– Welcome everyone to Wednesday Nite @ the Lab. My name is Bea Mumm. I am a science outreach educator at UW-Madison Biotechnology Center. On behalf of the Biotechnology Center, UW Division of Extension, Wisconsin Public Television, the Wisconsin Alumni Association, and the UW-Madison Science Alliance, I would like to thank you for coming out tonight to Wednesday Nite @ the Lab. We do this every Wednesday night, 50 times a year. Tonight it is my pleasure to introduce Dave Hart. He is a hydrogeologist and professor in the Department of Environmental Sciences, University of Wisconsin Division of Extension. Dr. Hart completed his bachelors in physics and mathematics from Luther College in Decorah, Iowa. He served in the Peace Corps as a science teacher in Botswana.

He then earned both his masters and doctorate in geophysics from UW-Madison. Throughout his journey to getting his PhD, Dr. Hart held various positions as teaching and research assistants through the Department of Geology and Geophysics. In 2000, he completed his postdoctoral work through the UW-Madison Department of Geology and Geophysics and accepted a position as a hydrogeologist. These positions have led Dr. Hart to play a vital role in informing the decisions regarding water quality. He now focuses on both researching water and teaching other educators about his work. Tonight, Dr. Hart will provide a review of his water quality testing canoe, his journey to how he finalized this comprehensive and rapid method of testing, and the importance of this information. Ladies and gentlemen, please join me in welcoming Dr. Hart. [audience applauding]

– I’m gonna talk about some multi-instrument stream surveys we did on some small streams across Wisconsin, kind of the purpose, why we do it, and kind of what we found out, what was the value that we saw there. There’s a large cast of characters. I’m Dave Hart with the Wisconsin Geological Natural History Survey. I want to recognize Professor Sue Swanson from Beloit, Jake Westrich, a summer student from Beloit. There’s Jake in the back of the canoe. Catherine Christenson in the front of the canoe there. And standing there was a student in the Geoscience Department who really, much of what I’m doing here, maybe most of what I’m doing here, Catherine really led the charge on and did a wonderful job. Susie Richmond, also another student in the civil engineering. Designed a lot of the electronics that we used for this research.

Doctors Jean Bahr and Mike Cardiff and Dante Fratta are all professors at the UW. Again, I felt really fortunate to be able to tie in a wide cast of characters and their expertise. Andy Leaf at the USGS also provided a lot of really great input. All right, so the issue that I think we see facing us and this issue’s only going to become more acute is our natural resources are, the decisions we have to make are gonna rely on ever more complex modeling and data to support that modeling. And so for an example, there’s ELOHA. It’s got a really kind of a great name, ecological limits of hydrologic alteration. How far can we push the system before a species don’t want to be there anymore? We mostly worry about trout, but I think we could also worry about, I think this is a redbelly dace as well too. How much alteration do we see before we don’t see the creatures, before we lose diversity in our system? And so if we’re gonna do that, we may have a groundwater flow model that groundwater and the streams are connected. And so we may want to predict how that’s gonna occur. So let’s base, we’ve got a groundwater flow model.

Stream temperature is one of the most essential things to predict whether or not a fish species will be around. And so we put this into our model of whether or not a species is gonna be here, our stream flow, our temperature, land use, and we put in some of these other things. We want to get these models right so that we can do predictive stuff. What happens if we get more rainfall? What happens if the temperature changes significantly? We can work on doing predictions for this. And there are people in my office and across the university who are thinking about these questions. These models require more and better data to constrain the models. This is kind of an inside joke. This is often how people say geophysicists view the world. Data is equal to a model operating on some parameters, but we need observations if we’re gonna do modeling. We need good data.

And early on in my career, I did more modeling, but what I saw is that we have a lot of people, really good people doing modeling. And what I saw is also Cliff Thurber in geoscience has one of the best books on doing inverse modeling, fitting parameters to models, to data. Professor Dante Fratta, also of UW does this. Mary Anderson at UW, recently retired hydrogeologist and Randy Hunt at the USGS, all of these folks are doing modeling, they’re doing all this work on modeling and I thought well, we want to catch up. So why don’t I work on this side? This side’s getting a lot of work that’s working on getting more data. More and better data that we can support the modeling so we can support decisions. So the second one is okay, we’re gonna go out and collect data. Then the second one was, we need to be able to communicate our science and result, and don’t be such a scientist I guess can be summed up in don’t, you have to tell a story with your data. And one of the things, so we need to be able to communicate our science. In Wisconsin Geological Natural History Survey, one of the most important things we do is communicate our science to town boards and county boards.

I often think those people should all have geologic, should be forced to take geology in high school, because so many land use decisions are dependent on that understanding. And so we really kind of want to be able to communicate things. And so that’s one of the other issues that we want. Citizen science is also a part of that, getting folks involved in understanding and caring about the science that we have. So looking at those two issues that I’m dealing with and other people in my office are dealing with every day, there’s several trends that occurred in the last decade or two decades really since I started at the survey. Locating data is now easy. That’s where I was when I was making this presentation. I pulled up my phone, it opened up Google Maps, took a screenshot, and put it in here. It’s incredibly easy. 20 years ago, it was super hard to know where I got that data from, now it’s almost trivial.

20 years ago, what did I get, now I can get data within 10 feet. 20 years ago I was happy I can make a mark on a map, maybe it’s 100 feet, maybe it’s 200 feet is how accurate things are. Storage large amounts of data is low cost. In fact, it’s so kind of absurd, you kind of need this adapter so you can actually handle that 64 gigabyte thing. It’s so tiny, that little tiny thing is like a little chip. I’m glad they make the adapters, I’m not gonna be able to read that in a few years. Then the other thing is inexpensive videos everywhere. We can capture what we were doing really quite easily with our iPhones. Here’s a Hero GoPro and I just, I was gonna google GoPro on a canoe but I didn’t get far enough, and you can see what pops up. GoPro on a dog, turtle, fish, cat, shark, bird, catfish, eagle, RC car, and helmet.

So that’s another trend. 10, 20 years ago that wasn’t there. We can make use of this for our issues. And then it’s easier to develop prototype instruments now too. I am kind of on a lower budget where I’m at. And so we now have low-cost microcontrollers, a wide range of sensors that we can plug into them, and we can see how to build things on internet project hubs. You might not be so familiar with this, I’m gonna spend a little bit of time kind of going through that. That last bullet is, there’s kind of a maker culture out there, which is a combination of do-it-yourself hacker and artisan cultures. It’s a cut and paste approach to standard hobbyist technologies, and it’s really kind of a neat thing that we can go out and build it ourselves. If you google makerspaces in Madison, we end up with three.

UW-Madison Makerspace, The Bodgery, and Sector 67, and here’s where they’re located. And so I just went to the Bodgery’s webpage and pulled some stuff off of that. I’m not recommending anyone over another, but they’ve got woodworking shop, welding, blacksmithing, sewing studio, 3D printing, electronics lab, et cetera et cetera. And then we have this gentleman here who built a longboard. You can quickly and easily build a board that goes 30 miles an hour and 10 or more miles. It all depends on your budget. On his budget, he went one that can go 70 miles and up to 26 miles per hour. I’ll tell you as a father, I wish he had a helmet. [all laughing] I saw that and thought, “Oh God, okay. ” So then the internet’s also kind of revolutionized about what we, the things we can do.

I just google Arduino, which is a brand of microprocessor, and temperature, and I come up with all sorts of things. And this is just an example. And so this page explains what materials are needed, the code that you need to record temperature on the Arduino. We worked with this sensor a little bit, I’ve used it for some other things. But it’s accurate, waterproof, easy to install, and you only need a single port. And social media is kind of also part of this. You can click on “respect this. ” I don’t see it somewhere, but there’s a button you can click on, “respect this project. ” And so the more respect you get, the more, it’s a very similar sort of thing. But that’s another tool that’s available.

We didn’t have that available. Maybe you could look stuff up in a book. It was a lot more specialized. So the research question then, look, thinking about those issues, thinking about the changes that we have, we’re gonna design a method to collect spatial and temporally dense, that’s that low cost memory. Geo-located, that’s the, having GPS everywhere, sets of hydrologic data on small streams to understand stream quality and groundwater/surface water interaction. That’s that low cost sensor that’s gonna collect data to support our modeling. And so then, getting into background of what we do now and there’s no reason to change that, here’s one of my colleagues, Anna Fehling, up in northern Wisconsin collecting some water quality data. And she’s doing that the old-fashioned way. She’s at a point in the stream, she’s got a single measurement that’s kind of integrating everything going on upstream. And she’s got a sensor and she’s recording data.

And then she’s gonna record it in this very extremely stable platform called a field notebook. [audience laughing] These things are field tested, tried and true. They last for centuries. I’m not sure, the digital data is gonna require some more care to make that happen. So this is generally what we do, and it’s not gonna be replaced because we know where we were, we knew when we were there, we have all sorts of good data, and we know how to do this. So sometimes we’ll put a station in here, something that will just record over time as well too. And so the data volume and locations are limited by cost. It would be difficult to collect hundreds of measurements in a day on these streams. You can see it’s pretty rocky as well too. Okay, so with those things in mind, what were we gonna do? Let’s collect multiple datasets simultaneously from a canoe or kayak.

We’re gonna geolocate everything, we’re gonna collect it on the scale of seconds. So you know how a canoe goes about a meter a second or a couple of meters a second depending on how ambitious you are. And so we’re gonna be on a decimeter to meter scale, how fast we’re going. We’re gonna look at groundwater and small stream interactions. Groundwater because that’s what I’ve been trained in and something I can understand, and I want to understand how groundwater is interacting with these small streams. These small streams are most susceptible to impacts. They don’t have a lot of flow. When you get dry years, the headwaters may dry up and the stream recedes. Wet years, it’s gonna move around a little bit more. We’re gonna see the most variation in water chemistry, why? Because if you have a small spring flowing into this stream, you might get something measurable, a small spring flowing into the Wisconsin River, you’re not gonna be able to measure that, you’re not gonna be able to see it.

And they also support a large amount of diversity and native species too. So we can kind of tie all those things together. So we want this data to support groundwater models, and we are guessing that this stuff will be informative for other purposes. Maybe not something that a hydrogeologist will understand, but there are other experts out there who might use this. So this is what we are doing, this is what we hope to do. Just kind of pure and simple. We hope to collect a nice long reach of a river all in the course of a couple of hours. And so that’s what it was conceptually. This is Lulu Lake, here’s our stream that we’re going up. There’s our canoe.

You can kind of get a sense of what a really pleasant day this can be if everything’s going right. You can see Jake barely, you can maybe see Jake there smiling. Being in the back’s where you want to be, because you’re not worried about all the instrumentation. Catherine up front is not smiling because she’s trying to manage all the data and make sure everything’s working. So what five streams were we going to do? We’re gonna look at our criteria, we’re gonna look at previously studied streams: Marengo, Allequash, Little Plover River, Grant River, and the Mukwonago. These streams all had smaller flows, they’re all across Wisconsin in different geographic provinces. And so what are the parameters that we’re going to collect here? We’re going to collect water quality parameters, temperature, dissolved oxygen, pH, specific conductance, how well does the water conduct electricity, nitrate and chloride, and all that will be geolocated. It’ll all have a location, that long stamp on it. Ecological, maybe GoPro video, and then geophysical will collect how well the ground conducts the electricity, we’ll collect the depth of the water, and we’ll try ground-penetrating radar. So location, both all those will be geolocated.

And then we were gonna try RTK GPS, real-time kinetic GPS which is very accurate. It gives you centimeter horizontal and close to centimeter vertical. Now you just take your, boop, it’s really kind of magical for someone who had to use a surveying total station for the first 10 years of my career. This is kind of a magical way to go. That was so much more work, this is super easy. And the reason I wanted to do that is I wanted to get the stage of the stream. How does the stream elevation change as I go downstream or upstream? How do things vary? And so we didn’t do the ground-penetrating radar, we discovered that’s a ton of work, the batteries keep dying, it’s a lot of effort, and all we really are doing is getting a radar wave bouncing off the bottom sediment. We’re not getting a lot of information. So we said that’s too much work, we’re not gonna do it. We already have a lot of measurements we’re making, and we’re not gonna do that real-time kinetic GPS.

It sounds pretty cool, sounds pretty fancy, but now we have LiDAR. I can go download an elevation and get the stream elevation if I want. It’s available to all of us in this room for almost, I think there’s only a few counties that don’t have it available. So I can get the elevation of every stream and its gradient as I go. I would also say I kind of was, didn’t want to miss seeing either one of these go. This was maybe worth 20 or 30K replacement value, and this was worth 20K replacement value. We’re in a canoe, so I don’t want things to get wet. And so that reduced, that took away like two-thirds of the total cost of swamping. And this was also on loan from the USGS. And so swamping someone else’s equipment is also not something I wanted to do.

So we’re gonna collect a lot of things. I should say we’re following the work of a lot of other folks and folks from Wisconsin. For the geophysics professor who’s, Wu and Fratta did some work in Lake Superior. Bill from UW-Madison, Bill Kean and his student, Mike Baierlipp from UW-Milwaukee, did a study in the Fox River and Waukesha. Eric Compas from Whitewater and Suzanne Wade from UW Extension did a really neat study where they went down the Rock River and you could see the data come in in real time, and the water quality data, really neat study. And then of course there’s the FLAM group, fast limnological automated measurements from UW-Madison Center of Limnology and these guys have a national reach and reputation. They go all around and collect data. And then there’s video, I wasn’t the first person to put a GoPro on a canoe or a kayak. Those are generally relaxing. However, you can find many much less relaxing GoPro videos.

They’re absolutely terrifying GoPro videos as we go. So here’s kind of the canoe, kind of tricked out. This is one of our earlier surveys, we’re in the Waubesa Wetlands here. There’s kind of the, we’re sitting, Lake Waubesa is off to the back. You can see we’ve got our camera, our fish finder. This was an early one as I said. We don’t have the, we thought I was gonna have a little more heft to me than I did. We thought that was gonna be underwater, it wasn’t underwater. We wanted it to be underwater. So we fixed that, and here’s the EM31 measuring ground conductivity.

We’ll revisit this again here. The canoe’s plastic because we’re using an electromagnetic meter and we want to be able to get the signal through the canoe. Plastic canoe works, aluminum canoe not at all. And I think even, I wasn’t able to swing a nice sweet new Kevlar ’cause I think they still had even too much metal in them. So we went with all plastic, 100% plastic. It also allowed us to modify the canoe with a saw if we needed to, so we can fit more stuff in and around. It’s easier to work. Okay, here’s the fish finder and back. Again earlier on, here’s some zip ties, and here’s kind of our flow-through cell with pump and we’ve got some electronics. we’ll talk more about that a little bit.

Just want to give you a sense and there’s that fish finder. So first off, the first system if we want to think about it, was for the water chemistry. And so we used Arduino, there’s also a cleverly named Arduino’s, is an Italian company. There’s also, Raspberry Pi is another microprocessor that’s often used in science. And so that’s that open-source so we couldn’t use the Uno, which was the basic small model of the Arduino. We had to use the Mega. And so here’s the Mega here. We needed more ports and inputs with all of those water chemistry parameters that we were gonna measure in the location. So here’s the board, it’s gonna read all of the sensors and it’s gonna store the data. It’s gonna connect with the GPS and do all of that communication.

Here’s the little GPS, they call it a shield that connects on here, that’s the antenna. This GPS works way better than my phone and it cost, I think you can get this card for like $15. And you get something that’s really quite accurate. And then here’s the sensors that we measured, pH, nitrate chloride, conductivity, dissolved oxygen, and temperature. This one, I’m not quite sure that I’m done with this one yet. We thought we’d try Turbidity. That sensor is quite inexpensive. They’re mass-produced and if you have a dishwasher or a clothes washing machine, its turbidity, when is it done rinsing your clothes, when is it done cleaning out the suds, is being measured by a sensor like this. Kevin Fermanich at UW-Green Bay has told me he’s getting some decent data from a sensor like this that can read pretty low turbidity, how cloudy is the water. They can read water that looks pretty clear but still has some turbidity in it that’s blocking light.

And so we might crank that one back up. You’ll hear why we didn’t use that in a second too. This is something I can afford on my budget. If I were to go and buy a system like this, I’d be looking at $20,000, and it wouldn’t be geolocated. That said, it’s really nice to have somebody engineer things for you, because otherwise, we went through a lot of trial and error getting things to work, and you’ll see some of that. Our success rate in the beginning on getting all the data was maybe 50% or 20%. At the end, it was more like around 100%. So it was very difficult getting everything to work together. Here’s our flow-through cell. Water would be pulled out of the stream, flow into the cell here, outlet at the top, go through a tubing in this one out the top, tubing out in the top.

Why did we put it into three pieces? Well, it’s because the nitrate fights with the chloride, fights with the electrical conductivity. All three of these probes put out a voltage and so one, so they all think something else is going on, but it’s just the other probe interfering with it. So we split ’em into three and then we put a little copper tube that we grounded in between each one. And that pretty much eliminated the interference between the three transducers. And so there’s our flow-through cell where the water’s gonna move through, where this fills up with water completely. That’s gonna make our measurements. And then here’s our Arduino system. There’s the Mega, there’s the GPS, and here’s the, kind of all the signal conditioners for all the different things. This looks like a mess, but I was really pretty pleased with what a nice job Susie did with keeping it clean. But it still looks all right.

We also discovered in one of our trial and errors that the antenna does not work well when placed beneath the canoe seat with someone on top of it. [audience laughing] So we had to get an antenna, we got an external antenna and that improved the accuracy even more. It worked quite well after we did that. All right, the geophysical methods we put in the ground conductivity meter, again we used Arduino. This is an older unit, older instrument. They’ve been around since the 80s. This one has also been around since the 80s. But gee, it has a pin out here so you can record, it gives you the signal. Susie came up with a little, another Arduino system that measured location and recorded the data out of the EM31, and we’ll talk about that data in a little bit. Don’t get too concerned about that.

But again, it all just dumps it on an SD card, records time, data, and location. And that’s run off a little 9-volt battery, that’ll run it all day. The other water quality system that I just showed, that runs off a little six double-A and nine volts. That’ll run it all day as well too. And then here’s our GoPro camera, kind of standard. These things I think are kind of engineering marvels. It’s so tiny, can record a couple hours’ worth of video on a card, super-high resolution, 1440. At 30 frames. And so here’s an example of what we get with that video, with the GoPro. If you look there, that is a dogfish or bowfin and that’s from the Mukwonago, kind of on the upper part of Mukwonago.

I was like, “What is that big fish doing “in this little tiny stream?” But it’s apparently quite common there. Then the next one, let’s see if I can get this to go. [splashing] [all laughing] So you saw the canoe shake, that wasn’t from the mighty mighty beaver’s splash. That was from me reacting. I had no idea that thing was coming upstream. But again, I could tell you exactly where that beaver did that, if you really wanted and needed to know. So anyway, that’s how the video kind of works and looks. We just used an off-the-shelf depth finder with chirp sonar technology. This is probably the most sophisticated thing on the boat, and it probably costs just about the least, because there’s a big market for these things. I was a little disappointed.

I was hoping to get something that only went to like a few inches of depth, but apparently fishermen don’t need that information. [audience chuckling] So kind of stuck with, it kind of stops working at one foot depth, but it would have been nice to have the whole thing. And so that data is also geolocated and stored. That’s just part of the software package that you can get with them. Here’s the track, that’s where the location, here’s the data that was coming out of it. That red represents the stream bottom. So you can see the variation there. All right, just to sort of summarize how the canoe’s set up. Here’s Catherine and Susie again. We have a camera in front, we’ve got one in back.

This time below water, we learned something. We’ve got fish finder here connected back behind. We’ve got a water inlet taped with Gorilla Tape, that stuff works great. It’s temporary, it’s perfect for what we want to do. Taped underneath on the bottom of the canoe, we’ve got a pump here that moves the water. It goes to our flow-through cell tucked under the front seat. You can see it sitting right there. Comes out of that flow-through cell and gets ejected here. And then we’ve got our EM31 strapped in the canoe. And seven batteries that can’t fail for two to three hours, which is a harder test than it might sound right there.

Again, we got good at managing our batteries toward the end. So how do we know how well our data quality, our assurance? We did three things, we calibrated our sensors, we had a standard protocol before every survey, depended on the sensors, we compared to grab samples that we just collected while we were going down the stream where we just collected the bottle and a sample and sent it to a lab for analysis. And then were the data reproducible? We’d go upstream, then downstream. Or you circle the lake clockwise, and then you turn around and go counterclockwise. A lot of times we found like, temperature varies when you do this, but the other parameters don’t. The sun warms things as you go through the day. And the temperature is also one of the things we didn’t worry about quite as much. So here’s kind of our, here’s our measurements, and here’s our calibration procedure. We just used standards and kind of did what the manufacturer recommended, and what we kind of thought we needed to do. But we did this before every trip.

Here’s laboratory values versus survey float values. Here’s the conductivity micron lab sample, is shown in red. And the stream float samples are shown in blue. And the time that those things were collected is shown along the bottom. You can see there’s a spread in the stream flow and in the float data. That’s because there’s a little bit of a lag time getting through those flow-through cells as we go. And so there is some smear in our data. When I started I was like, “We can measure this “every tenth of a meter. ” I think we’re actually, because we have the smear, we’re more like measuring things every five, several meters, maybe even 10 meters depending on how fast we’re going. The slower we go, the better our accuracy.

So I’m still pretty happy with 10 meters, that’s pretty good location and can really help us inform things. One of the things we found is the chloride probe didn’t work very well. It was fussy, and that kind of shows up with the data. We’re getting okay matches but not. . . This is about as good as the chloride probe got. It’s an ion-specific probe, and they’re just not quite so easy to use and get great data out of. And then reproducibility. So we circled the lake, so we started here, this is Plainfield Lake about a year ago.

So we started here, we went counterclockwise, got to here, and then went back. When I say we, I mean Susie and Catherine, I stood on the shore, answered email or did something, I don’t know. And so you can see that we’re looking at dissolved oxygen, where green is lower saturated percentage and red is higher. And you can see those two things are pretty reproducible. There’s a pretty low dissolved oxygen over on this west side. The biologist at this site suspects there’s a lot of decaying plant matter here. This lake was nearly dry five years ago. There’s a lot of vegetation there. That’s now very much under water. So it’s probably, the DO, there’s a lot of biological oxygen demand going on here.

So we felt good about that measurement. Here’s the Grant River track that Bea was talking about. And apparently Bea’s been down this several times. So here’s the Grant River. We’re gonna look at specific conductances. The red points are where we collected samples. And you can see the track here, and then the specific conductance is related to the amount of total dissolved solids or salts, and it’s kind of a proxy for water quality. If you’ve got really low specific conductance, often that water’s of higher quality than very high specific conductances. And this we think illustrates the value of collecting the continuous data. So keep those points in mind.

Here’s what we got from the lab samples. Here’s our scale, everything’s kind of bracketed between 5 and 600 microsiemens per centimeter. You don’t have to worry about the units there, but that’s a pretty narrow range. That’s about our drinking water in Madison a lot of the time. Maybe it gets down to 400 depending on your well. But then if we look at its extent, up here where it’s blue, it’s lower. Blue is lower values and yellow is higher values. So you can see there’s a peak here and a peak here, and this is really kind of the lowest. So this took like an hour to collect, and we can already see something’s going on here, and something’s going on here and even down here. We didn’t really test this much but this is, there’s a big spring, beautiful little spring house park called Klondike Park down in Grant County.

I highly recommend checking it out. But you can see if we look at that data plotted with time again, so here’s the conductivity versus time rather than distance, you can see there’s a lot more going on in that data in those streams than was just recorded in the five samples. I wouldn’t do one without the other always, but they’re really complementing each other right there. The fact that most of these are lining up pretty well, this one, don’t know what happened here, but that gives me more assurance in the data quality as well too. And so where do we see those peaks? We see the peaks where Rogers Branch comes in. Let me go to the next slide. There’s a small, unnamed tributary here and Rogers Branch comes in here. And so those two have a little bit lower water quality as they join in, and so that’s these two peaks. And if we zoom in, here’s Borah Creek with the lower specific conductivity. Rogers Branch has a higher specific conductivity.

I can tell you which tributary to the Grant River I’m gonna go trout fishing in. I also know that there were trout fishermen on Borah Creek whenever I was there. There were quite often trout fishermen. So this is a little bit higher quality water and we can see that, and we can see that comes in and then as it goes down, there’s some mix, we finally get some mixing and get to an intermediate value. So that’s the other thing. We can get a sense of how long it takes for things to mix in the system too. If we look at the other. So let’s move on and look at the Mukwonago Watershed, is kind of another one of our examples down in southwestern Wisconsin here, or southeastern excuse me. And this is really kind of a pretty, kind of a gem of a small little stream. Here’s an example on some nature conservancy land.

Here’s a couple of images that we collected with the GoPro going here as well too. So if we look at temperature. This up here’s a beautiful little spring pool or actually a very large spring pool. We went down, this is actually Crooked Creek. I think this is unnamed and when we get to here this is, we call the Mukwonago. And so we start up here in the spring pool, go downstream, and then out across Lulu Lake and out at a landing there. And so let’s see if this will start. And so you can see in that spring pool, there’s a lot of groundwater discharge, just beautiful crystal clear water. The other thing to note is this water’s cold, as you might expect groundwater to be when we did this in June, late June. It’s a warm time of year.

Groundwater is cold and it warms up as we go downstream, and we have a record of how things warmed up as we went downstream here. Went from 11 degrees C up to over 20 in the lake as we go. So you can see cold and we can see how it’s warming up. All right, now if we look at the next. Next one is dissolved oxygen. The groundwater generally is depleted in oxygen, the oxygen reacts with the minerals, and so if it’s been in the ground for a long time, usually dissolved oxygen is pretty low. This is kind of intermediate, so it’s blue, it’s this kind of light blue, and then it goes up. Green is higher dissolved oxygen. Because cold water can take more, can hold a higher percentage of oxygen. So this water is still cold, it hasn’t warmed up yet.

So colder water has a higher percentage of oxygen and now it’s warming up and we see the oxygen drop. And there’s probably also a biological component to this signal as well too, that I would like to eventually tease out, and then the lake’s get a little bit higher as we go. The specific conductance is high in the spring pool. There’s a lot of dissolved calcium carbonate, the water’s a calcium bicarbonate water. And so when that comes to the surface, the CO2 is out of equilibrium, it shifts and you get mineral deposited. And so you have a high specific conductance because you’ve got a lot of calcium carbonate dissolved. As you come into equilibrium with the atmosphere, that calcium carbonate’s gonna come out of solution. So the specific conductance is going to go from a high value with lots of ions to a low value with fewer ions, and that’s what we see here. I’m very curious about what’s going on. There’s a railroad trestle here kind of separating these two, and there seems to be a jump.

I’m very curious about what’s going on there. There’s another interesting signal that we see with the pH. Again the pH when it comes out, it’s slightly acidic, becomes more basic. Blue is more acidic, green is more basic. So it’s slightly more acidic. That’s the excess carbonic acid, the carbon dioxide in the water. That comes out of solution, things become more basic. And then we get this funky signal again down here, and I suspect that isn’t have anything to do with geochemistry. It’s probably biochemistry. There’s probably some sort of biological activity making that more acidic.

We would have no idea about any of these questions except for we’ve got too much data perhaps. Model limitations. So this is not a total panacea, there are limitations here. Site accessibility and conditions. This is not an awesome place to take a canoe with lots of expensive equipment. Here was Catherine’s solution for this location. She’s got the battery and the pump in a backpack, and she’s got her flow-through cell here doing the measurements. This worked okay but again, you’re not going a meter per second like you are in a canoe. It’s hard to cover a large amount of territory. Tag alder is not your friend when you’re trying to do this.

You may have a lot of stream that you want to get through, but you need to get out the loppers if you really want to get through that. And then I’ve got, over the couple of years of this project, we’ve taken a lot of ribbing about the flotation devices that are filled with certain beverages behind the canoe, which we never did. But I just want to show those people this picture. It’s not all fun and games all the time. You can, mosquitoes. On this day it rained all day, and we were in northern Wisconsin and mosquitoes were in full hatch. And so you can’t get comfortable. Again, I showed up after the end of this day and met these guys for lunch and they were, and one of my colleagues, Pete Chase, who’s an extremely experienced field person, actually said “Dave, that was actually a really hard day,” and he doesn’t ever say things like that. So you can see Catherine’s looking a little ragged there. So that’s the water quality, kind of the summary of what we saw with the water quality.

If we look at stream bed electrical conductivity, again, electrical conductivity is how well does the material conduct electrical current? And we’re gonna use this to maybe let us know what’s underneath the streambed. A higher electrical conductivity indicates more clay, silt, and organic material, and that corresponds to a low hydraulic conductivity. The water can’t move easily through these materials so it’s kind of inverse. High electrical conductivity, low hydraulic conductivity. Low electrical conductivity, sand and gravel gives us a higher hydraulic conductivity. So the idea is then with this tool, we can map the entire stream and get indications of where there’s good connection to groundwater, ’cause we have the streambeds at high hydraulic conductivity, low electrical conductivity and vice versa. All right, here’s kind of it laid out again. We’re getting ready to go. This is in Borah Creek, upstream there, and here’s again the unit. How does this thing work? Well, it seems a little magical.

You don’t need to put anything into the ground. It works because you have a coil on your transmitting end. That coil has an alternating current through it, that current produces a magnetic field, that alternating magnetic field produces current in the ground. That current in the ground produces a magnetic field that the receiver sees. So this is, and what you do is you have to, the electronics mostly are trying to separate out the magnetic field from the transmitter from the magnetic field in the ground. They’re shifted in phase so that helps. But if you get more induced current in the ground, that means you have a better connector. So this is kind of a wonderful tool, it’s very mature technology. It’s been around for a long time, and you can just kind of float over the water with it and it will record the streambed conductance. There are issues though because we’ve got this, before, we’ve got this water in the way, and it’s going to contribute to the overall electrical, to that signal that we see.

But we’re lucky because we’ve measured the water conductivity with the water quality with the Arduino thing and recorded it, and we’ve got a depth finder that recorded the depth. And so with these two things, we can remove the waters, the stream’s contribution, the stream water’s contribution to the EM reading. What we have left over is the streambed electrical conductivity. And here’s that for the Grant River. We collected sediment samples at all these locations and I’ll show you, we’ll look at 11, 12, 14, and 15. Low electrical conductivity is blue, that would be, we would hope that’s more sand and gravels. High electrical conductivity is yellow. Those are the silts and organics. And so yellow, that’s not any place I’d want to step out of the canoe. Yellow, that’s maybe not quite as bad, but still a little mucky.

And then we’ve got gravels, which I’m gonna want to make sure I want to have sandals at 11 and 15. So we do see a pretty decent correlation. These are probably the best examples, often it’s a little more subtle than that. But in general, we can correlate electrical conductivity to sediment and we know that’s gonna correlate to how well the stream is connected to the surface. All right, and then the last piece, and this is maybe the piece that we, that’s maybe unique to what we’ve done, is putting it all together. And we did that using a software called Dashware, Why would anybody use software called Dashware for a ecological study? Well, it’s made to take your race car, put your GoPro camera on your dashboard, and record the g-forces, record your velocity, record your lap speeds, all these things, and then play it for your buddies in a video. I thought well, instead of g-forces and velocities, we’ll do dissolved oxygen and electrical conductivity and we won’t worry about g-forces. So we can put this in, it’s a bit of a stretch, you kind of have to fool the program a little bit. There’s another one called Race Render that we’ll also use too. And so here’s the Grant River survey on the YouTube, and I’ll just show a little bit of that movie.

Yeah, and so here we, this is the same data that you see.

– Woman: All right.

– Dave: So here’s us getting ready to go, and we’re going downstream. So let’s go down and look, so you can see here we are up in Borah Creek. This is just the time, you can see this video is about an hour and a half long. As we go here, this is water temperature, WT is water temperature. This is electrical conductivity, that’s the fluid electrical, water electrical conductivity, and then here is dissolved oxygen as we go here as well too. So you can see how these things are varying as we go down the stream. So I can, and one of the other things is I can move this ahead, and you can see right at this fence, we kind of go from high dissolved oxygen to low dissolved oxygen. I don’t know what is going on and why we saw such a dramatic change there, but that’s what we saw.

So the dissolved oxygen goes low. Then we’re gonna see a bunch of spikes here, and I think those spikes have to do with the riffles. So if we go along, maybe the dissolved oxygen goes up, the electrical conductivity is still pretty low, but it’s higher. We were up in Borah Creek and now we’re downstream a little bit more, it looks like our water quality’s decreased a little bit. Now let’s go down a little bit farther. And you can see that big jump in water temperature here as we’re going, we’re kind of going. Things seem kind of stable. I think this riffle is gonna give us a little pop in our dissolved oxygen. I don’t think we’ve got an hour and a half here but. .

. [audience chuckling] And so you can see, that’s going up, and then we’re gonna cruise along a little bit more. We’re gonna go around a corner, and I’m gonna get us kind of right to where, so you can see the temperature’s gonna jump up and the electrical conductivity is also gonna jump up. And we’re gonna have low DO. And what are we doing? Right there, we’re in the confluence of Borah Creek and Rogers Branch. There’s Rogers Branch there. As we go right there, we’re gonna go up Rogers Branch and you can see the water temperature is significantly higher in Rogers Branch. And then we’ll back out of Rogers Branch and then we end up going downstream. And you can see the temperature’s kind of a mix of Borah Creek and Rogers Branch. It’s kind of in between.

Kind of similar with the electrical, here’s Borah Creek, Rogers Branch and we’re kind of in between. So we can kind of see the mix, we can kind of see how these two things are interacting. And then our DO went back up right there too. It went back up a little bit more. If we keep going down, we can see another little spike in that electrical conductivity. Remember I mentioned a small unnamed tributary. So let’s see if we can get to it. You can see it’s gonna show up right here. This little tiny tributary is gonna be responsible for this bump in electrical conductivity, and we stayed here and just collected data for a while, so we could get a sense of it. So that’s really kind of the highlights of this video, and kind of the power.

We did a similar thing for Plainfield Lake, a similar thing for the Mukwonago as well too. Kind of giving us some insight into how things vary spatially, which was kind of the whole plan and idea for the study. Okay, let’s go back and see where we’re at with the presentation. If you want to spend more time, you can google Grant River Float and I think this will show up on YouTube. You probably can’t click that link, but if you google Grant River Float, you’ll probably find it in YouTube. All right, so we developed a method that’s low cost, easy, pretty easy to process compared to other methods. It provides guidance. We know some places that we’d like to instrument on the Grant River now. We have some idea after doing those surveys. Maybe these repeated floats can help us understand variations in quality under different conditions, and I think the movies help me understand what I’m looking at a lot of the time.

They also help me remember what we were doing too, it provides a good record of the survey. I’d like to last acknowledge the funding source, the Wisconsin Department of Natural Resource funded this project through the Groundwater Coordinating Council’s Joint Solicitation Program. So that’s what I’ve got, thank you. [audience applauding]