eggert: And so, moving on to phosphorus we also see a similar trend with the reference wetlands very consistent and phosphorus is a little interesting because it's actually much higher.

500
01:07:58.070 --> 01:08:13.700
Chloe Eggert: The reference level and does not even make it 2.1 where the agriculture wetland is pretty consistent between point five, and point one and we see the spike at 20 and we've seen a little bit of a spike in other.

501
01:08:14.960 --> 01:08:23.240
Chloe Eggert: parameters and we might suggest that that could have something to do with the input implementation and the installation.

502
01:08:23.690 --> 01:08:44.630
Chloe Eggert: Of pivot agriculture in the late 70s, maybe early 80s um so we would love to see if we could identify that and the change that they might that the high geologic change that that may have incurred on the sedimentation rates and sediment transport into the system.

503
01:08:46.010 --> 01:09:05.930
Chloe Eggert: And as for our nitrogen, phosphorus ratio This tells us what nutrients and fertilizers, are entering the system we again see that spike at the bottom for the agriculture wetland that what we might think is an ecosystem shift from when.

504
01:09:06.980 --> 01:09:11.150
Chloe Eggert: The wetland could have been surrounded by forested system forested.

505
01:09:12.350 --> 01:09:33.080
Chloe Eggert: land use and then switch to farming, so we see that it's around 12 and entropy ratio of 12 now the reference wetland, which is currently forested is pretty consistent at 12 so this may be an indication that the agriculture wetland.

506
01:09:34.310 --> 01:09:41.660
Chloe Eggert: Between 50 and 60 centimeters was predominantly a force it what are surrounded by forest.

507
01:09:43.040 --> 01:10:02.540
Chloe Eggert: So we think that isotopes will help us unveil that, as well as our dating, which is one of the last things we do will help us understand kind of a time period of when that may have incurred and we will try and connect back to um.

508
01:10:03.710 --> 01:10:07.790
Chloe Eggert: Historical records of land use and land ownership.

509
01:10:09.140 --> 01:10:21.440
Chloe Eggert: And as for the spatial distribution we're still in the preliminary stages of this as well, but so far i've shown that percent organic matter distribution from these top course I collected.

510
01:10:22.190 --> 01:10:30.080
Chloe Eggert: This top image is the reference Westland surrounded by force, and we see it's more of a heterogeneous distribution with.

511
01:10:30.620 --> 01:10:38.420
Chloe Eggert: Red being higher and yellow being lower we can kind of see that there's a little bit more higher organic matter in the Center.

512
01:10:39.050 --> 01:10:47.300
Chloe Eggert: And then the bottom is agriculture wetland where it's more homogenous everything was consistently low and organic matter.

513
01:10:47.690 --> 01:10:58.430
Chloe Eggert: And we'll be adding some more points, but we may attribute that to vegetation or consistent mixing and we still have a lot more work to do with that, but we are curious what we will find.

514
01:10:59.840 --> 01:11:15.110
Chloe Eggert: As for different effects and mechanisms at play in these systems, we do think fire has a role in this story, because the reference wetlands are burned on a two to three year basis, we think that that would promote.

515
01:11:15.740 --> 01:11:28.850
Chloe Eggert: phosphorus loading, because it cannot enter exit a system once it's entered, we also think hydrology has a big play in these nutrient mechanisms, because wedding and drying can alter.

516
01:11:30.110 --> 01:11:34.460
Chloe Eggert: The storage geometry of the system, as well as how do.

517
01:11:36.200 --> 01:11:48.770
Chloe Eggert: How do things like pivot ag and different anthropogenic hydraulic changes affect the system we think vegetation may play a role in the sediment transport.

518
01:11:49.250 --> 01:11:58.130
Chloe Eggert: And the spatial distribution, but also how does an algae bloom affect the system and the buyer.

519
01:11:58.640 --> 01:12:07.130
Chloe Eggert: geochemistry of it and we're interested to see if we can identify one fertilizers applied and what kind and how different farming.

520
01:12:08.120 --> 01:12:19.760
Chloe Eggert: techniques may alter these wetlands um so so far we've seen that reference ones are more consistent in their strategic Duffy wedding and drying maybe at play.

521
01:12:20.060 --> 01:12:31.460
Chloe Eggert: With the story geometry and as well as fertilizers, and we also think that agriculture wetlands might have a more homogenous spatial sentiment distribution throughout their systems.

522
01:12:32.090 --> 01:12:44.120
Chloe Eggert: But we still have quite a bit of work to do we want to run isotopes elements on charcoal we're going to date, our cores run pigments and look at different land use, as well as one very statistical analysis.

523
01:12:44.540 --> 01:12:59.510
Chloe Eggert: And I just showed you to have the sites and the work from that, but we have various other sites this picture on the right is a image of another reference wetland and we have various other ag wetlands to compare.

524
01:13:00.560 --> 01:13:08.240
Chloe Eggert: them all to to have a broader sense of the mechanisms that play in the door to playing the agriculture.

525
01:13:09.050 --> 01:13:22.400
Chloe Eggert: influence that they have on the geographically isolated ones um so with that I just like to say thank you to my usda project team, and my lab and if you have any questions i'd love to hear them.

526
01:13:26.630 --> 01:13:30.410
Steph Shepherd  (she/her): Thank you chloe see I can uncover new if I.

527
01:13:32.840 --> 01:13:37.640
Steph Shepherd  (she/her): spotlighted you there I have good there we go remove this fall I do we have questions for chloe.

528
01:13:43.640 --> 01:13:51.320
Steph Shepherd  (she/her): Being and I i'm going to go ahead, because I am a little bit familiar with your sites, because I have a student that's going to start working on some of them soon.

529
01:13:52.640 --> 01:14:07.220
Steph Shepherd  (she/her): And so my question is about the history of the agricultural sites, do you guys have a good handle on the history in terms of like how long they've been irrigated with pivot irrigation, or what types of how long certain properties have been.

530
01:14:07.940 --> 01:14:15.530
Chloe Eggert: yeah so that is a part, a component of this project and we haven't delved too far into it, but we are.

531
01:14:16.730 --> 01:14:18.350
Chloe Eggert: We do know that.

532
01:14:19.490 --> 01:14:29.840
Chloe Eggert: it's likely that these sites have been farmed for over 100 years and the time of the pivot ag is around the 80s, for this.

533
01:14:30.980 --> 01:14:41.300
Chloe Eggert: This region, there has also i've been talking with some people at the Jones Center and understand that one of actually one of the sites.

534
01:14:41.840 --> 01:14:52.970
Chloe Eggert: The site, I presented on used to be owned by Barry college and i'm pretty sure they had that as a working farm and news does research.

535
01:14:53.480 --> 01:14:58.820
Chloe Eggert: um so that gives us a good link into who to contact to find out more details.

536
01:14:59.330 --> 01:15:16.400
Chloe Eggert: Throughout time and kind of look at different ownerships it's a little hard, because this farm is actually just sold, so we don't have quite a month quite as much history through that landowner but we'll be able to track that which is going to be interesting to see.

537
01:15:16.460 --> 01:15:18.920
Steph Shepherd  (she/her): Great looks like you have some hands raised here.

538
01:15:19.610 --> 01:15:21.110
Chloe Eggert: Okay, secondly.

539
01:15:22.160 --> 01:15:26.420
Steph Shepherd  (she/her): are no he may leave did you have your hand raised from previous he may be.

540
01:15:29.750 --> 01:15:30.140
Chloe Eggert: Okay.

541
01:15:30.410 --> 01:15:31.100
Steph Shepherd  (she/her): let's move on.

542
01:15:34.340 --> 01:15:36.110
Steph Shepherd  (she/her): looks like Donald siegel.

543
01:15:36.140 --> 01:15:36.710
Donald Siegel: Can I can I.

544
01:15:36.770 --> 01:15:38.630
Donald Siegel: Can I just speak because that's the way it works yeah.

545
01:15:38.720 --> 01:15:39.380
Okay.

546
01:15:40.640 --> 01:15:48.470
Donald Siegel: Nice job I i've worked with some isolated wetlands, but in a different region up for the prairie pothole region in North Dakota.

547
01:15:49.970 --> 01:15:58.160
Donald Siegel: But there we we were looking at nutrients in a variety of things, but you also had done some hydrological instrumentation you know they get a sense of whether.

548
01:15:58.940 --> 01:16:08.090
Donald Siegel: These wetlands were leaking out to groundwater or receiving groundwater and in a sense, how Islay that we're in but kind of nutrients and other things were being delivered.

549
01:16:08.750 --> 01:16:17.030
Donald Siegel: Through groundwater, have you thought about doing any of this kind of stuff with little mini pedometers sucking out some water, you know that kind of thing.

550
01:16:17.480 --> 01:16:30.530
Chloe Eggert: yeah I have seen in the literature, a lot about shao geographically isolated isolated wetlands aren't that isolated, especially with groundwater and especially in this region where grandmother is present.

551
01:16:31.040 --> 01:16:42.350
Chloe Eggert: i'm the other half of the team or the hydrology team is looking definitely a lot of the geologic mechanisms, I know they have level loggers.

552
01:16:43.460 --> 01:17:03.140
Chloe Eggert: And are testing the water level but yeah I would be very interested to see how groundwater does play into the systems and that that connection, because that does have big implications for connectivity with the systems if they are high geologically connected through groundwater.

553
01:17:04.250 --> 01:17:18.560
Chloe Eggert: So I think that might be in play, and to do that would be very beneficial and that's a little bit more, the other half of the team, but I know they have been doing a lot.

554
01:17:19.880 --> 01:17:20.690
Donald Siegel: Okay, thank you.

555
01:17:22.370 --> 01:17:33.470
Steph Shepherd  (she/her): yeah I think that's one of the coolest things about this project that you're on is that there are students from different like you're from crop soil environmental science we there's Coleman Barry from engineering.

556
01:17:33.830 --> 01:17:45.800
Steph Shepherd  (she/her): My student now is about to get started on the forest food watersheds there's there's a lot going on, and I think all that data will be exceptionally complimentary and give us some great information it's going to take a little while.

557
01:17:46.130 --> 01:17:47.120
Chloe Eggert: yeah it's a big trend.

558
01:17:47.480 --> 01:17:50.330
Steph Shepherd  (she/her): got to take the data get the data and start looking at it yeah.

559
01:17:51.350 --> 01:17:52.370
Steph Shepherd  (she/her): Other questions.

560
01:18:00.020 --> 01:18:02.060
Steph Shepherd  (she/her): And let me make sure I don't see anything in the chat.

561
01:18:06.680 --> 01:18:09.500
Steph Shepherd  (she/her): Well, thank you very, very much chloe that was excellent.

562
01:18:10.760 --> 01:18:15.800
Steph Shepherd  (she/her): it's it's really exciting to to see some of the work that you guys are doing.

563
01:18:17.420 --> 01:18:25.400
Steph Shepherd  (she/her): We, as I, as I mentioned before, that our next presentation is not happening so we, and we have a break, scheduled for 925.

564
01:18:25.880 --> 01:18:37.610
Steph Shepherd  (she/her): So if there's anyone who wants to hang out for a bit and just have a conversation or ask questions about this work or work that benjamin's he's still here i'll put them on the spot the benjamins working on.

565
01:18:39.530 --> 01:18:41.870
Steph Shepherd  (she/her): Or it looks like Christmas still around as well.

566
01:18:43.580 --> 01:18:46.160
Steph Shepherd  (she/her): guys are welcome to stick around for a little bit.

567
01:18:47.660 --> 01:19:01.280
Steph Shepherd  (she/her): it's up to you, otherwise we have quite a nice break and then our next formal presentation is at 940 and it's titled unveiling occur responses to glaciations using residential water well data.

568
01:19:15.500 --> 01:19:17.120
Steph Shepherd  (she/her): sharing my screen.

569
01:19:25.400 --> 01:19:32.090
Steph Shepherd  (she/her): Okay, well, it seems like everyone's pretty quiet, I do know it it's hard to get a conversation going on zoom.

570
01:19:46.850 --> 01:19:50.150
Steph Shepherd  (she/her): hey kristin are you actually still around, I do have a.

571
01:19:52.250 --> 01:19:54.890
Steph Shepherd  (she/her): Another question about green size for you, if you're here.

572
01:19:55.250 --> 01:20:01.940
Steph Shepherd  (she/her): yeah yeah so are you guys running the grain size in a lab at the usgs are you sending them out to someone else.

573
01:20:02.330 --> 01:20:08.150
Kristen Steele: yeah so the green, this is actually a really interesting story so typically in my lab we do.

574
01:20:09.350 --> 01:20:10.550
Kristen Steele: A late eight cores.

575
01:20:10.730 --> 01:20:15.380
Kristen Steele: So, initially, we just do the typical civil procedure with 32 and 63 months.

576
01:20:15.410 --> 01:20:21.740
Kristen Steele: yeah, and so I started doing that, on this like Jackson core, but it is just so fine grained I didn't show it here but.

577
01:20:22.400 --> 01:20:23.450
Kristen Steele: I still have maybe.

578
01:20:24.530 --> 01:20:36.770
Kristen Steele: 50 centimeters of the core and the average grade size was around like 20 microns so obviously classical something's not going to work for us, we do have a master sizer.

579
01:20:37.760 --> 01:20:38.210
Steph Shepherd  (she/her): I have.

580
01:20:38.810 --> 01:20:51.740
Kristen Steele: yeah i'm in rested but, unfortunately, no one has really had the time to really sit down and develop a protocol and figure out how this thing works so it's not really.

581
01:20:52.370 --> 01:20:57.140
Steph Shepherd  (she/her): i'm saying Benjamin laugh because he's the one who did that, for me, oh i'm sure it's just.

582
01:20:58.400 --> 01:20:59.900
Kristen Steele: not sure what i'm.

583
01:21:00.650 --> 01:21:06.110
Kristen Steele: Who the manufacturer is but we even sent to my colleagues to Chicago when take a training.

584
01:21:06.170 --> 01:21:06.620
Steph Shepherd  (she/her): That yet.

585
01:21:07.100 --> 01:21:11.930
Kristen Steele: to figure out and they they said they went in it was mostly geared towards like pharmaceutical company.

586
01:21:11.960 --> 01:21:14.900
Kristen Steele: Yes, they were like what like what's it what's.

587
01:21:15.230 --> 01:21:16.580
Kristen Steele: It like let's say you know.

588
01:21:16.610 --> 01:21:25.340
Steph Shepherd  (she/her): that's the same problem we have the manufacturers is very much not in tune with the geologic applications.

589
01:21:25.640 --> 01:21:33.260
Kristen Steele: yeah yeah, especially because you know we have my staff, we also have some colleagues who work in wetlands, we have peace corps, we have.

590
01:21:34.310 --> 01:21:47.210
Kristen Steele: So from the Everglades it has a lot of like mollusks shells and things like that, so we have a wide variety of things that we would want to measure but yeah that's what it sounds like you know we will spend a lot of time and money to go to Chicago for.

591
01:21:47.600 --> 01:21:48.140
Low.

592
01:21:49.400 --> 01:21:51.470
Kristen Steele: but thankfully i'm so.

593
01:21:51.500 --> 01:21:57.080
Kristen Steele: James Madison university and i'm in rest in the sun is about two hours away and.

594
01:21:57.080 --> 01:21:57.440
Steph Shepherd  (she/her): i'm.

595
01:21:57.560 --> 01:21:59.720
Kristen Steele: Like to Jane you have for undergrad.

596
01:22:00.020 --> 01:22:10.430
Kristen Steele: Oh cool they have a beckman coulter things is in wiser and we were they were they were kind enough to let us use that in the meantime.

597
01:22:10.640 --> 01:22:18.830
Kristen Steele: Great yeah, and so I have I had I was the one who ran all of our symbols on the beckman coulter in it, it worked really well for me.

598
01:22:19.100 --> 01:22:31.310
Steph Shepherd  (she/her): So so and Benjamin knows this so well so he's probably gonna laugh at me, but my biggest frustration is is that most of the way I was trained, because I was looking at river settlements and the Mississippi river valley and Grad school, the first time and then.

599
01:22:31.820 --> 01:22:39.470
Steph Shepherd  (she/her): I didn't do I did cobbles and so you're not going to do, calls and gravel and green size analysis that way in my PhD but.

600
01:22:40.340 --> 01:22:51.080
Steph Shepherd  (she/her): But I did all the traditional soil methods and so when I got to auburn I was like I want to get one and i'd played with a laser green size analysis machine and we're at a job I had for one year at Franklin and Marshall.

601
01:22:51.560 --> 01:22:58.670
Steph Shepherd  (she/her): college and, by the way, i'm familiar with jam you, because when i'm I went to university of Arkansas and and and when I went to field camp.

602
01:22:59.000 --> 01:23:07.430
Steph Shepherd  (she/her): We had a lot of jam you students at our field camp for some reason that year, like three or four something you know super unusual, but um.

603
01:23:07.940 --> 01:23:14.990
Steph Shepherd  (she/her): But yeah so we so i've been using this in my lab now for six years and somewhere I got really frustrated because one it doesn't do.

604
01:23:15.380 --> 01:23:27.200
Steph Shepherd  (she/her): sand size, they say, it does, it does not it only does really fine grain materials, which is why it seems suitable for your work, but then you know the results that are produced are percent by volume.

605
01:23:28.250 --> 01:23:38.420
Steph Shepherd  (she/her): and other methods are percent by weight, and so I get this is kind of where I was, I was wondering what other records, you might be looking at, because what I get super frustrated with is that if you're looking at percent by volume.

606
01:23:38.960 --> 01:23:45.530
Steph Shepherd  (she/her): And you're then try and compare to settlement run in a different lab at percent by weight it's not a direct comparison.

607
01:23:45.770 --> 01:23:49.730
Steph Shepherd  (she/her): Right, I want to be able to compare data I know yeah, it is very.

608
01:23:49.850 --> 01:23:54.770
Kristen Steele: tricky as i'm sure, as you mentioned veterans Are you familiar with the whole thing, and you have to really pick.

609
01:23:56.090 --> 01:23:59.870
Kristen Steele: You know, a special technique, depending on the material that you're working with.

610
01:24:00.140 --> 01:24:08.930
Kristen Steele: yeah and it wasn't I mean, I know that some people are kind of like grain size purists and that it's kind of like saving we.

611
01:24:09.410 --> 01:24:24.770
Kristen Steele: which you know I can understand you know why some people have some reservations in terms of laser protocol analysis but yeah so it raises those questions, but then it's also you know there's deceiving isn't the most advanced technique I.

612
01:24:25.430 --> 01:24:29.390
Steph Shepherd  (she/her): know and there is there is, you know definitely.

613
01:24:30.530 --> 01:24:38.660
Steph Shepherd  (she/her): I will I won't call it slop but if there is some change in data based on who's running it because we all have kind of our little tricks and.

614
01:24:39.050 --> 01:24:50.870
Steph Shepherd  (she/her): ways that we do it with with the machine like what I find very useful about them gene is when we are dealing with super fine grain material or like when i'm working with engineering students who are only interested in comparing grain size within their system.

615
01:24:51.200 --> 01:25:03.140
Steph Shepherd  (she/her): You can just run so many more samples so much more quickly, that they can do a larger comparison my, so it is very useful I just get frustrated at the thinking about how to compare it outwards.

616
01:25:04.730 --> 01:25:06.050
Kristen Steele: I agree, and that's a good point with.

617
01:25:06.050 --> 01:25:07.340
Kristen Steele: them depending on.

618
01:25:07.370 --> 01:25:20.810
Kristen Steele: who's running it because I ran all these samples for this project, but I actually found about halfway through I guess the instrument that I was working on had you could set the certain pump speed, which is, I guess.

619
01:25:21.500 --> 01:25:28.730
Kristen Steele: Yes, it comes the stuff into the you know your sample into the film analysis Chamber whatever it's called.

620
01:25:29.810 --> 01:25:32.780
Kristen Steele: And I had noticed a discrepancy, where I had ran like.

621
01:25:34.100 --> 01:25:45.110
Kristen Steele: one set of samples at a 30 pump speed and I ran at 14 so obviously I you know we weren't sure how that would really affect the data, but it needs to be consistent, because.

622
01:25:45.110 --> 01:25:51.770
Kristen Steele: they're in effect there, so I had to I think it was like 90 centimeters worth, of course, that I had to redo.

623
01:25:52.010 --> 01:25:53.420
Kristen Steele: Oh man yeah.

624
01:25:53.840 --> 01:26:04.430
Steph Shepherd  (she/her): No Okay, so I use is because of the type of grains as I do, I often do settling tubes as well, and so, if you have to rerun settling to data, you know that's days, sometimes.

625
01:26:04.460 --> 01:26:04.910
Days.

626
01:26:06.590 --> 01:26:14.270
Steph Shepherd  (she/her): So I do love the laser green plus analysis, to the fact that again super fast once you get the samples ready to go in it's super fast.

627
01:26:15.470 --> 01:26:20.450
Kristen Steele: And I felt like a robot so just like set up one sample, then I go to the next, that i'd prepped another one.

628
01:26:22.370 --> 01:26:22.640
Steph Shepherd  (she/her): yeah.

629
01:26:22.790 --> 01:26:27.350
Kristen Steele: yeah but thankfully, we were able to get it figured out it's not our intention in my.

630
01:26:27.350 --> 01:26:30.560
Kristen Steele: lab you know, like we do setting.

631
01:26:30.590 --> 01:26:43.130
Kristen Steele: But it's not our intention to go forward with bigger in size analysis, I really would like that would be interesting if we could get our master sizer up and running, I wish I knew who it was made by maybe it's the same one that you all have.

632
01:26:43.370 --> 01:26:45.830
Steph Shepherd  (she/her): All the master sizes are made by malvern all of them.

633
01:26:47.030 --> 01:26:47.360
Steph Shepherd  (she/her): And they.

634
01:26:48.800 --> 01:26:59.450
Steph Shepherd  (she/her): used to get tired of me calling me like what about and then they're like do you want the system service i'm like no we got it here at this point, I think we know more about what we need them, they do yeah.

635
01:27:00.050 --> 01:27:00.650
Kristen Steele: that's fair.

636
01:27:00.740 --> 01:27:08.030
Kristen Steele: yeah actually I was at jm you when they so they completely moved the entire department basically moved.

637
01:27:09.230 --> 01:27:16.220
Kristen Steele: And, and they moved one summer, when I was there running samples so like you know they didn't want to just pick up that the master the.

638
01:27:16.280 --> 01:27:19.490
Kristen Steele: analyzer just moving out to gail pleasers and.

639
01:27:19.550 --> 01:27:22.100
Steph Shepherd  (she/her): yeah that'd be careful with it just a with it.

640
01:27:22.520 --> 01:27:33.470
Kristen Steele: They had to like hire the beckman coulter people to come move it for them, and I think is the same sort of thing where they just like don't entirely understand what the what the uses for the instrument there but.

641
01:27:34.460 --> 01:27:40.220
Kristen Steele: You know they had to they had to move because they were like we don't want to you know get anything off kilter here.

642
01:27:40.520 --> 01:27:47.330
Steph Shepherd  (she/her): I had a student recently tell me that, and this has been happened more than once, but recently it came up again like that kind of looks like a souped up.

643
01:27:48.410 --> 01:27:55.430
Steph Shepherd  (she/her): Coffee machine like a fancy no sediment you don't want to drink any of the liquid coming out of that.

644
01:27:56.180 --> 01:27:58.880
Kristen Steele: Like the noise that needs to be that.

645
01:28:00.950 --> 01:28:01.460
Steph Shepherd  (she/her): We also.

646
01:28:01.520 --> 01:28:04.310
Kristen Steele: Have I don't know if you all have the same thing we have a sonic hater will not.

647
01:28:04.340 --> 01:28:04.670
Steph Shepherd  (she/her): yeah.

648
01:28:05.030 --> 01:28:06.500
Kristen Steele: He has a sonic hater attached as well.

649
01:28:06.770 --> 01:28:08.720
Kristen Steele: yeah That was a little.

650
01:28:08.810 --> 01:28:12.950
Steph Shepherd  (she/her): annoying makes squeaky squeaky squeaky noises benjamin's nodding like.

651
01:28:14.510 --> 01:28:27.830
Benjamin Webster: It was such a pain trying to determine what velocity to have the flows and how long you're gonna sonic aid and if you're going to do pulse, on occasions, or just maintained and ya know I feel you on that one.

652
01:28:28.040 --> 01:28:34.070
Kristen Steele: yeah typically with what you all run in your life, do you have do you have to deal with organic settlers it typically just.

653
01:28:34.430 --> 01:28:39.140
Steph Shepherd  (she/her): mineral matter most Benjamin worries about organics I do not.

654
01:28:40.550 --> 01:28:49.250
Steph Shepherd  (she/her): I probably should actually with my current student that starting on the project that's tied to chloe's work i'm gonna have to start worrying about organics.

655
01:28:50.120 --> 01:28:50.690
Steph Shepherd  (she/her): hey Lisa.

656
01:28:51.020 --> 01:29:03.440
Lisa Davis: hi i've been listening like a quickie with my camera off, but I had a question about organics we have an ongoing debate in my lab and we have a better sizer instrument, we also have a malvern here.

657
01:29:03.920 --> 01:29:16.760
Lisa Davis: In the geology department and but i've kind of transitioned to better sizer because the customer support they're super awesome they used to be seamless and so anyways so.

658
01:29:17.690 --> 01:29:30.080
Lisa Davis: But the better size or instrument, you know, has two separate set of optics that allow the finer range, as well as the larger range of harmless as distributions to be very finely.

659
01:29:30.410 --> 01:29:37.160
Lisa Davis: discern and so it's Nice, you can set it to like you know we do like a test batch to kind of discern you know where we're at.

660
01:29:37.580 --> 01:29:43.490
Lisa Davis: In terms of particle size ranges and then we set the optics to what works best for that particular set of samples.

661
01:29:43.760 --> 01:29:54.230
Lisa Davis: So it's Nice that it allows that but my real question is, do you burn off your organics before you do particle size analysis or do you consider them part of the settlement load.

662
01:29:55.370 --> 01:29:57.890
Lisa Davis: and part of the particle size story.

663
01:29:58.970 --> 01:30:00.680
Lisa Davis: i've heard arguments in both directions.

664
01:30:02.090 --> 01:30:04.940
Kristen Steele: Right, you have a benefit, do you want to answer for yours.

665
01:30:05.030 --> 01:30:20.600
Benjamin Webster: uh well so moving forward from our work, one thing we're really interested in is looking at particle size, in conjunction with the drought cycles because of obviously number lake levels, especially linear particle size, transport, the higher flows.

666
01:30:22.910 --> 01:30:36.470
Benjamin Webster: And so we've been thinking about this question a lot in our lab because a lot of our resident corps of average about 15% organic content and so it's yeah it's just going to be significant in all of them, luckily it's consistent between all of them also.

667
01:30:38.360 --> 01:30:45.740
Benjamin Webster: And we go back and forth on that pop for for two main reasons one because a lot of organic content we believe is this.

668
01:30:47.060 --> 01:30:54.680
Benjamin Webster: All talk fitness in grown primary producers and we're like well you know these are the sediments that's growing here it's depositing here.

669
01:30:54.950 --> 01:31:06.410
Benjamin Webster: Are we destroying what our sediments actually are, or are we not, and I think it's going to and where we've landed on it's a question of what our question is going to be for the study itself.

670
01:31:06.710 --> 01:31:11.660
Benjamin Webster: And it's looking for drought questions is how our premier producer communities are being shifted over time.

671
01:31:11.990 --> 01:31:22.790
Benjamin Webster: So I think what's going to happen for us is through that analysis will do a set where we try bringing up before reconstituting this the solution backwards primary particles again.

672
01:31:23.660 --> 01:31:31.340
Benjamin Webster: Which is that sounds like a lot of Sana occasion and a ton of work and I, who knows how feasible that's even going to be.

673
01:31:33.140 --> 01:31:43.490
Benjamin Webster: But if it wasn't if we really care about the terrestrial erosion, I think we'd be more inclined to try to burn off and we probably do that in our mobile for us to like the normal loss on the mission analysis.

674
01:31:45.350 --> 01:31:47.540
Kristen Steele: Young in terms of ours and.

675
01:31:48.020 --> 01:32:02.420
Kristen Steele: With my core here like Jackson, it is just so fine and so clay that ideally you do when you're doing a green size analysis, we would do loss on ignition first to get rid of all the organics and that works great and really see any samples.

676
01:32:03.140 --> 01:32:12.020
Kristen Steele: But here, if I were to put one of my samples through loss on ignition so that's a 550 degree burn it would just become like a teeny brick, and it would be.

677
01:32:13.190 --> 01:32:15.560
Kristen Steele: impossible to break it apart at all.

678
01:32:16.880 --> 01:32:23.480
Kristen Steele: So, unfortunately, I mean it's a really good it's a really good point is something that we need to consider because we don't we don't add any.

679
01:32:24.890 --> 01:32:39.140
Kristen Steele: We also add, like any chemicals or anything to the sample to get rid of any organics excuse me so um but thankfully for my core it's because it's so fine grain and we don't have a lot of.

680
01:32:39.920 --> 01:32:55.520
Kristen Steele: visible macro fossils that are obviously it is not very rich in like leaves barks or anything like that, so you don't have to deal with those as much, but they have they got to be there, you know so that's why we chose the sonic eight.

681
01:32:57.140 --> 01:33:05.360
Kristen Steele: For our samples to hopefully get rid of that stuff so it's not that big of a factor in our samples, and also, so we use.

682
01:33:06.170 --> 01:33:22.250
Kristen Steele: So what we looked at was the 98th percentile green size and by that, theoretically you're going to remove any large macro fossils that may be in the sample as well or any like clumps of clay that may have that may not have been broken down.

683
01:33:23.330 --> 01:33:31.340
Kristen Steele: Because I did like a physical kind of like plunger technique at first to physically desegregate and then we again had the song occasion.

684
01:33:32.420 --> 01:33:35.510
Kristen Steele: But didn't use, like any diesel oculus or anything like that so.

685
01:33:37.580 --> 01:33:39.680
Kristen Steele: Some sticky stuff in there for sure.

686
01:33:42.650 --> 01:33:44.750
Lisa Davis: Here I mean sorry go ahead.

687
01:33:45.050 --> 01:33:55.040
Benjamin Webster: No, I was just gonna say one thing that I forgotten so we're also kind of trying to explore some i've been helping in the lab procedures of developing as some micro plastic analysis from our settlement corps.

688
01:33:55.460 --> 01:34:14.090
Benjamin Webster: um and we have been using hydrogen peroxide to remove organic content so we've been arguing, maybe doing a subtracted approach to see if we're getting consistent amount of dissociation but it's just one of the authors like another thing that we've been considering but it.

689
01:34:15.980 --> 01:34:16.160
yeah.

690
01:34:17.660 --> 01:34:31.190
Lisa Davis: yeah well we've been kind of statistically managing it, you know because i'm working with like a floodplain flood set and it's and i'm looking for peaks and sand, essentially, and so you know the small stuff is is noise, essentially for me.

691
01:34:32.300 --> 01:34:37.130
Lisa Davis: And, and so we we do an end Member model, in addition to using like the.

692
01:34:37.760 --> 01:34:45.950
Lisa Davis: And stuff like that, then all of those data, then put into an end Member model to isolate the extreme values and stuff and separate out the particle size.

693
01:34:46.850 --> 01:35:02.240
Lisa Davis: distributions into different populations, and so, so I know it's happening, and all of that statistical massaging but it just doesn't feel as good as if I were controlling for it in that lab through an actual measurement you know so but it sounds like you know.

694
01:35:03.620 --> 01:35:08.180
Lisa Davis: everybody else kind of has their strategy and and you know it's well reasoned.

695
01:35:09.980 --> 01:35:10.400
Kristen Steele: yeah.

696
01:35:11.660 --> 01:35:13.430
Kristen Steele: To do the best we can, with what we got.

697
01:35:15.410 --> 01:35:22.820
Kristen Steele: going to, and I did want to ask, because I know you know you're working with reservoirs and everything you may have noticed in my presentation I mentioned lake lanier.

698
01:35:23.660 --> 01:35:31.190
Kristen Steele: In Georgia, I just wanted to like get your perspective on that if you have you know any particularly special thoughts on it, or anything.

699
01:35:31.550 --> 01:35:34.970
Benjamin Webster: So lakeland near is kind of been.

700
01:35:36.200 --> 01:35:38.030
Benjamin Webster: elusive to my research.

701
01:35:39.320 --> 01:35:48.230
Benjamin Webster: If you're really interested in like linear it's Dr Fitzgerald would be a great person to look up she's releasing collected some I believe.

702
01:35:49.100 --> 01:36:00.470
Benjamin Webster: usps corps from lake lanier and they've actually have that information publicly available on the usps website, Dr Susan Fitzgerald her last name is Michelle can remember.

703
01:36:02.090 --> 01:36:13.130
Benjamin Webster: It is super interesting to us, the reason why it says five reservoir six raspberries is a really big undertaking for a master's work of collection.

704
01:36:14.840 --> 01:36:23.360
Benjamin Webster: And I wanted to collect material from lake lanier also do this comparison of what is upstream of a majority of the metropolitan Atlanta area over this period.

705
01:36:24.140 --> 01:36:34.460
Benjamin Webster: But just timing didn't work out it's actually something that I want to go collect maybe within the next month and a half and do my own personal analysis on to be as consistent as possible.

706
01:36:36.590 --> 01:36:48.470
Benjamin Webster: But it's super interesting in the that sequence of reservoir is it has the largest volumetric ability to hold water storage compared to all of the other systems by almost double it's a huge reservoir.

707
01:36:49.220 --> 01:36:52.010
Benjamin Webster: it's extremely dendritic in terms of its morphology.

708
01:36:52.400 --> 01:37:02.480
Benjamin Webster: And it's the one I think that, and I believe it's the one that's experienced the greatest amount of water level fluctuation throughout all the system it's just because it's the one it's drawn on the most.

709
01:37:03.440 --> 01:37:18.680
Benjamin Webster: middle reservoirs are harding garage and all of her are all owned and operated by the Georgia power, all the other systems are all operated by the army corps, so they really don't have any say in terms of Georgia power of what they're like levels will be.

710
01:37:20.960 --> 01:37:30.110
Benjamin Webster: yeah no I, I have a tons of I would love to go there if the money's there for me to get to go and collect material and have time to analyze it.

711
01:37:30.710 --> 01:37:40.130
Benjamin Webster: Is is really just the limiting factor I think it's going to happen fingers crossed currently in our work, now we are including the flint watershed we're going up that system, as well as to like blackshear.

712
01:37:40.580 --> 01:37:50.210
Benjamin Webster: worth or cigala depending on what you call it and going back to seminal to get a new core kind of in a better location, we believe, so that's kind of the amy right now.

713
01:37:51.410 --> 01:37:53.300
Kristen Steele: yeah that's really interesting especially just.

714
01:37:53.660 --> 01:38:05.510
Kristen Steele: What I looked into with like linear very briefly these like you know the legal side of it, and it seems like a lot of you know those four states that are right around there they all kind of have their fingers in it and they all use it for different purposes.

715
01:38:05.780 --> 01:38:06.320
Yes.

716
01:38:07.910 --> 01:38:10.490
Kristen Steele: they're just like a lot of a lot of stakeholders there.

717
01:38:12.080 --> 01:38:20.180
Kristen Steele: Who would be interested in, you know, for you to do what you do on Lake lanier so it's really interesting it kind of just seems like.

718
01:38:21.200 --> 01:38:36.260
Kristen Steele: You know, not a mess, but you know it's like it's recreational it's used for residential water, and you know, is it, and I think there's like a nuclear power plant nearby Is that correct where they use it to cool the reactors.

719
01:38:36.530 --> 01:38:38.210
Benjamin Webster: For cooling station I.

720
01:38:39.710 --> 01:38:44.450
Benjamin Webster: don't I should know that i'm actually from Atlanta that's where I grew up inside.

721
01:38:44.450 --> 01:38:45.980
Benjamin Webster: The perimeter.

722
01:38:47.030 --> 01:38:47.690
Kristen Steele: Maybe not.

723
01:38:48.020 --> 01:38:56.420
Benjamin Webster: I can't it no it definitely could be true, I just don't know what that top of my head, I feel like for looking at something like that and trying to just keep up with some of the litigation.

724
01:38:57.140 --> 01:39:00.200
Benjamin Webster: so hard that I lose track of some of the other important details.

725
01:39:01.370 --> 01:39:10.550
Benjamin Webster: But yeah the people who are interested it's it's funny because, like growing up in Atlanta I had this perspective, like, who is the bad guy in terms of the water wars, because of the dry everything.

726
01:39:10.910 --> 01:39:24.170
Benjamin Webster: and going back to my family with this new perspective on it, even just people weren't close with they're like no, you have it wrong we're the good guys it's everyone else who's the problem and it's yeah it's a lot of that.

727
01:39:24.800 --> 01:39:29.300
Kristen Steele: So, like it in your experience Georgia would be the good guy in that instance.

728
01:39:30.950 --> 01:39:33.680
Kristen Steele: or like I you want to like who are the good guys and bad guys.

729
01:39:34.220 --> 01:39:47.120
Benjamin Webster: You know it's that's a I don't like to phrase it that way anymore, I think I think it makes it really polarizing I think it's easier to talk about in terms of just water, the actual water needs who actually needs what.

730
01:39:47.870 --> 01:39:55.070
Benjamin Webster: Because people there's an enormous population Atlanta within the part of the watershed it's it's really probably the largest one.

731
01:39:55.880 --> 01:40:06.500
Benjamin Webster: So there's just a lot of people there who do need water for drinking water purposes and water efficiencies can be improved in the are being improved and, if you look at the flint watershed that's all pivot our culture that.

732
01:40:07.670 --> 01:40:17.870
Benjamin Webster: enters the groundwater system, and those are becoming more and more efficient over time and it's just kind of this back and forth question of who is using it appropriately, you know what I mean.

733
01:40:18.230 --> 01:40:22.880
Kristen Steele: yeah yeah I can understand that that there you know there's a hierarchy of needs there.

734
01:40:23.150 --> 01:40:27.680
Kristen Steele: hmm you know and and imagine the recreation needs probably kind of go down a little bit.

735
01:40:29.090 --> 01:40:45.440
Benjamin Webster: I will say as a person who does not care for golfing and when I see the golf courses having these very lush lawns I just kind of look at them and i'm like okay that's, I guess, a need for someone but I guess i'll leave it there on that feeling.

736
01:40:45.740 --> 01:40:47.780
Benjamin Webster: yeah actually a question about your.

737
01:40:47.840 --> 01:40:58.400
Benjamin Webster: viper core i've only had like the privilege of doing the vibe report, one time, I think, was on cherry late in June so South Georgia um how long is that core.

738
01:40:59.330 --> 01:41:14.180
Kristen Steele: So I actually wasn't there to collect this actually it's kind of a funny story with this course, so it was taken with a Ross felder vibe record and they actually they recently they being on people at woods hole oceanographic institution they.

739
01:41:15.590 --> 01:41:31.280
Kristen Steele: were just testing out this vibe record and they went to lake Jackson and found this you know deep pocket in there and test it out and got this nice long core so ultimately the cord that I have is about 6.7 meters consisting of five sections so it's long.

740
01:41:32.300 --> 01:41:39.380
Kristen Steele: it's high resolution and they're behemoth course for sure, but I mean that's just one of my I know I was going to ask about.

741
01:41:40.010 --> 01:41:50.030
Kristen Steele: It because it kind of sounds like our coining techniques are probably very similar, but in my lab we also have you know polly carb cores that were taken with Bolivia coring.

742
01:41:50.630 --> 01:42:02.180
Kristen Steele: And livingston coring and things like that so they're not all giant behemoth vibe records, but you know when you need to get into that thick clay, sometimes you gotta do what you gotta do so.

743
01:42:02.750 --> 01:42:12.050
Benjamin Webster: Especially the sand, I hate having to work with sand settlements and they just compacts and you can't get through it.

744
01:42:12.740 --> 01:42:15.890
Kristen Steele: Right yeah and, especially, this is kind of like.

745
01:42:17.870 --> 01:42:27.830
Kristen Steele: My lab but we're also doing some projects with Paleo seismology we're we're taking lake course from places that are near like known seismic zones and stuff.

746
01:42:28.550 --> 01:42:41.810
Kristen Steele: And you know so that's particularly important because what we're looking for there's the earthquake structures and the sentiment, so if you're calling and you're you know messing around and not doing a straight like you can really jeopardize your record there.

747
01:42:43.280 --> 01:42:51.770
Kristen Steele: So I mean i'm sure you've been recording is a very delicate process are you guys and when you call it is it all manpower or do you have anything else that you use.

748
01:42:52.370 --> 01:42:54.440
Benjamin Webster: A it's all man power.

749
01:42:56.690 --> 01:43:03.860
Benjamin Webster: For Mayra further so our lab normally doesn't do reservoir is i'm kind of like the kid in our lab who does I kid.

750
01:43:03.890 --> 01:43:11.750
Benjamin Webster: Nice and say that anymore i'm a little bit too old for that i'm i'm the person who does a lot of our reservoir coring but traditionally when much sal or.

751
01:43:13.190 --> 01:43:24.380
Benjamin Webster: Florida reservoirs Georgia resume resume is natural legs shallow legs, we do a lot of push scoring systems and we can usually do our push core with our rig at about.

752
01:43:25.280 --> 01:43:33.980
Benjamin Webster: Six to 10 meters of depth and then we stopped hitting a ability to go much further just from a lack of poles of attaching to one another.

753
01:43:34.220 --> 01:43:42.560
Benjamin Webster: yeah but for the gravity corps it's a little bit different just because you're holding onto a single rope the whole time so it's this really.

754
01:43:43.040 --> 01:43:54.080
Benjamin Webster: It was a really learned process of how quickly do lower the rig down near the sentiment how near discernment you get before you let it free fall and plunge into it.

755
01:43:55.040 --> 01:44:02.630
Benjamin Webster: And there was a lot of just personal trial and error at lake Martin elect Jackson, which are pretty close by wrestlers to our location here.

756
01:44:03.140 --> 01:44:15.530
Benjamin Webster: And we kind of found a sweet spot, but the I send it pissing cause a whole nother challenge, because you have two ropes you have one route that you actually can holds the rig another one that holds the other plate that hammers it back down into the sediment.

757
01:44:16.580 --> 01:44:27.230
Benjamin Webster: And it's a very I don't know if you've had the experience, but just a bizarre feeling holding this one wrote me like Okay, if I drop this and now it's going to be sideways and it's a top heavy objects, so if you drop it too.

758
01:44:27.770 --> 01:44:32.450
Benjamin Webster: Far of a freefall it's going to invert itself and just get a bunch of mud upside down on him.

759
01:44:32.810 --> 01:44:33.170
yeah.

760
01:44:34.190 --> 01:44:35.420
Benjamin Webster: yeah it's.

761
01:44:35.480 --> 01:44:35.960
Benjamin Webster: So we should.

762
01:44:36.260 --> 01:44:42.230
Benjamin Webster: push course which are much more manageable and but we're I do more of the gravity course.

763
01:44:42.890 --> 01:44:49.790
Benjamin Webster: But in terms of manpower we've got I had the privilege, you get to go to like sit alone in Guatemala, which is pretty deep and we use our gravity cornering there, we had to get.

764
01:44:50.240 --> 01:44:58.640
Benjamin Webster: A new type of rope for it, but I did build a little mechanical winch for us to use that was collapsible to go as an airplane carry on.

765
01:44:59.030 --> 01:45:06.230
Benjamin Webster: Night so that was a really cool feeling so that's all manpower, but we did make a simple machine for that one.

766
01:45:07.040 --> 01:45:15.470
Kristen Steele: very nice yeah, what is your um your vessel, what does that look like you know or do I have a boat canoes like how's the.

767
01:45:15.470 --> 01:45:21.440
Benjamin Webster: Sunday, we have a 20 foot little aluminum Jon boat it's a tiny little thing.

768
01:45:22.190 --> 01:45:39.890
Benjamin Webster: So when we have a windy day windy days just a pain for calling anyways you have with us nice calm days, but if the winds get over 10 miles an hour on a medium sized large lake we're just we're almost blew off the water we can't handle it yeah how about you got you have a nicer vessel.

769
01:45:41.330 --> 01:45:47.120
Kristen Steele: One would think, one would think but we used to have I mean this was kind of a nice little setup but.

770
01:45:48.170 --> 01:46:00.770
Kristen Steele: We used to have a few years ago we just had two canoes that we had side by side and then had a piece of plywood will two pieces of plywood and then we would call in between the two, so you know everyone is standing on the plywood platform there.

771
01:46:01.790 --> 01:46:13.040
Kristen Steele: And coursing through through the gap, but last year we bought it's more of like a pontoon boat, but like pontoons and their inflatable pontoons.

772
01:46:13.460 --> 01:46:33.680
Kristen Steele: And then, it has you know this kind of like structure to it, we again then put the the two pieces of plywood on there, but to get out to our site, we just use oars usually to get out, we have used a Jon boat in the past with one of those just like electric attached will Motors.

773
01:46:36.230 --> 01:46:43.760
Kristen Steele: That was fun I got to do that as an intern and I just thought like this is this is going to be it for me, but everything was fine.

774
01:46:45.260 --> 01:46:57.740
Kristen Steele: So yeah that's what that's what we're working with but I mean I completely understand her sentiment sentiment of like it when just be you know, especially when you're on like a little pontoon boat like that, but.

775
01:46:57.830 --> 01:47:08.600
Benjamin Webster: I can have the canoe coring i've only done it one time, and it was just so challenging so i'm super impressed by that my heart goes out to guys on the field for days like that.

776
01:47:09.080 --> 01:47:16.820
Kristen Steele: yeah and having to you know also when you're in the it just like you're constantly it's so hard to stay in place and we had this sort of like.

777
01:47:17.420 --> 01:47:27.800
Kristen Steele: triangular anchoring setup and so that's also a really delicate thing because you know, we have our corn spots picked out based on typically gdpr data or you know something else.

778
01:47:28.370 --> 01:47:38.270
Kristen Steele: So we have a very specific spot that we want to get to and it's a very delicate dance having to like triangulate these anchors so that we're right on top of the spot, where we need to be.

779
01:47:40.790 --> 01:47:44.720
Kristen Steele: But actually we've started over the past few years.

780
01:47:45.800 --> 01:47:55.940
Kristen Steele: One time we were out in the field doing coaching and it was just kind of like you know we were in a battle with coring and we were losing and we started this new technique, where we.

781
01:47:56.840 --> 01:48:10.220
Kristen Steele: We started whispering while we were on the boat like just try it like everything was just whispered and we're like okay now we're going to drop this and it actually had like a pretty nice effect because everyone, I feel like was just.

782
01:48:10.520 --> 01:48:16.820
Kristen Steele: Really focused yeah just really focused and like you know really delicate with every I mean delicate, as you can be but.

783
01:48:18.440 --> 01:48:30.920
Kristen Steele: You know, it was a chaos on the plywood out there and that works pretty pretty nicely, I mean you feel like fools being out there in the middle of a way completely by yourself and everyone is whispering but.

784
01:48:32.210 --> 01:48:33.620
Kristen Steele: If it works, you know.

785
01:48:33.950 --> 01:48:45.170
Benjamin Webster: We started doing a similar thing when we were, I think we've gone about four or five times to practice gravity corn, and we just were not successful for these four trips that we took out there.

786
01:48:45.650 --> 01:48:59.450
Benjamin Webster: And my lab may started, playing on like our little speaker free falling and somehow That was the song that made everything come together so for while that was a tradition of claims certain songs to get good course so.

787
01:48:59.840 --> 01:49:00.770
awesome.

788
01:49:02.090 --> 01:49:05.840
Kristen Steele: yeah and, of course, we have the rule that everyone is allowed to curse as much as they want.

789
01:49:07.580 --> 01:49:14.240
Kristen Steele: The situation calls for that you know so professionalism kind of goes down a notch there but.

790
01:49:16.190 --> 01:49:24.680
Kristen Steele: I actually haven't been in the field at all during the pandemic, we are, we are able to you have to get special permission to go.

791
01:49:25.880 --> 01:49:33.530
Kristen Steele: But you know, especially with coin it usually a lot of people are involved, so that makes it a bit harder because right now, our.

792
01:49:33.980 --> 01:49:45.830
Kristen Steele: Protocol is if you're going out into the field with a group, everyone needs to be in their own vehicle and you know you have to be able to maintain distance it's like well that's kind of impossible and coring when you're all on top of each other.

793
01:49:46.940 --> 01:49:49.910
Kristen Steele: And so, unfortunately we haven't been able to do any.

794
01:49:51.470 --> 01:50:02.330
Kristen Steele: Which is a bummer because we are planning on going back to this lake lake Jackson sometime this spring to get more cores but it didn't happen so.

795
01:50:03.080 --> 01:50:08.090
Benjamin Webster: Now look jackson's gorgeous we got to, and I feel you on the train for offerings how to.

796
01:50:08.660 --> 01:50:17.930
Benjamin Webster: Changing rules here and there, of how things work but two summers ago we got to do a large Florida trip looking at sun Microsystems concentrations for another graduate students project and.

797
01:50:18.260 --> 01:50:29.810
Benjamin Webster: I believe, like jackson's one of the legs he went to we went to 47 lakes over a two week period we weren't doing extensive completed core is redoing top four centimeters bottom four centimeters of a.

798
01:50:30.950 --> 01:50:39.830
Benjamin Webster: 1.2 meter barrel, and the data should be it's near the end of the pipeline so i'm excited to see that coming out sooner than later, but.

799
01:50:41.180 --> 01:50:45.260
Kristen Steele: i'm excited for it gosh I mean whether that's a two lakes, a day.

800
01:50:45.530 --> 01:50:47.300
Benjamin Webster: Oh, we one day we hit for.

801
01:50:49.100 --> 01:50:55.070
Benjamin Webster: Most these were three they were long days we went to bed, it was just long days it was great it was a wonderful trip.

802
01:50:55.460 --> 01:51:03.260
Kristen Steele: Right, and I can I mean i'm sure you really got a system down with like okay here's how we're going to set everything up here that we take everything down here's how we load like.

803
01:51:04.160 --> 01:51:06.620
Kristen Steele: OPS down to a science.

804
01:51:07.040 --> 01:51:17.450
Kristen Steele: yeah, but I also I mean i'm not i'm from Virginia, but I, you know so i'm kind of familiar with you know the southeast and everything, but I also didn't realize like my lake is like Jackson, but then.

805
01:51:17.810 --> 01:51:23.660
Kristen Steele: there's also the more well known lake Jackson, I think outside of tallahassee I want to say somewhere.

806
01:51:24.530 --> 01:51:26.780
Benjamin Webster: there's so many like jackson's in Florida there's like.

807
01:51:26.780 --> 01:51:27.440
Kristen Steele: Is there.

808
01:51:27.470 --> 01:51:34.670
Benjamin Webster: So there's a silver lake and lake silver in Florida so yeah they might have been a different like Jackson.

809
01:51:35.660 --> 01:51:47.300
Kristen Steele: It but yeah that's why I got so confused because there is, you know this, the city that surrounds my lake for Allah there's a nice little like human history of it.

810
01:51:47.840 --> 01:51:57.500
Kristen Steele: And then I saw that there's this other like lake Jackson Oregon I was like oh my gosh this is everything I need and i'm like I realized five minutes later, this is not my lake.

811
01:51:58.640 --> 01:52:01.310
Kristen Steele: Like it was just completely different shape like not hit.

812
01:52:02.090 --> 01:52:10.550
Steph Shepherd  (she/her): So i'm gonna have to wrap it up you i'm so excited that we've actually gotten to have a real conversation, even though I wasn't there for quite all of it.

813
01:52:12.230 --> 01:52:14.840
Steph Shepherd  (she/her): So, in turn, it over to Kim for the next part of the session

814
01:52:18.800 --> 01:52:20.300
Steph Shepherd  (she/her): Okay you're still muted, I think.

815
01:52:22.460 --> 01:52:27.710
Kimberly Takagi: Still morning great um so welcome everybody to the session.

816
01:52:29.270 --> 01:52:39.710
Kimberly Takagi: On understanding natural and anthropogenic influences on rivers wetlands and coastal environments, we have, I believe, three of the co hosts here.

817
01:52:41.720 --> 01:52:53.510
Kimberly Takagi: Two or three of our co hosts if, during the presentations you would like to ask a question, you could go ahead and raise your hand or at the end you can.

818
01:52:54.800 --> 01:53:11.810
Kimberly Takagi: Ask it in the chat and the three of us will try and monitor the chat to see who is answering questions you guys have about 20 minutes for each of your presentations and if you can set a timer on your phone that would be great also.

819
01:53:12.860 --> 01:53:19.490
Kimberly Takagi: I will try and monitor as well and give you a heads up if you win you're about two minutes close to finishing.

820
01:53:20.510 --> 01:53:21.050
Kimberly Takagi: and

821
01:53:22.100 --> 01:53:23.780
Kimberly Takagi: stuff is there anything I missed.

822
01:53:26.150 --> 01:53:29.660
Steph Shepherd  (she/her): I think that's it and I, I do have a little card I haven't had to show it yet.

823
01:53:30.140 --> 01:53:32.210
Kimberly Takagi: Oh sweet, I appreciate that.

824
01:53:33.590 --> 01:53:34.370
Kimberly Takagi: Okay um.

825
01:53:34.670 --> 01:53:43.040
Kimberly Takagi: The first presentation is recording so I I believe i'm supposed to share my screen and play that recording.

826
01:53:43.130 --> 01:53:46.790
Kimberly Takagi: Yes, so I will do that now and we'll see if this works.

827
01:53:46.850 --> 01:53:51.320
Steph Shepherd  (she/her): And just make sure that you click the on the share when you do that, you you.

828
01:53:52.880 --> 01:54:01.910
Steph Shepherd  (she/her): optimize, for I don't know I always forget how to do this, that they hear that they hear the video or they hear the sound from the video.

829
01:54:02.780 --> 01:54:06.650
Kimberly Takagi: Okay, all right well let's see we'll try this.

830
01:54:06.710 --> 01:54:07.760
Kimberly Takagi: Okay, how it goes.

831
01:54:08.300 --> 01:54:09.110
Steph Shepherd  (she/her): I screwed up all the.

832
01:54:10.850 --> 01:54:13.430
Kimberly Takagi: First, I have to find it because I see it, but.

833
01:54:14.240 --> 01:54:14.870
Kimberly Takagi: Oh, here we go.

834
01:54:20.570 --> 01:54:21.080
Kimberly Takagi: well.

835
01:54:22.820 --> 01:54:25.100
Kimberly Takagi: Okay, this is, this is a bit of an experiment, but here we go.

836
01:54:26.480 --> 01:54:30.350
Kimberly Takagi: Can I share my screen and then videos here.

837
01:54:33.530 --> 01:54:37.190
Kimberly Takagi: For tuning into this presentation, could you see here that.

838
01:54:38.150 --> 01:54:39.230
Steph Shepherd  (she/her): Yes, we've got it.

839
01:54:40.220 --> 01:54:40.610
sweet.

840
01:54:42.560 --> 01:54:51.830
Kimberly Takagi: Good morning, everyone, and thank you for tuning into this presentation, my name is medan murder and i'm going to preach it might be.

841
01:54:52.970 --> 01:55:08.660
Kimberly Takagi: revealing responses to a chef and using residential water well data this research was conducted as a part of sense internship at risk of North Carolina at pembroke and was supported by a grant from robson county public.

842
01:55:12.260 --> 01:55:24.590
Kimberly Takagi: Electronic movements and glacial drifts can generate enough interest to the bedrock that can change in principle is stress conditions doesn't fencing parameters such as hydraulic conductivity and the strategy.

843
01:55:26.720 --> 01:55:37.490
Kimberly Takagi: As we know that many of the northern states in the United States had been pleasure multiple times in the recent history during crisis in our, also known as I said.

844
01:55:38.030 --> 01:55:47.030
Kimberly Takagi: The last Ice Age extent until 1000 years ago the initial advance at different times has sown in the figure with the different colors.

845
01:55:48.290 --> 01:56:03.440
Kimberly Takagi: nebraska think the old and Wisconsin think that youngest one applause ice in general, from the North to the South, as shown in this map here is true for the United States atlas.

846
01:56:05.210 --> 01:56:17.810
Kimberly Takagi: disclosures are a good transporting isn't an act as conveyor belt carrying sediments reading from very fine to very course up to boulder top of publishers and within the.

847
01:56:19.310 --> 01:56:23.960
Kimberly Takagi: During the interview special period, though ice melted and the settlements were left.

848
01:56:24.680 --> 01:56:38.420
Kimberly Takagi: Behind and deposited in aggregate train of privilege shares and surface making those land smoother the techniques of patient sediments very widely and mostly depend on the pre glaciated topography.

849
01:56:39.260 --> 01:56:50.840
Kimberly Takagi: Take her fifth were found of free history stream networks such as a case river sewn in the pink and blue collar in the middle of the State of Ohio.

850
01:56:52.880 --> 01:56:53.420
Kimberly Takagi: Ohio.

851
01:56:55.190 --> 01:56:57.980
Kimberly Takagi: Oh, and the stories of water in that are dependent upon.

852
01:57:00.140 --> 01:57:21.080
Kimberly Takagi: That could be compromised to to excellent stresses like lashes and take courses, it is our best interest to understand whether or not appreciated aquifers have experienced any changes in apple for characteristics, due to the psychic loading and unloading measures during college.

853
01:57:22.640 --> 01:57:41.660
Kimberly Takagi: We seek to compare the hydraulic conduct the value of a drop aquifers underappreciated and accomplish it it isn't to test whether the aquifer was impacted by the glaciers, and then then relate the thickness of visual drift to yelled.

854
01:57:43.790 --> 01:57:57.800
Kimberly Takagi: We decides that they should it returns would have higher hybrid conductivity value than on glaciated reason, as these bedrock experience higher stress fishes and and discuss interfaces.

855
01:57:58.850 --> 01:58:16.940
Kimberly Takagi: promoting the development of secondary prosperity in them, and the second half past six was sicker patients would correlate higher aquifer yield, that is, the wheels in Jordan reason we have higher yield and data southern reason.

856
01:58:18.500 --> 01:58:35.330
Kimberly Takagi: To test that hypothesis, we click hundreds of private residential logs and printing reports from our department of natural resource database that those whales are located in place, yet it, as well as on glaciated reasons and later analyzed and statistically.

857
01:58:36.410 --> 01:58:46.340
Kimberly Takagi: This was an attempt to find next of kin cyclic loading and unloading of fascism tobacco first characteristic statistical.

858
01:58:48.800 --> 01:58:55.340
Kimberly Takagi: a steady area lights in the northern region of the State of Ohio million homes and for kids count.

859
01:58:56.180 --> 01:59:12.740
Kimberly Takagi: Was counties like in the appalachian plateau and consists of sandstone run it through the county Holmes county, which is in close by a red box was partially glaciated it's counting votes blue box was completely fleshed out in.

860
01:59:14.480 --> 01:59:23.540
Kimberly Takagi: The rain carb line running through the State of Ohio is the maximum official extent that runs through Holmes county and divided into play shaded.

861
01:59:24.140 --> 01:59:37.490
Kimberly Takagi: goes on and on vitiated southern reason or reasons were covered with taker I said during the last patient and a stress important portage county due to the overlying is it.

862
01:59:37.970 --> 01:59:50.510
Kimberly Takagi: was better than that in the Holmes county lauren art can be described as a pleasure at all, which was smoothing out with a question filling in on top of a drug addict.

863
01:59:51.470 --> 02:00:01.970
Kimberly Takagi: or homes and tortoise counties have thousands of residents of wealth, but only the depicted here are the ones that were tapped in Sharon sense.

864
02:00:06.470 --> 02:00:16.010
Kimberly Takagi: conceptual model, reflecting the responses of because fair to to the glacier loading and unloading was adopted from Brian at all we did.

865
02:00:17.480 --> 02:00:23.600
Kimberly Takagi: not directly under the Arctic ice is compressed file the rock at the depth are under tension.

866
02:00:24.710 --> 02:00:36.230
Kimberly Takagi: or acted upon the rock creates an uneven stress distribution, which then creates new facts or Iraq smile, it was fear subsides under the pig I see.

867
02:00:36.770 --> 02:00:59.960
Kimberly Takagi: The same little square on either side of the ball created by the ice will have differential compressing and tensile strength posit other in comparison to the area right under the ice due to the unloading of the ice the force acted upon a bedrock reciprocate after we get off the ice.

868
02:01:01.820 --> 02:01:12.650
Kimberly Takagi: rock on reports plastic to for medicine from the equation loading and unloading ultimately intensifying the creation of factors and increase the secondary prostate.

869
02:01:14.780 --> 02:01:18.830
Kimberly Takagi: Bigger on the right so development of factors bedrock.

870
02:01:20.000 --> 02:01:25.190
Kimberly Takagi: coding and unload ticker the ice deeper the factors and higher they.

871
02:01:30.050 --> 02:01:31.610
Kimberly Takagi: are left with the general settings.

872
02:01:33.980 --> 02:01:39.620
Kimberly Takagi: the middle of the slide can see the photographs of some of the box your.

873
02:01:40.640 --> 02:01:47.120
Kimberly Takagi: Data was obtained from the whale logs and report for hybrid extension water wells tempting.

874
02:01:49.640 --> 02:01:56.030
Kimberly Takagi: archive those data in the oil and water resources of it department of natural.

875
02:01:57.950 --> 02:02:07.670
Kimberly Takagi: These whales were randomly clicked it and tallied 691 for Holmes county and 731 for four days county.

876
02:02:08.660 --> 02:02:26.120
Kimberly Takagi: Where log and truly reports content will locations will production test data well constructed details and rudimentary he was prescription, as highlighted within the red rectangles and those were tuned in for your convenience here.

877
02:02:32.270 --> 02:02:41.900
Kimberly Takagi: Production test off the residential wills consist of static water level read on the time duration and drawn out at the end of the pumping.

878
02:02:45.650 --> 02:03:01.250
Kimberly Takagi: Discipline sequence is the Cooper tech of approximation of the ties solution for the pumping test which contains two unknown aquifer parameters it's our trends sensitivity and directing store activity takeaways T and S.

879
02:03:02.780 --> 02:03:14.420
Kimberly Takagi: transmissibility may be computed issue being the value of relativity based on the furniture, such as whether they have confined leaky or unconfined.

880
02:03:15.020 --> 02:03:22.250
Kimberly Takagi: errors in estimating interactive devalue would cause significant errors in the resulting value of transmissibility.

881
02:03:22.910 --> 02:03:36.860
Kimberly Takagi: However, and error of two orders of magnitude in the estimated strategy with a would only yield roughly 10 to 20% in error resulting transmissibility value I couldn't to Walter 97.

882
02:03:38.000 --> 02:03:49.340
Kimberly Takagi: I probably can't make money off that first in the immediate vicinity of the world was estimated by dividing the calculate specific value with the thickness of the aquifer.

883
02:03:52.040 --> 02:04:02.990
Kimberly Takagi: We made assumptions, such as part of flow in Wales, so the complete thickness of the aquifer meeting that total thickness of it and cast.

884
02:04:04.400 --> 02:04:14.720
Kimberly Takagi: A political well loss at his partner flew into the well is laminar outside the world as well to our pump adore red, which has.

885
02:04:15.530 --> 02:04:31.970
Kimberly Takagi: Less than hundred cubic meters per day and for collectively short period of time, typically less than two hours goes public test the radius of wells remained constant as development of world had not altered the radius of to build.

886
02:04:33.200 --> 02:04:44.540
Kimberly Takagi: out a cool project of approximation to the tides method yield and insignificant here's estimating that hyperlink conduct we devalue and the radius of well being small.

887
02:04:45.620 --> 02:04:54.950
Kimberly Takagi: frequency distribution curved display a nature of data characteristics of home company and it's glaciated and on reasons.

888
02:04:55.610 --> 02:05:08.060
Kimberly Takagi: The X axis represents the log transform hydraulic conductivity of Saturn San Fernando for the y axis represents the frequency and the vertical bars are back to himself.

889
02:05:08.810 --> 02:05:26.750
Kimberly Takagi: A blue green and the red vertical lines, so the mean hybrid conductivity of Saturn sense shown in the reason or frequency distribution curves in the bigger have the same starting and ending highly conductive values, to make the comparison easy.

890
02:05:28.040 --> 02:05:39.770
Kimberly Takagi: Although the number of whales at were assembled in vitiated and Andre said reasons are not vastly different means calculator For these reasons, are significantly different.

891
02:05:40.730 --> 02:05:48.050
Kimberly Takagi: Basically reason had highest mean ordered by Holmes county as a whole, and then the unweighted reason.

892
02:05:48.950 --> 02:06:09.770
Kimberly Takagi: All the carbs positively skewed and had approximately equal amount of spread in data, also a table a frequency distribution curve for pleasure that reason, so a noticeable shift towards by in comparison with on place it reason and orange county.

893
02:06:11.480 --> 02:06:20.000
Kimberly Takagi: The result, so the hydrogen economy after homes come to spread much more wider than the port is counted as the figure.

894
02:06:20.780 --> 02:06:30.890
Kimberly Takagi: On this county data are positively skewed while portage county data displayed a normal distribution curve, they have integrity conductivity of the bedroom.

895
02:06:31.370 --> 02:06:41.810
Kimberly Takagi: or within the 40s county was much higher than that within the county we ran the statistical tests like T test after test.

896
02:06:42.500 --> 02:06:53.330
Kimberly Takagi: To test were conducted for glaciated an unrelated reasons within the Holmes county as well as Holmes county and it's counting separately.

897
02:06:54.140 --> 02:07:09.080
Kimberly Takagi: TTS basically suggests that qualitative reasons had significantly higher been values and, on the situation as well as portage county has hired a significantly higher in value and count.

898
02:07:09.800 --> 02:07:20.330
Kimberly Takagi: Similarly, the F tests were conducted for question it and unpleasant reason as well as the homes and the portage county as a whole to check there are variances where they are.

899
02:07:20.780 --> 02:07:36.530
Kimberly Takagi: And are not I would be and it's basically a question on the set reasons had a variance which are not statistically different as the homes and orange county had the variance sorry statistic different.

900
02:07:38.540 --> 02:07:50.930
Kimberly Takagi: That creek back displayed the distribution of the hydraulic conductivity values and allowed or the visualizes not just traditional public conduct periods of sense for within homeless and for gets counted.

901
02:07:52.010 --> 02:08:02.690
Kimberly Takagi: county had higher hydraulic conduct devalues in the glaciated reason, there was a stark difference in the hybrid economy, the value of the bedrock of works south of the maximum.

902
02:08:03.260 --> 02:08:17.420
Kimberly Takagi: extent, especially in the eastern region of Holmes county orange county had cluster of low hydrogen economy values in the Center of the county while the surrounding area contents I am hydrogen economy and it.

903
02:08:20.240 --> 02:08:31.580
Kimberly Takagi: Just triggers so the structure contour map of the base of the seven cents on for mason showing the Paleo geography in the northern central Ohio mode five.

904
02:08:34.820 --> 02:08:44.990
Kimberly Takagi: overlaid instruction controller on top off the BAT though good overlap between the high LM s men high tide really conduct failures.

905
02:08:45.650 --> 02:08:58.790
Kimberly Takagi: You man, the special auto correlation of hybrid conduct many of the bedrock aquifer within the county and found that a special, this is not actually conduct he was cluster significantly different to counter.

906
02:08:59.450 --> 02:09:09.740
Kimberly Takagi: likelihood that the clustered pattern of it can't, be they could be the result of random chance is about 58% for Holmes county my it is.

907
02:09:10.790 --> 02:09:14.780
Kimberly Takagi: Less than 1% for its county and Sony and the table.

908
02:09:16.040 --> 02:09:23.570
Kimberly Takagi: The results of this that the question loading and unloading yield significant difference in hydraulic conduct the value of the aquifer.

909
02:09:24.380 --> 02:09:37.220
Kimberly Takagi: counting the travel distance is an older higher for Holmes county and that of the county or the status or lesson was much, much stronger in the portraits county and dad.

910
02:09:38.270 --> 02:09:58.400
Kimberly Takagi: out the ritual could have been missing it because of the limitations, such as discussion a tragedy in the bedrock friend sides sand gravel the nature of factory pattern and intensity precocious and paper well documented for that sense from have.

911
02:10:00.050 --> 02:10:08.450
Kimberly Takagi: Value typography and feel morphologic to mind to focus on patient and found very significant stance.

912
02:10:09.830 --> 02:10:19.370
Kimberly Takagi: Because uncertainty what the patient thickness concert as primary driver for us generate these secondary process the bedrock.

913
02:10:21.020 --> 02:10:26.900
Kimberly Takagi: So this study was basically an attempt to uncover act for responses from the sellers.

914
02:10:27.950 --> 02:10:29.450
That covered most part of this.

915
02:10:31.460 --> 02:10:35.690
Kimberly Takagi: Initiative recent had higher hydraulic contract be devalued stand on these.

916
02:10:36.800 --> 02:10:44.870
Kimberly Takagi: On that the wealth located in higher let you were under higher stress is resulting in higher value than the wealth that.

917
02:10:46.070 --> 02:11:01.400
Kimberly Takagi: Now it would be interesting to understand what would be the impact of climate change your experience now like Antarctic ice ice cap are currently experiencing higher rate of melting.

918
02:11:02.780 --> 02:11:06.350
Kimberly Takagi: jen's that could be off better interest hyper.

919
02:11:07.610 --> 02:11:08.030
tutor.

920
02:11:09.800 --> 02:11:13.340
Kimberly Takagi: Thank you, and I will take any questions from the audience.

921
02:11:24.440 --> 02:11:36.410
Kimberly Takagi: Oh, unfortunately um he had to leave for his class at 11 so he will be unable to answer questions and he says that if you have any questions to please forward to his email.

922
02:11:40.700 --> 02:11:45.560
Kimberly Takagi: um but with that that actually puts us on right on time for the next presentation.

923
02:11:47.030 --> 02:11:55.550
Kimberly Takagi: By Assad Hussein and his his on remote sensing of water quality in the Tennessee river using a Sentinel to imagery.

924
02:11:56.750 --> 02:11:58.670
Azad Hossain: hey Kimberly can you hear me.

925
02:11:59.180 --> 02:12:00.380
Kimberly Takagi: Yes, okay.

926
02:12:00.410 --> 02:12:04.040
Azad Hossain: Great, so I think in to let me share the screen.

927
02:12:05.990 --> 02:12:06.320
Kimberly Takagi: I can.

928
02:12:06.380 --> 02:12:07.460
Azad Hossain: I yeah I think I can.

929
02:12:07.970 --> 02:12:08.840
Kimberly Takagi: Okay, great.

930
02:12:38.630 --> 02:12:38.960
Azad Hossain: well.

931
02:12:39.260 --> 02:12:44.990
Kimberly Takagi: Can you see the slides yep making see you see you see the slides and we can see you too.

932
02:12:45.440 --> 02:12:46.100
Azad Hossain: Okay, great.

933
02:12:47.630 --> 02:12:47.990
Azad Hossain: well.

934
02:12:49.100 --> 02:12:49.640
Azad Hossain: My name is.

935
02:12:49.670 --> 02:12:50.930
Kimberly Takagi: As art hussein's i'm.

936
02:12:50.930 --> 02:12:59.360
Azad Hossain: an assistant professor at the Department of biology biology and environmental science at the University of Tennessee at chattanooga.

937
02:13:01.730 --> 02:13:12.230
Azad Hossain: My co author is killer Matthias he's a recent graduate of our geology program is currently working as a research assistant.

938
02:13:13.250 --> 02:13:25.880
Azad Hossain: In my lab that is geological and environmental remote sensing lab at utc well, as you can see, the title of my talk is remote sensing of water quality in the Tennessee we're using Sentinel to.

939
02:13:27.020 --> 02:13:27.650
imagery.

940
02:13:31.160 --> 02:13:33.560
Azad Hossain: So this is an ongoing project.

941
02:13:34.610 --> 02:13:43.040
Azad Hossain: And that we have been doing for last couple of years and for these talk i'd like to share some recent.

942
02:13:44.360 --> 02:13:45.170
results.

943
02:13:46.940 --> 02:13:56.780
Azad Hossain: Say that say so let's talk about little bit about quantitative water quality remote sensing is little bit about history.

944
02:13:57.770 --> 02:14:09.380
Azad Hossain: Satellite remote sensing has been providing the opportunity for sign up to and multi temporal viewing of water quality, for more than 25 years.

945
02:14:10.190 --> 02:14:20.630
Azad Hossain: Well, that constitutes an alternative means of estimating water quality and which offers three significant advantages over crown sampling.

946
02:14:21.500 --> 02:14:33.680
Azad Hossain: When it's not that to replace ground sampling, it is there to compliment ground sampling, with some advantages and what are those so it provides near continuous special covers.

947
02:14:34.460 --> 02:14:46.460
Azad Hossain: and provides water quality estimation in remote in the next several years, it also provide historical water quality data when no no ground measurements can possibly be performed right so.

948
02:14:47.660 --> 02:15:02.090
Azad Hossain: It is pretty good to have some sort of capability to do that if we have an airway water resources, unfortunately, there is no remote sensing this algorithm available to study the surface water quality.

949
02:15:02.660 --> 02:15:24.560
Azad Hossain: in southeast Tennessee specifically in the Tennessee river, so this research, aims to investigate the potential of remote sensing technology to monitor surface water quality in the Tennessee river in chattanooga area steps within Hamilton county Tennessee.

950
02:15:27.830 --> 02:15:37.820
Azad Hossain: So one critical component of water quality remote sensing is to have concurrent ground measurements without that we cannot do quantitative remote sensing of water quality.

951
02:15:38.960 --> 02:15:42.830
Azad Hossain: So we need the means to go in the field and have concurrent.

952
02:15:44.420 --> 02:16:09.350
Azad Hossain: Excuse me ground measurements, so in my lap so I have the research vessel is a small and Jon boat and I have the state of the art of water quality song I do love hl and we can measure temperature pH conductivity do charity and chlorophyll with high precision and having this capability.

953
02:16:12.260 --> 02:16:26.060
Azad Hossain: and gives us the opportunity to go to feel to collect in the ground data when we have no clouds so he's just give some extra sort of flexibility and be independent.

954
02:16:27.500 --> 02:16:29.180
Azad Hossain: to collect those kinds of data.

955
02:16:32.360 --> 02:16:38.900
Azad Hossain: Alright, so this is the status side we basically wanted to cover them tab Tennessee river that is located within.

956
02:16:40.700 --> 02:16:42.200
Azad Hossain: Hamilton county Tennessee.

957
02:16:45.440 --> 02:16:59.780
Azad Hossain: So over the last two years, we were able to collect for different data sets in four different times so as I told that our goal is to collect as many as near real time concurrent grounded as possible.

958
02:17:02.180 --> 02:17:09.650
Azad Hossain: With the real time image acquisition of land set satellites and Sentinel to and planet imagery.

959
02:17:10.160 --> 02:17:25.970
Azad Hossain: These three satellites provides multiple spectral data with different special revelation so kind of an always keep an eye when there is a good day and we have the email acquisition calendar if we have a good day and we have.

960
02:17:27.440 --> 02:17:34.730
Azad Hossain: The option to go in the field we try to take that opportunity, so we collected this for data sets and now we're working on that.

961
02:17:36.800 --> 02:17:45.230
Azad Hossain: So this study that we're presenting here this one focused on Sentinel to imagery that is.

962
02:17:46.970 --> 02:18:07.130
Azad Hossain: That coincides we are October 22 2019 field data so that means we we explore the potential of Sentinel two images, whether we can develop a remote sensing base model to estimate different water quality parameters, based on the field data that we collected during that time.

963
02:18:10.520 --> 02:18:28.640
Azad Hossain: Sentinel tool is a in European satellites and it has a collaboration with NASA it provides basically data from tools to our twin satellites at two images are equity, at the same time, and provides different spatial resolution but.

964
02:18:30.620 --> 02:18:47.210
Azad Hossain: The thing is, provides a good temporal resolution that is five days and the spatial resolution that we get 10 meter 20 meter and 62 on what we get data into a respected chance medical data and a professor lots of options in terms of their spectral capability.

965
02:18:51.350 --> 02:18:59.450
Azad Hossain: So here we have the table that shows the field that are that we collect it and we collected 50 samples.

966
02:19:01.430 --> 02:19:14.480
Azad Hossain: And the parameters that we measure the temperature chlorophyll conductivity do pH and rigidity and using that high to live hl seven songs and we try to.

967
02:19:15.920 --> 02:19:23.750
Azad Hossain: cover as much larger area as possible and our focus was to be uniform with the distribution with the samples.

968
02:19:25.640 --> 02:19:28.370
Azad Hossain: And after collecting the data that we found that.

969
02:19:29.810 --> 02:19:39.020
Azad Hossain: There are some issues with the samples that we collected to close to the bank of the land interference that when we look at the image that we have to, we have to.

970
02:19:39.530 --> 02:19:57.980
Azad Hossain: discard some of the samples to basically down to this 50 we are able to use for your of them to develop the models So here we have the data that we got on October 22 you can see that as a pretty good data and there's two things 110 meter another 20 meter.

971
02:19:59.120 --> 02:20:07.610
Azad Hossain: So it covers a significant area of our studies that doesn't cover the entire area but it covers enough area to develop the model.

972
02:20:09.980 --> 02:20:15.710
Azad Hossain: So here we can see the field data that we collected during October.

973
02:20:16.730 --> 02:20:25.430
Azad Hossain: That data that we use for modeling so in this map, you can see all the samples that we collected, and this is the area.

974
02:20:26.240 --> 02:20:44.540
Azad Hossain: And for developing models so we divided the data set into two parts, as a training data and testing data, the training data we used for developing the models and testing to test the models performance and here we have the separation of the training and testing data.

975
02:20:45.650 --> 02:20:46.580
We.

976
02:20:47.960 --> 02:20:52.010
Azad Hossain: create this group randomly so it was unbiased.

977
02:20:53.870 --> 02:20:59.630
Azad Hossain: Right so yeah we have the spreadsheet that finally we use for training and testing.

978
02:21:02.150 --> 02:21:15.020
Azad Hossain: And 32 samples for us to develop the models and eight used to test the models and basically what we did we use the training data to extract the reflectance data from.

979
02:21:15.650 --> 02:21:23.480
Azad Hossain: The Sentinel to imagery or four different manners and some examples of the reflections values that we have for like different bands.

980
02:21:25.010 --> 02:21:26.120
Azad Hossain: And then we.

981
02:21:28.100 --> 02:21:37.430
Azad Hossain: Try to develop a series of linear and long linear regression equation, focusing on single band, we tried to see.

982
02:21:39.530 --> 02:21:46.520
Azad Hossain: How the model performs specifically with each bands and we did not.

983
02:21:47.570 --> 02:21:56.630
Azad Hossain: explore the Multi temporal another multiple linear regression that we did not use multiple bands that's The next step, so this step, we wanted to see.

984
02:21:56.960 --> 02:22:12.320
Azad Hossain: What kind of relationship shape we have for each band, so we looked at linear and and both long linear and try to see what kind of our square we get So here we have the list of our Square, for example, for chlorophyll.

985
02:22:13.550 --> 02:22:26.690
Azad Hossain: Using the 10 meter data, then, here we have a conductivity is in the 10 meter data, we can see that chlorophyll we did not get very strong relationship, but connectivity to really get very good strong relationships.

986
02:22:27.290 --> 02:22:40.400
Azad Hossain: And then we have dissolved oxygen so we have a good relationship with in that case in both 10 needed and 20 meter pH we did not get we didn't expect to get good relationship pH because it doesn't have.

987
02:22:41.000 --> 02:22:55.040
Azad Hossain: An optical sensitivity and then ability we expected good too strong relationships we got that so we expect a little more, but we have about an order more than 80% for 10 meter and.

988
02:22:56.210 --> 02:22:56.840
Azad Hossain: Then.

989
02:22:58.760 --> 02:23:00.620
Azad Hossain: We also looked at.

990
02:23:01.820 --> 02:23:03.170
Azad Hossain: The relationships for.

991
02:23:05.300 --> 02:23:11.330
Azad Hossain: For 20 meter data sets to 20 meter data sets provided more bands and like about.

992
02:23:12.650 --> 02:23:18.380
Azad Hossain: Eight bands, that we can look at the relationships and lots of looked at the R squared.

993
02:23:18.890 --> 02:23:27.890
Azad Hossain: So i'm not going to go into very detail with these equations and just go over So here we have all five parameters equations the Finally, we got this.

994
02:23:28.310 --> 02:23:41.330
Azad Hossain: As an a five sets of equation for eight days, so we have equations for chlorophyll conductivity do pH and community based on their performance in terms of an R squared and here you can see that.

995
02:23:42.110 --> 02:23:59.690
Azad Hossain: The R squared that we get for this equations for this algorithm and pH is very poor and others expected we we did actually expect to get a little higher R squared for chlorophyll where we did not and that's probably.

996
02:24:01.130 --> 02:24:05.570
Azad Hossain: The number of samples that we have the visibility that we had in the field.

997
02:24:06.590 --> 02:24:08.000
Azad Hossain: We are looking at that.

998
02:24:09.050 --> 02:24:16.580
Azad Hossain: And this is about 20 meter we can have found the singular and performance in terms of spatial resolution but it's also important to.

999
02:24:17.750 --> 02:24:24.860
Azad Hossain: Look at that because 10 meters sometimes and provide higher and open opportunity to look at some.

1000
02:24:26.000 --> 02:24:35.450
Azad Hossain: In open and chimichurri so for Tennessee, but that is the small creeks that we cannot see that using laughs at that has 30 meters spatial resolution.

1001
02:24:36.020 --> 02:24:54.050
Azad Hossain: But saying that getting 10 meters, sometimes it also provide some noise there, so we like to see that what is the difference of 10 meter and 20 meter how much we actually gain with 10 meter and how much no error, we get intended so it's important to have both to analyze.

1002
02:24:55.670 --> 02:24:56.960
Azad Hossain: Alright So here we have this.

1003
02:24:58.100 --> 02:25:08.510
Azad Hossain: Applause all those selected equation so and we can see that chlorophyll you know the relationship inverse and all other religions are positive so and.

1004
02:25:10.460 --> 02:25:16.670
Azad Hossain: Just to look at that what level of difference we see in different types of relationships here.

1005
02:25:17.840 --> 02:25:18.620
Alright, so i'm.

1006
02:25:20.240 --> 02:25:31.820
Azad Hossain: going to the actual data that we we obtained from this models, here we have to add data 10 meters, so the our goal was to provide.

1007
02:25:33.740 --> 02:25:42.380
Azad Hossain: A safe seamless continuous again for the entire revert that we have within our studies and you can see that it is.

1008
02:25:43.550 --> 02:25:56.810
Azad Hossain: much better than the the scattered in situ measurements, that we can have in the field, so the question is the accuracy if we have higher accuracy and this is.

1009
02:25:57.380 --> 02:26:10.910
Azad Hossain: This is very, very useful for many different applications, so we have this 10 meter turbo velocity 20 meter every day, and then we have a 10 meter chlorophyll.

1010
02:26:11.990 --> 02:26:13.850
Azad Hossain: And 20 meter chlorophyll.

1011
02:26:15.530 --> 02:26:31.550
Azad Hossain: And i'm quickly going over that for the sake of time and the conductivity 10 meter conductivity 20 meter and then we have a pH 10 meter and a pH is actually that we have pH.

1012
02:26:32.660 --> 02:26:40.640
Azad Hossain: Is the other pH D or 10 bigger and do 20 meter and here we have.

1013
02:26:42.020 --> 02:26:46.820
Azad Hossain: The testing results you can recall that I we spared.

1014
02:26:47.840 --> 02:26:56.840
Azad Hossain: No age samples are randomly selected to test the models performance So here we have the results of the models performance and chlorophyll are a square.

1015
02:26:57.260 --> 02:27:09.620
Azad Hossain: is very poor and we're exploring this and, but we have very an open strong R squared for conductivity deal and turn to reality and.

1016
02:27:10.430 --> 02:27:22.250
Azad Hossain: The arrow arrow arrow is also quite reasonable and based on the range of the values that that we we have rights we have similar performance with 20 meter data.

1017
02:27:23.390 --> 02:27:35.060
Azad Hossain: Not much different than was expected yeah so when, and these are the observations that we we summarize from our preliminary results so Overall, we found that.

1018
02:27:35.420 --> 02:27:42.080
Azad Hossain: Results of reasonable and promising and for some parameters and we found the different.

1019
02:27:42.740 --> 02:27:58.790
Azad Hossain: parameters, the different types of numerical algorithms and that was expected and, and that is true for both linear and long linear and over equations so we found that estimation of timidity conductivity and do and achieve higher accuracy.

1020
02:27:59.840 --> 02:28:12.080
Azad Hossain: But the model did not perform well just in a chlorophyll mph and we expected to have higher accuracy for the chlorophyll though and, but we are exploring that how we can improve that.

1021
02:28:13.970 --> 02:28:23.720
Azad Hossain: Then we of course realized that we need more field data, we were able to only use 40 of them to develop them to refine the model.

1022
02:28:24.290 --> 02:28:38.720
Azad Hossain: And to achieve higher positions, we also need to use emails from other dates and other field data and to make them the model last that's what we're actually currently doing.

1023
02:28:39.920 --> 02:28:40.250
Right.

1024
02:28:41.480 --> 02:28:56.570
Azad Hossain: So that's all I have, I like to attend, you know the funding source of this and worker faculty prep work that I received earlier from an office of this justin sponsors Program.

1025
02:28:57.170 --> 02:29:05.840
Azad Hossain: And this research is currently being funded by tx and the Center for excellence in applied computer science and engineering so.

1026
02:29:06.950 --> 02:29:11.810
Azad Hossain: Then the rake and William they graduated.

1027
02:29:13.460 --> 02:29:17.810
Azad Hossain: From our program and both were heavily involved with this work they helped a lot.

1028
02:29:19.280 --> 02:29:27.860
In the field and processing it some initial data and we'd also like to thank and Copernicus for Sentinel to the good data set we got it for free.

1029
02:29:29.210 --> 02:29:31.010
said, thank you and I will.

1030
02:29:32.150 --> 02:29:34.760
respond to any questions that you have.

1031
02:29:56.900 --> 02:30:07.340
Kimberly Takagi: Well, thank you for your presentation um I was wondering if you could expand on maybe some reasons why you think chlorophyll didn't match up with the model, as you expected.

1032
02:30:07.880 --> 02:30:18.260
Azad Hossain: Yes, and you know the chlorophyll is we always expect a good results when the parameter is particularly sensitive so chlorophyll is one of them and.

1033
02:30:20.120 --> 02:30:30.260
Azad Hossain: The one of the when we were in the field we we look for the variation so the measurements, we found that the maintenance that will have our the 40 samples.

1034
02:30:30.740 --> 02:30:38.990
Azad Hossain: And within the middle of the Tennessee river so and usually there is no variation the variation we got a little bit when you went to.

1035
02:30:39.530 --> 02:30:51.380
Azad Hossain: That justin quicks when we have an open and higher little higher, but the variability is is that if we want to look for a statistical relationship that we'd like to get more more variability.

1036
02:30:52.160 --> 02:31:02.660
Azad Hossain: So, and we could what we saw in case of other models and I didn't talk about that here about Lancet basis study, where we were actually able to combine.

1037
02:31:03.080 --> 02:31:18.470
Azad Hossain: Two datasets two different times, we were able to increase and or the variability there we got a little better relationship in terms of chlorophyll but here we have only one day so maybe it will improve when, as we keep collecting the data.

1038
02:31:19.550 --> 02:31:20.690
So yeah.

1039
02:31:23.720 --> 02:31:25.520
Kimberly Takagi: Okay, thank you you're welcome.

1040
02:31:31.580 --> 02:31:38.240
Kimberly Takagi: We have another question from the avanti and she says, which reflectance band, did you use for chlorophyll.

1041
02:31:39.410 --> 02:31:49.850
Azad Hossain: Yes, and i'm just go back to we tried, all of them, we tried, all of them, and here we have the chlorophyll so we found the best.

1042
02:31:50.930 --> 02:31:58.310
Azad Hossain: R squared we get from the Van to of coal burning, so the band to have Coco knickers.

1043
02:31:59.300 --> 02:32:07.970
Azad Hossain: Here we have is it's a blue band that's what we we found this provide better stronger relationships, but no, we also.

1044
02:32:08.780 --> 02:32:27.860
Azad Hossain: expected to get a stronger relationships with from a near infrared usually, it is very sensitive, you know when you have no green algae on kinds of things and I think the reason we didn't have that, because the the value that we got from a chlorophyll was not very high.

1045
02:32:39.320 --> 02:32:47.450
Kimberly Takagi: Okay, thank you you're welcome, I think we need to move on to our next presentation, so thank you very much you're welcome, thank you, if.

1046
02:32:47.450 --> 02:32:55.580
Azad Hossain: You have any questions, please you know reach out to us by context the email, thank you.

1047
02:33:00.650 --> 02:33:02.660
Kimberly Takagi: Our next presentation is.

1048
02:33:03.290 --> 02:33:17.270
Kimberly Takagi: By travis simmons and he's from here at the College of coastal Georgia and he's going to be presenting on mapping episodic close on the dirty plane a pilot analysis, using a novel landsat eight and dwi processing tool.

1049
02:33:19.160 --> 02:33:20.300
Travis Simmons (he/him): You gotta call you Thank you.

1050
02:33:20.480 --> 02:33:20.930
11.

1051
02:33:22.280 --> 02:33:25.130
Travis Simmons (he/him): And that was an awesome presentation, thank you for sharing.

1052
02:33:28.310 --> 02:33:29.150
Azad Hossain: Thank you very much.

1053
02:33:29.780 --> 02:33:31.850
Travis Simmons (he/him): get my thing shared here.

1054
02:33:41.450 --> 02:33:42.830
Travis Simmons (he/him): Oh right you'll see that okay.

1055
02:33:45.230 --> 02:33:45.680
Travis Simmons (he/him): awesome.

1056
02:33:46.760 --> 02:33:49.190
Travis Simmons (he/him): So Hello everyone, thank you for coming I.

1057
02:33:50.480 --> 02:33:57.590
Travis Simmons (he/him): really enjoyed the talk so far and I hope you have as well, my name is travis simmons and undergraduate biology major the College of coast of Georgia.

1058
02:33:58.070 --> 02:34:04.760
Travis Simmons (he/him): And today i'm going to be talking about mapping episodic flows on the dirty plane using a tool caleb Tyler James Damien I developed.

1059
02:34:05.750 --> 02:34:14.810
Travis Simmons (he/him): US called land with so before we start to talk about this tool let's talk a little bit about what episodic flows are and why they are challenging but important to track.

1060
02:34:17.570 --> 02:34:29.360
Travis Simmons (he/him): So what are episodic flows well, these are inconsistent water flows that are often triggered by storms, so that means you know they're not there, most of the time, but then there's either heavy rain or others some other event, then they begin to flow.

1061
02:34:30.830 --> 02:34:46.520
Travis Simmons (he/him): And here, you can see our region of interest, which is a small subsection of the dirty playing these dashed red lines represent known areas of episodic flow and the polygons represent known wetlands and then on the right, you can see one of what one of those flows would look like.

1062
02:34:49.100 --> 02:35:01.310
Travis Simmons (he/him): So while we want to track these events well these flows are really happening in a bubble right these flows carry sediment and run off from agricultural fields in the watersheds all the way downstream, as you can see in the picture on the left.

1063
02:35:02.450 --> 02:35:08.330
Travis Simmons (he/him): This affect this affects the water chemistry of the wetlands and all the areas that these flows travel through.

1064
02:35:09.410 --> 02:35:25.670
Travis Simmons (he/him): So if we had a record of exactly when these flows were active we then look around those dates for look around those dates in precipitation records or and look for any mechanism mechanistic drivers to understand the dynamics of these systems and make more informed land use decisions.

1065
02:35:27.650 --> 02:35:38.150
Travis Simmons (he/him): However, recording these float events is time consuming and difficult because currently have to go there and see it and say hey this thing is flowing market check is flowing on this date.

1066
02:35:39.200 --> 02:35:45.050
Travis Simmons (he/him): it's all this is just not feasible to do if you're looking at more than one site over an entire county or state.

1067
02:35:47.240 --> 02:35:48.740
Travis Simmons (he/him): It will also take a lot of time and energy.

1068
02:35:51.200 --> 02:36:02.390
Travis Simmons (he/him): So also if you started recording these flow events you would still be limited in your data collection, because you don't have the luxury of going back in time and and checking these other previous days for flow as well.

1069
02:36:03.170 --> 02:36:16.070
Travis Simmons (he/him): So we were interested in creating a system, a systematic an automatic automated tool to identify pass episodic flows on the doherty points leveraging field freely freely available way inside a data set.

1070
02:36:17.330 --> 02:36:30.770
Travis Simmons (he/him): But there are many places where episodic vince episodic flow of instacart, many of which are very ecologically important and we felt the creating a generalizable and easily usable tool can benefit other researchers doing this type of work around the world.

1071
02:36:32.810 --> 02:36:41.210
Travis Simmons (he/him): But we chose to focus on this region of the door to climb, and for those of you aren't familiar with the dirty plane is a large and heavily researched area in the southwest corner of Georgia.

1072
02:36:42.290 --> 02:36:47.180
Travis Simmons (he/him): And what's special about the episodic flows in this region region is that they have I.

1073
02:36:48.530 --> 02:36:53.330
Travis Simmons (he/him): As you can see, on the right on the map on the right, these flows go through.

1074
02:36:55.040 --> 02:37:10.370
Travis Simmons (he/him): known wetlands, the small, isolated weapons these weapons hold a substantial amount of surface water in the area, so when they are connected by these episodic flows their water chemistry is not only influenced by the chemistry of the flow itself, but all the other ones upstream.

1075
02:37:11.420 --> 02:37:19.010
Travis Simmons (he/him): Another reason that we focus on in this area is thanks to previous data collection by Dr James D me and Dr Todd Todd wrath museum.

1076
02:37:20.450 --> 02:37:32.450
Travis Simmons (he/him): We had access to a validation data set of known flow dates from 2013 through 2009 same this was super useful because we could develop a tool and identify flow dates and then I.

1077
02:37:33.980 --> 02:37:39.710
Travis Simmons (he/him): identify floods during this time period and then use this data set to validate our our identifications.

1078
02:37:41.720 --> 02:37:52.400
Travis Simmons (he/him): And it's all good to say that we want to identify flow, but I, we were trying to automate this process and we were trying to revisit previous days, so we can't go out there and look at these areas.

1079
02:37:53.840 --> 02:38:00.320
Travis Simmons (he/him): If you take a look at the crop plant and the crop land self image on the Left there's no way you're going to be able to see in your water flowing and that image.

1080
02:38:00.800 --> 02:38:06.020
Travis Simmons (he/him): So we decided to use a processing technique called in dwi, which is the normalized difference water index.

1081
02:38:06.920 --> 02:38:13.760
Travis Simmons (he/him): Which is an image analysis technique, where you take two specific bands of an image and then combine them in the way this formula describes here.

1082
02:38:14.060 --> 02:38:17.690
Travis Simmons (he/him): And it turns a regular image, like the one on the Left into an image, like the one on the right.

1083
02:38:18.470 --> 02:38:24.050
Travis Simmons (he/him): This not only makes the image look really cool but also turns any water in the image bright red.

1084
02:38:24.500 --> 02:38:32.960
Travis Simmons (he/him): And as you can see here many folks in the area must have irrigated their fields right before this scan because you can see they're all kind of artists red red, which indicates that there's more moisture there.

1085
02:38:35.420 --> 02:38:48.260
Travis Simmons (he/him): So we could did run this process over many days of landsat scans and say on this day on the Left there's no flow and, on this day on the right there was, which I know they look exactly the same pretty much, but if we focus in.

1086
02:38:49.880 --> 02:38:57.890
Travis Simmons (he/him): If we focus into this region, right here, you can see there's a faint wine going through all these different out wetlands, which indicates that there was flow on the right hand side day.

1087
02:39:00.710 --> 02:39:01.670
Travis Simmons (he/him): and on there wasn't on the.

1088
02:39:04.550 --> 02:39:10.610
Travis Simmons (he/him): So before we decided to create this land with tool we were doing this analysis by hand and we found a few problems.

1089
02:39:11.510 --> 02:39:18.140
Travis Simmons (he/him): So when you get this boat downloaded from the usgs earth explorer site, which is where you can grab that data from.

1090
02:39:18.860 --> 02:39:23.570
Travis Simmons (he/him): It comes from these giant compressed files that you have to go and go through an uncompressed one by one.

1091
02:39:24.410 --> 02:39:33.590
Travis Simmons (he/him): Also, once you uncompressed them they aren't just your area of interest they come in these giant tiles where your area of interest, maybe only an extremely small portion, as you can see in the bottom middle.

1092
02:39:34.640 --> 02:39:41.810
Travis Simmons (he/him): So you not only need to crop these images, but you need to crop to have these images that have for each day because remember, we need two different bands.

1093
02:39:43.070 --> 02:39:44.420
Travis Simmons (he/him): To do this analysis.

1094
02:39:45.500 --> 02:39:54.050
Travis Simmons (he/him): Then, once you've cropped one there may be crowd clouds over the region of interest on that day, so now your uncle pressing and cropping doesn't matter you got to go to the next day.

1095
02:39:55.460 --> 02:40:04.970
Travis Simmons (he/him): Once you finally found all the days that aren't cloudy You then have to import them into an analysis software and run the dwi analysis, then you move on to the next day.

1096
02:40:05.720 --> 02:40:14.030
Travis Simmons (he/him): The outputs didn't have to be organized in some meaningful way in order to investigate flow dates, the point is it's a it's a lot of hands on.

1097
02:40:15.380 --> 02:40:26.030
Travis Simmons (he/him): involved involves time with a range of different software's and nuclear protocols this hands on time and lack of protocols, as what we hope to address for other researchers through land when.

1098
02:40:27.950 --> 02:40:39.140
Travis Simmons (he/him): we're going to do this by taking advantage some really great spatial analysis software and Python packages, such as QC is G doll open CV rest area and the usgs earth explore website.

1099
02:40:41.930 --> 02:40:47.060
Travis Simmons (he/him): We wanted to string all the software's plugins and packages together and and efficient and meaningful way.

1100
02:40:47.870 --> 02:41:01.580
Travis Simmons (he/him): Where we can automate them and we can do we wanted to container eyes this tool, which means they're running this tool and requires one dependency and we can run it on any operating system, including cloud computing platforms and high compute high performance computing arrays.

1101
02:41:04.700 --> 02:41:17.270
Travis Simmons (he/him): And although we automated nearly all of this process we're able to automate all nearly all this process there's a small amount of recommended setup that you need to do if you don't matter already had the GPS corner coordinates for your region of interest.

1102
02:41:19.820 --> 02:41:29.990
Travis Simmons (he/him): So one thing you would need to do is again use the uga usgs site to get the boat downloaded data you're going to uncompressed one of those files just one of them instead of all of them.

1103
02:41:30.470 --> 02:41:40.220
Travis Simmons (he/him): And then use the lat Lon tools to GIs plugin to find your Roi corner coordinates and then you just run line we with a simple command line copy paste command.

1104
02:41:41.330 --> 02:41:49.790
Travis Simmons (he/him): So I would like to go through really quick what would look like start to finish, for one of these analyses anywhere in the world that is covered by the wind Saturday data so.

1105
02:41:53.570 --> 02:42:01.010
Travis Simmons (he/him): Also, I would like to mention that this whole process of using language that i'm going to run through here is detailed in the readme of the official github which I can post in the chat.

1106
02:42:01.430 --> 02:42:18.080
Travis Simmons (he/him): Including any relevant links to software downloads so in order to get the data I we can go explore usgs site and choose the county or state that contains our ally, then use the highlighted drop down box to create a bulk data order for all of the scans that contain our Roi on.

1107
02:42:19.250 --> 02:42:26.780
Travis Simmons (he/him): This boat the boat download tool will then start downloading and I usually just leave this or run overnight so again minimal hands on time.

1108
02:42:29.990 --> 02:42:33.260
Travis Simmons (he/him): So, then, we have our data which is this wall of compressed our files.

1109
02:42:34.280 --> 02:42:46.760
Travis Simmons (he/him): which by the way, if you have a large time frame or a large region of interest, these are hundreds of these files, so I would recommend downloading them into an external hard drive because they're about one getting a piece, and then, when you compress them or even larger.

1110
02:42:47.870 --> 02:42:55.820
Travis Simmons (he/him): So, then you will mainly uncompressed one of those files and I inside that you'll find all the bands and one of the some relevant metadata.

1111
02:42:57.590 --> 02:43:02.210
Travis Simmons (he/him): you'll then drag and drop that into Q GIs and zoom into your region of interest in the image.

1112
02:43:02.660 --> 02:43:09.740
Travis Simmons (he/him): And then click the copy canvas coordinates button on the lat long tools plugin to copy the corner coordinates of where you're zoomed in and on to your clipboard.

1113
02:43:10.490 --> 02:43:18.980
Travis Simmons (he/him): Land we is set up in such a way that these copied coordinates from this exact plugin are in the exact format and order that it needs that script wants them.

1114
02:43:19.640 --> 02:43:27.650
Travis Simmons (he/him): To do the analysis, so you don't have to keep track of this at all you just copy, this is going to copy it on your computer flipboard and you just paste it into the to the run command.

1115
02:43:29.390 --> 02:43:34.610
Travis Simmons (he/him): So then, all you have to do is install singularity on your machine which is you can find documentation for.

1116
02:43:35.240 --> 02:43:42.470
Travis Simmons (he/him): And then run these two commands via the terminal on any machine was singularity installed again, this can be on any operating system.

1117
02:43:43.160 --> 02:43:49.910
Travis Simmons (he/him): The only thing you have to do is change the path to that wall of compressed files and then paste in those corner coordinates that are already on your clipboard.

1118
02:43:50.990 --> 02:43:52.490
Travis Simmons (he/him): from their land, we takes over.

1119
02:43:53.660 --> 02:44:00.740
Travis Simmons (he/him): So language is going to go through and uncompressed all those files for you it's going to crop it to your region of interest using G doll.

1120
02:44:01.070 --> 02:44:19.910
Travis Simmons (he/him): it's going to use computer vision to sort these crop images into cloudy and not cloudy it's going to use rest star yo to run in dwi analysis is going to label those outputs by date and that's going to create a time series gift I So you can see all those outputs in I an easily.

1121
02:44:21.110 --> 02:44:22.280
Travis Simmons (he/him): easily analyzed simple way.

1122
02:44:24.560 --> 02:44:41.510
Travis Simmons (he/him): So let's take a look at what these outputs look like at the annual have a file full of indie wi images they're just your region of interest, one for each scan date and named the date of the scan as well as the time series gift of the images that you see at the top right.

1123
02:44:43.250 --> 02:44:44.270
Travis Simmons (he/him): And these I.

1124
02:44:46.400 --> 02:45:00.380
Travis Simmons (he/him): These within sorted these into you I flow we're not flow days and we identified these dates, as the ones that we saw flow in and these aligns very well with our validation data.

1125
02:45:01.400 --> 02:45:07.100
Travis Simmons (he/him): So we believe that this tool and workflow is an effective and quick method for doing this type of analysis.

1126
02:45:07.970 --> 02:45:18.140
Travis Simmons (he/him): Now we can start to investigate these dates further and leverage other data set such as soil moisture data or participant precipitation records to help us understand the mechanisms involved in these episodic flows.

1127
02:45:20.540 --> 02:45:30.260
Travis Simmons (he/him): In the future, we would like to use this data set along with soil moisture data to train a semantic segmentation machine learning model so just the one pictured on the right to automatically categorize pixels in a.

1128
02:45:30.740 --> 02:45:40.520
Travis Simmons (he/him): land set images wetlands or not let lens or water not water, this would be useful for wetteland mapping and automatic automated episodic flow detection.

1129
02:45:41.510 --> 02:45:56.060
Travis Simmons (he/him): Also, we are currently integrating this tool into the publicly available cyber infrastructure platform servers as one of their vice applications, this will allow researchers to use cloud storage to maintain their data and avoiding any command line usage.

1130
02:45:59.930 --> 02:46:10.340
Travis Simmons (he/him): yeah so I want to thank a few people, I would like to thank Dr Todd Rasmussen at the University of Georgia for the input on the original ideas behind this research.

1131
02:46:10.970 --> 02:46:19.760
Travis Simmons (he/him): The usgs for precision, the provision of the public data, the final work or research team of the university Arizona for their help with the container ization of this tool.

1132
02:46:20.600 --> 02:46:26.510
Travis Simmons (he/him): And the geological society of America for making this conference happen, and everyone else that had a hand in doing that was much appreciated.

1133
02:46:28.580 --> 02:46:36.830
Travis Simmons (he/him): And there's my references, and again I thank you all for your time and I hope that you and your college could leverage this tool to accomplish your research goals.

1134
02:46:37.130 --> 02:46:45.170
Travis Simmons (he/him): And you've decided to give it a go or any run into any problems or have any questions feel free to reach out, and I can help you up.

1135
02:46:46.940 --> 02:46:48.200
Travis Simmons (he/him): If you guys have any questions.

1136
02:46:58.430 --> 02:47:00.650
Azad Hossain: travis's a nice presentation.

1137
02:47:01.070 --> 02:47:01.880
Azad Hossain: Thank you good work.

1138
02:47:04.370 --> 02:47:06.740
Azad Hossain: I was curious about your.

1139
02:47:08.450 --> 02:47:10.670
Azad Hossain: temp world sensitivity of the.

1140
02:47:12.020 --> 02:47:21.680
Azad Hossain: War like lance at like age 16 days to do thing or what you were trying to achieve that temporal frequencies okay.

1141
02:47:23.360 --> 02:47:28.310
Travis Simmons (he/him): Well, I think any increase in the temporal frequency would be very advantageous right.

1142
02:47:29.270 --> 02:47:42.980
Travis Simmons (he/him): And the only reason, this is anywhere specific to landsat eight data is based on which bands it's hard coded to pull out and just like you saw in your presentation, there you know, in the sensible to or any other.

1143
02:47:44.600 --> 02:47:53.210
Travis Simmons (he/him): Satellite imagery you have those bands freely available, so I was a little modification, this can be changed to any other any of those data sets.

1144
02:47:54.110 --> 02:48:04.010
Travis Simmons (he/him): That would be wonderful so yeah any of those ones that have more temporal resolution give us some more data points and I give us some more insight yeah that definitely would help.

1145
02:48:04.580 --> 02:48:08.720
Azad Hossain: Okay, so you said this tool is a fully automated now.

1146
02:48:10.010 --> 02:48:23.030
Travis Simmons (he/him): Yes, yes, so you still have to do that little bit of setup like I said to get those corner coordinates using kg is just so the tool has the information it needs and then knows where to find your compressed data, but everything else is automated and containerized.

1147
02:48:24.500 --> 02:48:26.330
Travis Simmons (he/him): Great Thank you no problem.

1148
02:48:29.060 --> 02:48:40.190
Corey Scheip: hey this is corey shy This is great travis this is cool dude did you explore using earth engine to do some of this type of processing Google earth engine.

1149
02:48:41.210 --> 02:48:42.200
Corey Scheip: Just curious.

1150
02:48:42.920 --> 02:48:47.750
Travis Simmons (he/him): No, I didn't I definitely have some what prophecy somebody.

1151
02:48:48.020 --> 02:49:01.790
Corey Scheip: yeah yeah yeah take take a look at it it's it might make this even easier for folks to use without downloading data and without having to do too much setup but what you're doing is great so it's just a friendly suggestion to check it out.

1152
02:49:02.330 --> 02:49:04.460
Travis Simmons (he/him): Okay, I definitely will be like making things easy.

1153
02:49:04.670 --> 02:49:04.880
yeah.

1154
02:49:12.020 --> 02:49:23.330
James Deemy: So quick note on the Google earth engine I did think about going that direction originally with this, but I really wanted to see if we could pull off download and.

1155
02:49:24.530 --> 02:49:32.720
James Deemy: Have the original data, because sometimes you do like having that original download and manipulating it on your machine, and this makes that a whole lot easier.

1156
02:49:33.860 --> 02:49:40.970
James Deemy: But yeah I know that Google earth engine does have its own dwi data sets and all that stuff already processed.

1157
02:49:41.960 --> 02:49:57.560
James Deemy: And I know exactly where you're coming from on that, but I was really hoping we can do this, and this was also a chance for travis to expand on his overall skillset travis normally works with lidar and hyper spectral imagery and some more advanced.

1158
02:50:00.290 --> 02:50:07.940
James Deemy: Programming and so I was, I was hoping to give him a chance to apply those skills to lands that just become familiar set.

1159
02:50:08.300 --> 02:50:18.110
Corey Scheip: Right right yeah and seeing everything yeah seeing everything that y'all have linked together, this is no small feat at all it's it's super cool that you have all these different platforms talking to each other so.

1160
02:50:18.860 --> 02:50:20.120
James Deemy: yeah and that's totally travis.

1161
02:50:20.150 --> 02:50:21.830
Corey Scheip: that's so far out of my league.

1162
02:50:23.630 --> 02:50:33.140
James Deemy: I had an idea let's use ND wsi because I was trying to use that during my dissertation and I beat my head against this fraser and travis did nine and a half 10 months worth of work and a night.

1163
02:50:33.650 --> 02:50:34.100
Corey Scheip: and

1164
02:50:34.130 --> 02:50:43.010
James Deemy: i'm not kidding go said he did an entire year of my dissertation research that never really went anywhere and I didn't end up actually using.

1165
02:50:43.430 --> 02:50:55.970
James Deemy: Obviously right we wouldn't have been doing this project, and he did it in a night and so he's now his project so, and this is a side project, this is not his wheelhouse is not what he does.

1166
02:50:56.990 --> 02:51:07.940
James Deemy: And, and the same thing with caitlyn caitlyn has coming up has her own set of stuff, and this is all side projects for both of them so anyways i'll shut up let you guys ask real questions.

1167
02:51:16.370 --> 02:51:26.390
Steph Shepherd  (she/her): I don't know if anyone else has any questions, but I just want to reiterate like how much work, I know that is i've been teaching myself Python for two years and I can't do that kind of stuff.

1168
02:51:27.680 --> 02:51:40.130
Steph Shepherd  (she/her): And i've been a professor for 10 years so that's really cool it's great to have people who are willing to dive into that and do the work, because then we can use those tools and we don't I don't have to go back and learn figure out the Python code.

1169
02:51:41.210 --> 02:51:42.080
Thank you so much.

1170
02:51:50.120 --> 02:51:55.640
Kimberly Takagi: cool well um if there are any further questions yeah as Trevor said, please feel free to email him.

1171
02:51:56.540 --> 02:52:05.870
Kimberly Takagi: very helpful Thank you travis for the presentation and we're going to move on to caitlin who also, as we said, is that the College of coastal Georgia.

1172
02:52:06.380 --> 02:52:16.010
Kimberly Takagi: And she will be presenting on evaluating maps of depression or wetlands on the dirty playing using lance at eight generated and dwi so it's all you caitlin.

1173
02:52:17.690 --> 02:52:21.350
Kaelyn Tyler: Thank you and thank you travis for setting me up so.

1174
02:52:21.350 --> 02:52:21.740
Kaelyn Tyler: nicely.

1175
02:52:21.950 --> 02:52:30.860
Kaelyn Tyler: Let me see if I can get this set up i'm also getting a bad Internet connection thanks, so if I go in and out just yell at me and i'll turn off my video.

1176
02:52:38.780 --> 02:52:52.460
Kaelyn Tyler: Alright, so I like to call you said, my presentation is on evaluating maps with professional wetlands on the dirty plane using lands at age generated and dwi data and I did this travel that I did this.

1177
02:52:53.480 --> 02:53:04.520
Kaelyn Tyler: This project with travis and Dr ED means well, so a little background and isolated weapons these isolated weapons are important because they start water and assimilate sediments.

1178
02:53:05.060 --> 02:53:15.230
Kaelyn Tyler: They transform pollutants and the small, isolated wetlands are all over the dirty plane and there occasionally connected by the storm generated episodic flow like Beatrice on jobs, you should.

1179
02:53:16.670 --> 02:53:32.870
Kaelyn Tyler: So our objectives for this analysis was to map wetlands using approximately six years a glance at eight generated and dwi images and evaluate potential wetland depressions identified through a dwi peak pixel intensities with no one locations.

1180
02:53:33.950 --> 02:53:44.780
Kaelyn Tyler: So again, our research site is going to be that naughty plane and, as you can see, on the right, our focal site we excluded all of the agricultural fields.

1181
02:53:45.320 --> 02:53:52.490
Kaelyn Tyler: Like you saw in travis's presentation, they present really high pixel intensities and we kind of skew the data that we're looking at so.

1182
02:53:53.840 --> 02:54:05.750
Kaelyn Tyler: Our data came from the usgs and it was all The Lancet eight data, so our sampling period was originally from 2013 to 2019 and we use the land, we tool.

1183
02:54:06.740 --> 02:54:18.110
Kaelyn Tyler: to extract the bulk download files probably images from move images of high cloud cover and generate the anti wi and and it was super quick super easy and really nice.

1184
02:54:19.760 --> 02:54:22.730
Kaelyn Tyler: Agricultural fields were eliminated in this process as well.

1185
02:54:24.020 --> 02:54:31.550
Kaelyn Tyler: So a little quality control on our data we needed to identify where known wetlands were so we could.

1186
02:54:32.150 --> 02:54:37.610
Kaelyn Tyler: Look at identifying wetlands that hadn't been charted first so we used a control set of.

1187
02:54:38.270 --> 02:54:46.640
Kaelyn Tyler: Know wetland data and kind of looked through all the images manually to identify those controls and see if we can see them.

1188
02:54:47.090 --> 02:54:51.080
Kaelyn Tyler: As the we kind of sorted our images into certain categories so we'll talk about a minute.

1189
02:54:51.890 --> 02:54:58.790
Kaelyn Tyler: And, and we did this, based on the assumption that if we could see are known wetlands if they were present as areas of high pixel intensity.

1190
02:54:59.090 --> 02:55:05.120
Kaelyn Tyler: than any unknown wetlands should also be present as areas pixel intensity and so from this assumption.

1191
02:55:05.450 --> 02:55:14.630
Kaelyn Tyler: That the images were separated, based on the visibility of these high pixel intensities and then agreements with the pixel intensity and the novellas.

1192
02:55:15.620 --> 02:55:24.590
Kaelyn Tyler: So as kind of an example, we set it up as like figure a where the controls are not visible with where there's either.

1193
02:55:25.310 --> 02:55:34.310
Kaelyn Tyler: Know areas of ethics low intensity, or they don't align with what we're expecting which could indicate cloud cover or a multitude of other things.

1194
02:55:34.880 --> 02:55:46.310
Kaelyn Tyler: And then we had our second image category of the controls are partially visible and, as you can see a B it's completely red, which means that it's most likely just complete coverage of cloud cover.

1195
02:55:48.200 --> 02:55:50.990
Kaelyn Tyler: And so, these pixel densities.

1196
02:55:52.040 --> 02:56:05.660
Kaelyn Tyler: aligned, but we can't really make them out from the rest of the images and then see where our controls are clearly visible and you can see that there's like isolated spots here, and here, where the placement entities are higher.

1197
02:56:06.560 --> 02:56:24.320
Kaelyn Tyler: And so we compare those two the known wetlands, to make sure that we could see on an island, and then we took those known well it's completely off and went year by year, to create shape files of just all of the areas that we would see that had high pixel intensity.

1198
02:56:25.610 --> 02:56:30.500
Kaelyn Tyler: And we only did this for the control visible data, because that was kind of our best data.

1199
02:56:33.410 --> 02:56:47.120
Kaelyn Tyler: To look at we set up those other categories, so that we didn't have enough images in that control visible data set, then we would kind of revert back to less data, but because we had enough data in the control, this will be kind of left the rest of that alone for now.

1200
02:56:48.530 --> 02:56:55.400
Kaelyn Tyler: This yielded shape files from 2014 to 2019 we had to exclude 2013 because none of that none of those data.

1201
02:56:56.510 --> 02:57:02.000
Kaelyn Tyler: know those images were present in the control visible data set, and that was either due to.

1202
02:57:03.440 --> 02:57:07.640
Kaelyn Tyler: Areas of icloud ever or just general issues with the data and the pictures.

1203
02:57:08.660 --> 02:57:17.240
Kaelyn Tyler: So from this, we made shape files for each year, and then we took the most conservative estimates of those weapons.

1204
02:57:18.110 --> 02:57:28.940
Kaelyn Tyler: based on agreement of those yearly shape files, and this was to normalize for periodic cloud cover so maybe we had one giant cloud that looked like it might be a wetland.

1205
02:57:29.360 --> 02:57:33.980
Kaelyn Tyler: But it was only present in one image, or we had periods of flood, where there was.

1206
02:57:34.850 --> 02:57:42.530
Kaelyn Tyler: A lot it was it's not necessarily a wetland but just an area of a lot of water that would show higher pixel intensity.

1207
02:57:43.040 --> 02:57:56.660
Kaelyn Tyler: And so we put all of these yearly shape files onto one map and use transparency to create a heat map to decide where these are mostly concert, so you can see, like the darker red spots are going to be the places where.

1208
02:57:58.310 --> 02:58:07.010
Kaelyn Tyler: The shape files overlap, the most and in that little zoom in part in the image, you see that there's some spots there's only one little bit.

1209
02:58:07.400 --> 02:58:15.110
Kaelyn Tyler: And then their spots where there's like a bunch of those shape files and talk to me for them and based off of this yearly agreement we decided on.

1210
02:58:17.090 --> 02:58:25.640
Kaelyn Tyler: These being the most highly conserved places of high pixel intensity, which would then our hypothesis is that it would indicate.

1211
02:58:26.240 --> 02:58:39.110
Kaelyn Tyler: Where a wetland is so we took this data and we compared it to our locations of our noble islands, so in figure a the blue outline show again Where are our control data set or known whelan's are.

1212
02:58:39.530 --> 02:58:55.070
Kaelyn Tyler: And in figure be is our heat map again and we see that all of our our no weapons and our focus I were highly conserved based off of our estimations of where those areas of iPad selling consumer so this gives us.

1213
02:58:56.360 --> 02:59:02.960
Kaelyn Tyler: This suggests highly that our areas of life excellent entity are actually indicating wetlands and not just.

1214
02:59:05.300 --> 02:59:11.120
Kaelyn Tyler: So from this, we excluded all of those areas of high pixel intensity that we're.

1215
02:59:11.780 --> 02:59:19.250
Kaelyn Tyler: Also mapped on our no wetlands and so now, all we have is areas that were highly conserved that weren't mapped and are known wetlands.

1216
02:59:19.670 --> 02:59:37.040
Kaelyn Tyler: And this, let us create two categories of hypothesized wetlands, our first one is going to be our most highly conserved and those are in red and we had about nine of those highly conservative estimates and those were measured on five or more out of the six years.

1217
02:59:38.300 --> 02:59:54.110
Kaelyn Tyler: As areas of high pixel intensity that were consistent and then the moderately conserved hypothesized wetlands were measured on three or more years from the shape files and that image is kind of small if you can see they're kind of spotted all around.

1218
02:59:55.520 --> 03:00:00.650
Kaelyn Tyler: And so here's it a little bigger, you can see that we have some that are up here and we left.

1219
03:00:02.600 --> 03:00:09.080
Kaelyn Tyler: The moderately conserved wetlands under the highly conserved to show that.

1220
03:00:09.620 --> 03:00:17.420
Kaelyn Tyler: it's most highly conserved at this, this is most likely are are consistent extent of those wetlands if they are wetlands, but then.

1221
03:00:17.810 --> 03:00:25.730
Kaelyn Tyler: It was also measured multiple times it being larger, so we have kind of a scale difference, so the implications of this is that.

1222
03:00:26.240 --> 03:00:37.580
Kaelyn Tyler: This the end dwi based has a digitized features suggest strong potential for mapping wetlands and dirty playing, and then the location of the identified wetlands.

1223
03:00:38.240 --> 03:00:45.710
Kaelyn Tyler: corresponds to the known, which will be useful in automating wetland mapping procedures like travis talked about in the last part of his presentation.

1224
03:00:46.580 --> 03:00:58.970
Kaelyn Tyler: And so, in future, research, we plan to use this to make a training data set for piloting and automated automate piloting automated procedures, using semantic segmentation model.

1225
03:01:00.290 --> 03:01:14.750
Kaelyn Tyler: And this will be used to attack wetlands within greater extent of the dirty plane so we're only looking at a very small amount, and the dirty playing kind of stretches pretty large so we're and that's our goal next is to use that model to detect where those wetlands might be.

1226
03:01:15.890 --> 03:01:25.100
Kaelyn Tyler: And finally i'd like to acknowledge, some people so firstly GSA for hosting the conference and allowing us to present and then.

1227
03:01:25.700 --> 03:01:39.410
Kaelyn Tyler: Dr Todd Rasmussen for the input on the original ideas, the USD for providing the public data and then college, because the Georgia department of natural sciences and geology club for supporting our participation in this conference.

1228
03:01:40.820 --> 03:01:44.420
Kaelyn Tyler: And that's it so if you guys have any question.

1229
03:02:05.660 --> 03:02:10.250
Steph Shepherd  (she/her): definitely have a hand up there, someone had their hand up if.

1230
03:02:10.670 --> 03:02:11.300
Travis Simmons (he/him): You want to clap.

1231
03:02:16.760 --> 03:02:18.050
Kaelyn Tyler: Thank you guys for letting me present.

1232
03:02:21.230 --> 03:02:32.180
Kimberly Takagi: And thank you for your presentation caillat um quick question um just because I honestly have no idea how any of this works um, how do you reconcile the ones that are.

1233
03:02:33.620 --> 03:02:40.880
Kimberly Takagi: episodic and not always there like How would you how would you map that or Kenya.

1234
03:02:41.690 --> 03:02:44.540
Kaelyn Tyler: yeah so that was that was part of the.

1235
03:02:45.020 --> 03:02:58.610
Kaelyn Tyler: distinction that I made between the highly conserved and the moderately conserved because our hypothesis was that the highly conserved ones are going to be the ones that are there over and measured over all the six years of our data or most of them and then.

1236
03:02:59.510 --> 03:03:06.440
Kaelyn Tyler: The addition of those moderately conserved, for we only see him on about three years of the sheet files would indicate.

1237
03:03:07.700 --> 03:03:15.080
Kaelyn Tyler: Hopefully areas of episodic flow that aren't necessarily there all the time and again it's kind of not.

1238
03:03:17.960 --> 03:03:29.960
Kaelyn Tyler: it's we're not accounting for as many episodic float wetlands, as we could, but this being a pilot analysis and our first attempt to this, and that was kind of the criteria that we went.

1239
03:03:31.670 --> 03:03:36.470
Kimberly Takagi: Through that and i'm guessing you can but can you see like how long.

1240
03:03:37.490 --> 03:03:47.150
Kimberly Takagi: These the ones that are there during episodic flow periods, can you see how long they're there versus not like to look at like I guess not residents time but like.

1241
03:03:48.290 --> 03:03:50.450
Kimberly Takagi: Established what light time versus not one time.

1242
03:03:51.560 --> 03:03:53.210
James Deemy: If you want me to take down.

1243
03:03:53.810 --> 03:03:54.590
Kaelyn Tyler: Yes, please.

1244
03:03:55.820 --> 03:03:56.630
Kimberly Takagi: I was just wondering.

1245
03:03:57.650 --> 03:04:01.220
James Deemy: If you could get a couple of consistent lance and images.

1246
03:04:03.170 --> 03:04:11.750
James Deemy: Could not consistent a couple of consecutive lance add images that were have good quality, you could get an estimate of the hydro period there.

1247
03:04:13.010 --> 03:04:18.770
James Deemy: It would give you an idea of how long they're staying wet and that that is part of what we're doing here.

1248
03:04:20.030 --> 03:04:25.400
James Deemy: The trick to that is getting consecutive landsat images the don't have a cloud on them and that's tough in Southwest Georgia.

1249
03:04:26.540 --> 03:04:28.220
James Deemy: In the winter, you can do it a little bit.

1250
03:04:29.420 --> 03:04:31.100
Well yeah.

1251
03:04:32.150 --> 03:04:37.880
James Deemy: Sometimes you can do it a little bit better and in the winter versus summer with afternoon thunderstorms and things like that, but.

1252
03:04:39.860 --> 03:04:49.070
James Deemy: The, the idea is it right now we're taking this kind of conservative approach to figure out which wetlands are definitely wetlands and then we'll.

1253
03:04:49.580 --> 03:05:06.380
James Deemy: expand out to a little bit more liberal classification scheme and some of the wetland portions that are less reliably inundated so they'll have a less a lower pixel intensity lower consistent high pixel intensity.

1254
03:05:08.780 --> 03:05:14.840
James Deemy: will then be included some as needed but we'll probably have to pull in a couple of different data sets.

1255
03:05:15.860 --> 03:05:16.520
James Deemy: To.

1256
03:05:21.770 --> 03:05:29.330
James Deemy: Properly map some of these wetlands, because in dwi looking at the the maps.

1257
03:05:30.770 --> 03:05:38.510
James Deemy: you're either really going to get a whale under you're just going to completely miss it and I know that there's wetlands on there that we're missing right now so we're gonna we're gonna have to do a little bit.

1258
03:05:38.720 --> 03:05:48.080
James Deemy: of drilling into that but, as far as a pilot analysis goes again here was another six months of my dissertation that we did in a couple of weeks now, thanks to these two and.

1259
03:05:48.800 --> 03:05:57.320
James Deemy: Where were they when I was University of Georgia, I don't know they were probably middle school so anyways yeah let's move on from that.

1260
03:06:03.980 --> 03:06:14.960
James Deemy: But if anyone has suggestions on this type of thing we're always welcome to it, and if you have anyone that's doing something similar, we would be either glad to help out or give feedback and vice versa.

1261
03:06:23.360 --> 03:06:30.950
Steph Shepherd  (she/her): I was actually thinking, are you guys familiar with the work that's coming out of university of Colorado boulder the earth lab group.

1262
03:06:31.730 --> 03:06:41.120
Steph Shepherd  (she/her): Because that's that's who I was learning I took three classes with them to learn Python and people students in their program do a lot of this kind of work out West.

1263
03:06:41.960 --> 03:06:51.290
Steph Shepherd  (she/her): So it might be really interesting to see what kind of work they're doing and see for comparisons, because what you know what one of the things I know about this kind of work is when you can compare with other people doing.

1264
03:06:52.100 --> 03:06:59.780
Steph Shepherd  (she/her): Similar process, but for a slightly different application, all of a sudden, you guys can like yeah yeah the ideas can go wild.

1265
03:07:00.860 --> 03:07:06.050
Steph Shepherd  (she/her): i'll send you guys there that the people's contact info the students, I know that are kind of working on some of this.

1266
03:07:10.400 --> 03:07:22.370
James Deemy: And a lot of this has sparked from travis having an internship with Arizona last summer and getting rolling on this Python programming and he has taken our research program to a completely different level here.

1267
03:07:29.240 --> 03:07:39.500
James Deemy: kayla is also a very good example of what a math minor can bring to the table in in a lot of our work, this project a little less, but some of the work that she's done with the socom data.

1268
03:07:40.280 --> 03:07:49.370
James Deemy: The project she just did is almost like working backwards for her because kayla normally takes point data sets and then creates an interpolated data set from those.

1269
03:07:49.700 --> 03:07:59.030
James Deemy: And then we analyze that image, instead of analyzing an image to try and to try and narrow down, and so this is almost working in reverse that's another reason why we're working on this project.

1270
03:08:17.480 --> 03:08:22.430
Steph Shepherd  (she/her): I was looking at our schedule, when we have a little bit of discussion time and concluding remarks shortly.

1271
03:08:25.760 --> 03:08:29.900
Steph Shepherd  (she/her): And then, so I don't know if anyone else has question.

1272
03:08:38.480 --> 03:08:48.710
Kimberly Takagi: yeah, we can also open up the questions I think to any of the presenters if anybody has questions for any of the presenters that they didn't get to ask earlier i'm you're welcome to do so now.

1273
03:08:59.210 --> 03:09:01.880
Travis Simmons (he/him): you're going to have a question for Dr Santa field online.

1274
03:09:05.120 --> 03:09:07.250
Azad Hossain: is still hanging out here yeah i'm here.

1275
03:09:08.870 --> 03:09:09.230
Travis Simmons (he/him): I.

1276
03:09:10.610 --> 03:09:14.330
Travis Simmons (he/him): I was just wondering, I know you said they kind of one of your next steps is combining the different.

1277
03:09:16.100 --> 03:09:22.370
Travis Simmons (he/him): Different bands of central to to try to get some better some better reading stories, but I can see, especially chlorophyll being.

1278
03:09:22.940 --> 03:09:34.970
Travis Simmons (he/him): You get some improve data from that for years stuff, but I have you decided on any like you know again you use like specific indices, are you just going to kind of go through like you did the other one try a lot of different combinations in some systematic way.

1279
03:09:36.500 --> 03:09:37.220
Azad Hossain: yeah I think.

1280
03:09:38.720 --> 03:09:59.150
Azad Hossain: So we're our next approach is to do multiple linear regressions and neural network of those little complex things involving first would like to see that is kind of a I can compare the sensitivity analysis like who's been more responsive and i'll.

1281
03:10:00.560 --> 03:10:01.010
pick them.

1282
03:10:02.900 --> 03:10:11.870
Azad Hossain: So that that is the plan that we had around the regressions and see which bands are sensitive to we pick them up and then we'll have the models.

1283
03:10:12.710 --> 03:10:27.050
Azad Hossain: And I think the bottom line is with, that is, that is, the list number of data or regionally, we started focusing on land set they told them help out of the four dates that we have three of them are.

1284
03:10:28.910 --> 03:10:39.290
Azad Hossain: concurrent with land steps, later on, we got some an opportunity to use planet data, you know the planet started providing data.

1285
03:10:39.770 --> 03:10:47.750
Azad Hossain: at very high resolution, like another planet is scopes provided a three meter resolution every day i'm part of the world, this very ambitious.

1286
03:10:48.230 --> 03:10:56.510
Azad Hossain: is literally you're having a drone on your on your side, all the time, so it's fun, it is not freeze commercial.

1287
03:10:57.140 --> 03:11:12.500
Azad Hossain: But they kind of have the they provided me data for resource for free, so I think they're doing that so so then we plugged in centennial data Center and yet I said, good resolution is the issue of the planet, like.

1288
03:11:13.520 --> 03:11:28.820
Azad Hossain: What do you do water quality remote sensing, we need to, we need to be careful about to using sophos reflectance we cannot use the digital number directly so so now Lancet and Sentinel both provide.

1289
03:11:30.800 --> 03:11:36.620
Azad Hossain: surface reflectance data as that I think Level two products, we have two records that and then.

1290
03:11:37.190 --> 03:11:48.380
Azad Hossain: planet also started, providing, but the results that we have another project that we're looking at the planet data planet, it has some signal to noise ratio pretty high because the higher is relations so.

1291
03:11:49.610 --> 03:11:52.550
Azad Hossain: yeah to answer your questions, the next thing is to.

1292
03:11:54.290 --> 03:12:06.230
Azad Hossain: The main goal of the of this project is to do something operational exactly what you were thinking with your work, so we don't have anything like that and i've been working with this thing.

1293
03:12:07.430 --> 03:12:20.120
Azad Hossain: More than 20 years, so all the water quality research that we can see out there they're published papers are based on very seasonal like you have a project, one or two year project.

1294
03:12:20.750 --> 03:12:34.310
Azad Hossain: That we got some data we got the results and published it and it's very difficult to develop some equations that works universally because the dynamics of the water is different and every water bodies, but.

1295
03:12:36.410 --> 03:12:44.450
Azad Hossain: Over the time if we have more data like that's what we have i'm anchor here at chattanooga so I have a resource vessels, I have the all time independent.

1296
03:12:44.900 --> 03:12:58.370
Azad Hossain: And earlier, I had projects and collaborating with different institutions, I could only achieve like to measurements in the entire year and it's very difficult to ask your colleagues hey tomorrow is going to be good day, can you give me your vessel so it's.

1297
03:12:58.370 --> 03:12:59.330
Travis Simmons (he/him): Difficult so and.

1298
03:12:59.360 --> 03:13:08.840
Azad Hossain: Then collect samples and allies in the lab that's also difficult, so I have some funds as able to develop this capabilities and i'm going to keep you know when, if you focus on.

1299
03:13:09.860 --> 03:13:21.110
Azad Hossain: That at all, but my plan is little ambitious to see that over the time and cover and Tennessee river with the more data that you have, then we can rely on more and more stronger relationship.

1300
03:13:22.610 --> 03:13:30.530
Travis Simmons (he/him): that's awesome that i'm really looking forward to seeing how that works with the network's trying to put all those images together that'd be that'd be a fun project Thank you yeah.

1301
03:13:30.590 --> 03:13:47.660
Azad Hossain: But, but your work is is interesting, so I can I am familiar with Google APP engine so that's, the last thing on the fly, but if you can bypass those heavy data sets then only download the portion that you're interested around this industry's although it's not.

1302
03:13:49.880 --> 03:14:02.630
Azad Hossain: It is it gives you a very quick preliminary results so that's that's very helpful, who can an interlock intimidated folks like you know they got to deal with like one year or two data loss of data, I think.

1303
03:14:04.280 --> 03:14:05.630
Azad Hossain: I like to see keep doing.

1304
03:14:30.560 --> 03:14:32.360
Steph Shepherd  (she/her): Any other questions or comments.

1305
03:14:37.400 --> 03:14:39.980
James Deemy: please feel free to grill my students that's why they're here.

1306
03:14:44.870 --> 03:14:47.120
Azad Hossain: they're doing excellent work, I can tell you that.

1307
03:14:49.910 --> 03:14:51.740
James Deemy: I cannot tell you how impressed I have been.

1308
03:14:53.540 --> 03:15:00.500
James Deemy: Things I tried for a decade to figure out in school they're they're figuring out a couple of years here it's it's been amazing.

1309
03:15:10.190 --> 03:15:10.400
Steph Shepherd  (she/her): well.

1310
03:15:10.940 --> 03:15:18.410
Azad Hossain: one suggestion that I have for you all different opportunity, you know, then B, if not probably only desert.

1311
03:15:19.100 --> 03:15:28.370
Azad Hossain: represented as vrs the American society of for the grommet and remote sensing Conference so you're going to see everybody's talking about remote sensing only remote sensing.

1312
03:15:28.910 --> 03:15:44.000
Azad Hossain: I typically Congress or you're going to find a big group of people like agu, so I think I know that's what you'd like to see that more people are talking about the same thing, so he says it's good so you feel more inspired like similar kind.

1313
03:15:47.750 --> 03:15:48.740
Azad Hossain: Of course, publish.

1314
03:15:50.570 --> 03:15:57.050
James Deemy: that's yes that's that's the stuff we're really looking to roll out at this point i'm caitlin did present at you this.

1315
03:15:57.110 --> 03:15:57.890
Azad Hossain: Okay okay.

1316
03:15:59.390 --> 03:16:01.580
James Deemy: Unfortunately, was the online agu.

1317
03:16:01.610 --> 03:16:05.420
Azad Hossain: yeah yeah yeah you don't get the feeling actually the in person is you feeling.

1318
03:16:06.500 --> 03:16:07.460
Azad Hossain: Is a massive thing.

1319
03:16:09.050 --> 03:16:10.430
James Deemy: But they're doing a good introduction.

1320
03:16:11.000 --> 03:16:11.600
yeah.

1321
03:16:13.070 --> 03:16:16.280
James Deemy: they're doing some really neat work with the Southern Ocean.

1322
03:16:18.080 --> 03:16:25.190
James Deemy: physical, chemical monitoring floats and socom data they have just run with and they've come up with some really neat.

1323
03:16:26.120 --> 03:16:42.710
James Deemy: Analysis of being able to figure out, you know where see is is a little bit more reliably then even some of the remote sensing stuff based on that physical, chemical data being recorded either below or not below sea ice and again that's where this math minor thing that I worked in.

1324
03:16:43.880 --> 03:16:53.420
James Deemy: Someone took linear algebra last semester, and then just completely ran away with the ideas and I let her so you know you never get in the way of a motivated students.

1325
03:16:57.260 --> 03:17:04.730
Steph Shepherd  (she/her): Well, it is 1105, and so I realized that people probably want to take a quick break and go manage life and lunch and.

1326
03:17:05.240 --> 03:17:13.670
Steph Shepherd  (she/her): or step into one of the other sessions if there's other things still going on this session continues in the afternoon it starts at 130.

1327
03:17:14.600 --> 03:17:22.280
Steph Shepherd  (she/her): So you'll just have to you know click on the link to join it I think it's technically the same zoom room i'm not really sure how that works, but I am.

1328
03:17:22.670 --> 03:17:30.530
Steph Shepherd  (she/her): I look forward to seeing everyone this afternoon Lisa Davis will be back this afternoon and I think she's mostly going to steer the ship.

1329
03:17:31.760 --> 03:17:35.270
Steph Shepherd  (she/her): For that session, and we have many.

1330
03:17:36.290 --> 03:17:42.260
Steph Shepherd  (she/her): oral presentations and one poster presentation at the end of the day and that'll go on till.

1331
03:17:42.650 --> 03:17:51.170
Steph Shepherd  (she/her): Little after four in the afternoon, so I look forward to seeing many of you there Thank you so much for a great session this morning, and all this interesting research.

1332
03:17:51.680 --> 03:18:00.710
Steph Shepherd  (she/her): And it went very smoothly and you guys are all great at being on time, like in terms of like i'd have to cut anybody off Kim didn't have to cut anyone off We appreciate that.

1333
03:18:02.210 --> 03:18:03.380
Thank you stephanie so.

1334
03:18:05.510 --> 03:18:05.930
Steph Shepherd  (she/her): Thank you.

1335
03:18:06.440 --> 03:18:07.000
Kimberly Takagi: For sharing Thank you everybody.