 putting that together, I forgot that one. Can you hear me? Okay, so hi, I'm Harrison. I'm a PhD student at the university here. My advisor is Greg Tucker, and my co-author here is Shannon Nahan at USGS. This is going to be some talk on my dissertation research, and I'd like to thank the USGS and the American Chemical Society for funding this. So to start, I'd like to say I'm really honored that I get to give this talk in front of you guys. I see everyone in this room as sort of this kind of modeler, you know, some really cool guy or gal up on the computer, all the science just blasting out. My opinion of myself is more like this when it comes to modeling and computer work, but I'm gonna try and show you some of the stuff that I've been doing. So I'm interested in fine sediment transport in rivers. And fine sediment transport is actually really important because it plays a key role in things like landscape evolution in terms of modulating how erosion can be promoted or inhibited. It's really important for our river infrastructure, and there's an example right there of a reservoir that's built in, and it's not a good reservoir anymore. And if we want to answer questions like, well, how do we expect things to, there are landscapes to change in response to climate change, like it helps to look back at the past and see what conditions were like before we started messing with things. So it lets us sort of compare with, we need more data on this. And this data is actually pretty difficult to obtain partially because there's a good chance that your sediment has been evacuated from your system or you need something like some sort of tracer, like a contaminant and so forth, and that doesn't always happen. So, also rivers are kind of jerks and so if we know more about them, they're not gonna pick on us. So, if we're talking about fine sediment transport on the long-term, one way that it's typically conceptualized is this idealized system between a river channel and its storage center. And in this environment, a grain of sand typically will travel some set distance. It'll deposit in a long-term storage center, sit there for years. It'll come up again and be re-eroded and potentially be stored for three periods of time before going back into these long-term storage moments. So, I've shown it simply up here, but these, the sort of LS, this characteristic transport length scale and the sort of tau S, this storage time scale are both based on probability distributions of grains moving from place to place. But if we're very, this one thing of sediment transport that I'm interested in is this virtual velocity, we take the ratio between these two, we can get a rough approximation of a time-average velocity of the grain moving through a system. So, I want to quantify this, but it's very hard to do so. So, trying to use this method called luminescence, which is kind of interesting and might give us a chance to get it, quantifying this sediment transport. So, luminescence is this really cool property where in grains of sand, it will charge up when it's in the dark and it will deplete when it's in sunlight. And the reason it does this is because of this property where in the crystal lattice of a grain of sand, of course, for example, there'll be background ionizing radiation which will displace electrons into defects in the crystal lattice. And now these electrons can only escape these defects if they're given an additional source of energy, which is usually sunlight. This is actually a property that's also used for age dating. But here, if we conceptualize, if we think about our grains moving through the system, they're going to see light some periods and then even dark others. So, potentially we can use this process to infer how things are moving through sedimentary environment. So, go about doing this, I'm so sorry. I went about performing a simultaneous conservation of energy and mass to develop a model. And basically how I constructed it was having our same idealized. So, it's conceptualized here as a sort of an idealized river channel and a storage center. And what I've done is I've said, okay, if we're gonna look at a handful of sand in the channel, there's gonna be a flux from upstream varying some luminescence. It's a flux downstream. It's taking the luminescence out of our location. The luminescence is gonna change a little bit due to sunlight exposure. And then there's gonna be a mixture going on as sediment is either positive or entrained by flow. So, and the equation down at the bottom is just sort of a mathematical expression of what I just said. If you take that equation and start going further with the math, that you end up with something that looks like this, you know, I think a pretty reasonable reaction to this is as such. But when you clear it up, you find that it simplifies. Oh my gosh, we can make some assumptions and collapse it down into a form that can teach us about what we would expect of luminescence to behave in a system. So we've got five parameters here and two terms. And so the first term, the L is the luminescence, the sort of bulk luminescence of the sand in there, X is downstream distance. That eta turn there is a, if you put like a handful of sand in the water and it's sort of going downstream, the percent that gets deposited and replaced by re-entrained sediment is that fraction right there. So some percent is being deposited in the rear room. And the LB there is sort of the luminescence that's in the storage center. It's been recharging and some new stuff coming in. The kappa and beta over there are sort of empirical parameters that describe how the luminescence disappears in sunlight. And then U is sort of a velocity in the channel here. And so what's useful here, and I've slightly just rearranged it a little bit to show this, but we have five parameters. We can isolate three. The two remaining ones are the ones we care about, which is the exchange rate and the inch channel velocity. And from those, we can calculate the length scale of the hop with those grains we're making. And then from there, we can get our virtual velocity, if you know what the storage time is as far as, which you can get just from using luminescence data. So okay, enough of that. If you're very interested in that horrible page of math at the paper and I'm not about shameless plugs, so there you go. But the basic predictions of the model are that, when we're upstream, we're sort of dominated by luminescence removal. And what we'll end up seeing is that as we go downstream, we should decrease in our luminescence. And when we get to our downstream reaches, that other term, the one that described the mixing, becomes more important. And eventually we reach a point where they level out and it's sort of a balance between the two interacting with each other. So to test this, I went to a river, this is the South River in Western Virginia, where that simplified model, that I showed earlier, the hops and rests kind of approach was shown to be a very good representation of the system. So if we go there and we try our luminescence thing, we can understand if we found a model that's useful to describe to try and connect those two. And so what we end up seeing, so the predictions here is on the left and then this is my field data on the right. The circles and different colors are two different flavors of luminescence. Some types of luminescence will deplete faster than others. So we can model two separate ones. And the solid lines on here are the best fitting model runs compared against them. And so to a first order, it seems that we've been able to recreate the shape of the data. The model, that model prediction seems to be filled. And if we take the best fitting model runs and compare them with literature values, is generally what I think is a good agreement. So here's a virtual velocity, the sort of rate of exchange with the storage center and that LS, the hop. This is sort of the mean value from the literature and this is the range and brackets here. And these are my luminescence results underneath. And for each of these, it seems like our results kind of fit into these ranges pretty well. Especially this one over here, the means, this matches up with the mean pretty well. And then same for the length scale over here, these seem to fit into the ranges that we previously observed in this group. So it's encouraging. I interpret that as being really encouraging. The next thing I'd like to discuss a model prediction is that if we suddenly started eroding something like a river terrace or something old that had really different luminescence, we might expect an increase as the rivers start to train stuff with more luminescence and mixes it together. So I took this to a place called Linganore Creek in central Maryland and this sort of domain is this channel, this basin is actually kind of interesting because the headwaters are sort of forested and when you move to the west, they become agricultural and then forest it again. So the yellow is agricultural land cover and green is forested. And as it does so, there's a lithologic control which increases the relief in these forested regions. And so from some hill slope modeling, we've done, we expected these hill slope areas should deliver a higher dose of unreached sediment. And so here's that same thing again, here's the predictions on the left and then this is our field data on the right. And so generally, if you look at the range between zero to 20 downstream kilometers, I've put in a dashed line there showing in sort of an interpreted relationship. And it seems to me like it's matching the model predictions fairly well. One complicating factor is that there is a sort of a large reservoir in the middle of it and we collected samples from downstream and those will break sort of our predicted trend here. So you see that dashed line kind of goes up, stays up where those samples are way lower. So that could be an indication that, okay, maybe my model's just wrong, but it also could mean that construction of the lake brought in a large amount of bleach material into the channel or due to the overflow of the lake, that also could expose grains to lots of sunlight and then the last test was to go somewhere with high amounts of the human development and land cover to see if a strong level of human influence would obscure the natural signal. So I mentioned earlier that I showed you that downstream decline and that is sort of the idealized natural state of the river. And what we end up seeing in these really developed areas or in this particular one, the luminescence is kind of weird. It looks like it's approximately constant with distance. I've put in two model runs here to maybe suggest what might be going on, but I don't really believe them. So the possibility is here that as you develop this environment, you dig up sediment, gets onto the surface of the earth, sees lots of sunlight and then gets moved by runoff into the channel and cop there. So it's further supported so these values right here that these data points come in are really low compared to everything else in those previous data sets. So I think that generally supports that idea. So to conclude, I think there's a lot of potential here with this method and some of my basic model predictions seem to be supported in some places where the model assumptions break down, especially in that last case that I gave you, it sort of limits the application of it, but you can do further work to see if these are reproducible and if they're applicable in other rivers. So thank you, thanks for your time. Thanks, Harrison. We have time for a question. So to me, this is really interesting and I just wonder if you could use the information about where the model doesn't hold if you looked at stratigraphic sequence to begin to identify periods of greater and lesser human impact on the landscape due to agriculture. Some people have already done that. There's this really cool new invention called a portable OSL reader where you can go out there and then... So if you go and look at this figure, you can see big jumps where you go from that sediment to... Yes, you said fine sediment, but I think your slide said fine sand. So I'm curious what range of particle sizes are you looking at, but also it seems like there could be shifts in the particle size moving downstream in large rivers and how would that influence this method? Yeah, for these rivers, the grain size roughly seems to be fairly simple. You're looking at that same fraction as you go, but as your river basin is larger and larger, those grains are shrinking. So what you end up doing with the model is now you're sub-sampling grains from when they were last that size. So to answer your question, there should be a point of which you would need the information to upstream to the grain to determine what size you're so close. But I'm not sure where that came from. It's kind of tough because you can't really apply the model to grain sizes bigger than that. There are problems with doing it on a common grain size, so it is a good question whether that grain size is representative. So the first data set was observed over a 60 kilometer long loop, and the next two were over a 20 kilometer long loop. So the length scales of the sediment hop to get the thing on your column. And so you can, depending on what you assume for the process, more area than the stream.