How to Interpret Hydraulic Conductivity Data

How to Interpret Hydraulic Conductivity Data

Application expert Leo Rivera discusses how to ensure you’re getting the most thorough and meaningful insights from every data set.

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You need to understand how water is moving (or not moving) through your soil. Gathering precise, accurate, and timely data is the first hurdle, which can be conquered with the right instrumentation. But how do you make sure you’re getting the most thorough and meaningful insights from every data set?

In this 30-minute webinar, METER research scientist Leo Rivera explores examples of hydraulic conductivity data you might encounter during your research and breaks down what to look for, what to avoid, and how to reach the most insightful conclusions your data has to offer. In this webinar:

  • Learn how to interpret hydraulic conductivity data
  • Take a deep dive into SATURO data and how to make the most of it
  • Explore data collected in the lab vs field
  • Examine impacts of land use and soil health

Leo Rivera operates as a research scientist and Director of Client Success at METER Group, the world leader in soil moisture measurement. He earned his undergraduate degree in Agriculture Systems Management at Texas A&M University, where he also got his Master’s degree in Soil Science. There he helped develop an infiltration system for measuring hydraulic conductivity used by the NRCS in Texas. Currently, Leo is the force behind application development in METER’s hydrology instrumentation including the SATUROHYPROP and WP4C. He also works in R&D to explore new instrumentation for water and nutrient movement in soil.


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Hello, everyone, and welcome to How to Interpret Hydraulic Conductivity Data. Today’s presentation will be about 30 minutes, followed by about 10 minutes of Q&A with our presenter Leo Rivera, whom I will introduce in just a moment. But before we start, we’ve got a couple of housekeeping items. First, we want this webinar to be interactive. So we encourage you to submit any and all questions in the Questions pane. And we’ll be keeping track of those for the Q&A session toward the end. Second, if you want us to go back or repeat something you missed, don’t worry. We will be sending around a recording of the webinar via email within the next three to five business days. All right, with all of that out of the way, let’s get started. Today we’ll hear from METER research scientist Leo Rivera who will discuss how to get the most accurate and thorough insights from your hydraulic conductivity data. Leo operates as a research scientist and Director of Scientific Outreach here at METER Group. He earned his undergraduate and master’s degrees in soil science at Texas A&M University, where his research focused on the impacts of land use and landscape on soil hydraulic properties. He also helped develop an infiltration system for measuring hydraulic conductivity used by the NRCS in Texas. Currently, Leo leads METER’s collaborative research efforts and focuses on application development in hydrology instrumentation, including the SATURO Infiltrometer and the HYPROP. He also works in R&D to explore new instrumentation for water and nutrient movement in the soil. And so without further ado, I’ll hand it over to Leo to get us started.

All right. Well, thank you, Brad. And thank you, everyone for joining today’s webinar. This is a topic I really love talking about. I’ve spent most of my career working and doing hydraulic conductivity measurements and trying to understand what those data mean, and looking at different impacts. And so this is a piece I’m really excited to talk about. And I hope you all are excited to hear about it as well. So with that, let’s get started.

Before we dive in to the data and the interpretation of the data, I always like to start with this. And the question is why do we care about hydraulic conductivity? Well, it impacts almost everything soil is used for. It impacts things like crop production, whether it has to do with water storage and water moving getting into the profile, or the risk of things like erosion. It impacts things like irrigation and drainage decisions, understanding at what rates can we irrigate at. And how does water move through the vadose zone, especially if we’re doing like drip irrigation and things like that. Obviously, it impacts the hydrology of both native and urban environments. And, you know, we have stormwater system design on there as well. And I think this topic is even more important nowadays as we’re seeing bigger issues with flooding events, and more intense rainfall events coming through, you know, the soil’s ability to infiltrate that water that’s coming in and our ability to store it to prevent it from running off is so critical. And this is going to become a more important thing that we need to understand as we keep moving forward. It’s also useful in understanding landfill performance, whether we’re looking at landfill covers that are designed to prevent infiltration, or ET covers that are designed to store the water and evaporate it so it doesn’t infiltrate into the landfill, things like that. And then of course, the topic of much popularity nowadays is soil health. We understand that infiltration and hydraulic conductivity is one of those parameters that’s indicative of a more healthy soil system. So it’s a really important measurement.

But what factors actually impact hydraulic conductivity? Well, we have things like soil texture, so what’s our sand, silt and clay fraction. And then we have things like soil structure, which I would argue that soil structure, and you’ll see a chart here that kind of illustrates that shortly, plays a bigger impact on soil’s ability to infiltrate and its hydraulic conductivity than soil texture does. And other things that also play a large role are things like biopores, and those are things like decaying root channels, worm channels, all those types of things that we would see from different forms of life in the soil. Other things obviously, that play a big role in the impact on hydraulic conductivity, is of course compaction and bulk density. We know that increased bulk density and high compaction rates obviously reduce the soil’s ability to infiltrate water. And so our goal is to avoid those in most cases, unless you’re trying to reduce infiltration rates. And one other piece on here that maybe sometimes I think gets forgotten is water content and water potential. And I’m referring to the antecedent water content water potential. So, what that state of that is before we take these measurements. It has been shown in literature, that antecedent water content or water potential can impact what the soil’s hydraulic conductivity is. And I think that’s even more apparent in expansive soils where the antecedent soil water content really impacts what the structure of the soil looks like, whether there’s larger cracks, and things like that. And I’ll dive a little bit into that on a project that we’ll discuss here shortly. So these are all factors that really play a big role in a soil’s ability to conduct water.

And before we get even deeper into talking about hydraulic conductivity, it’s really important that we break down what we’re talking about. So when we’re talking about hydraulic conductivity, there are two states. There’s the saturated hydraulic conductivity, which we refer to as typically Ks, or Kfs, if we’re doing field measurements of hydraulic conductivity. And then unsaturated hydraulic conductivity, which we typically reference to psi (Ψ), which is the water potential, and as the water potential decreases, the unsaturated hydraulic conductivity will typically decrease as well. And it’s really important when looking at unsaturated hydraulic conductivity that we know what water potential it’s being a reference to, because that’s gonna make a difference in what the actual value is. And if we’re using this for modeling, we need to understand, of course, the full range. But on this chart, it really illustrates, in my opinion, the impact that structure has on hydraulic conductivity, especially as we approach saturation, that’s where it really plays a much bigger role. So here we have an example of three soils, a well structured clayey soil, a structureless sandy soil, and a poorly structured clayey soil. And the structure as we approach saturation in the well structured clayey soil enables that soil to have a much higher saturated hydraulic conductivity, even higher than that of the sandy soil. So this really just illustrates the impact that structure has, especially as we approach new saturation. So it’s really important that we understand these impacts and especially if we’re trying to do things like model these parameters, structure plays a really big role. We can’t just use texture on its own to assess these properties.

So how do we measure hydraulic conductivity? Well, typically, the most common approach is we’re going to measure the infiltration over time. And so here on the left, you see a typical infiltration curve over time. And what you’re seeing, and this is kind of written up in the equations on the right here. And before I go much further, I should remember to define what hydraulic conductivity is. So hydraulic conductivity is a measure of the ability of a porous medium to transmit water. And typically, we’re going to reference it to a point like we said KS or saturated or unsaturated hydraulic conductivity. And if we’re looking at an infiltration curve like this, ideally what we’re trying to do is approach a steady state value so we can get that saturated hydraulic conductivity. And what you see in this equation is there’s three kind of components. You have the change in height or the change in flux, just over time due to the soil’s ability to infiltrate water, then we have the change in flux due to the matric effects of the soil, and also the gravitational impact components on that infiltration curve. Over time, that matric component is going to become zero. And so essentially as our — and this is where we’re saying that we’re starting to reach that steady state — once we hit that point, we can assume that our infiltration rate is approximately equal to the hydraulic conductivity. Now, we do need to apply some other correction factors, like the ponding height and things like that to remove those effects. But that can all be done relatively easily with math.

So this is your typical infiltration curve. Now, there’s a lot of tools that we’ve used to make this type of measurement. And we’ll show a project where we use some of the more traditional tools. But in today’s webinar, I’m really going to dive into assessing measurements from something called the multiple ponded head analysis approach. And this is the theory that one of our devices, the SATURO, operates on. And one of the reasons I like this approach is it really simplifies the analysis. So let’s kind of dive into what that analysis looks like and how we’re simplifying it. So when we’re trying— if we only had one ponded height, you could— imagine this equation being written as Kfs equals the infiltration rate times delta, which is some coefficients to correct for things like ring dimension, ring height, insertion depth, ring diameter, ponding height, things like that, or sorry not ponding height, that’s gonna go into d. Lambda (λ), which is a correction factor for the sorptivity component of the soil, and D, which is the depth. Now, typically, if we were just infiltrating at one height, we’re going to have to make an estimate lambda and try to understand what that matric effect or that sorptivity, how that’s going to impact the infiltration curve, because ideally, water infiltrates into the soil from a ring in three dimensions, but we want a one dimensional flux value. So typically, we’ll have to make an estimate of that lambda value. And if we make a poor estimate, we wind up with a poor estimate of our hydraulic conductivity as well. And in some cases, it can be an order or two of magnitude off. And so it’s really important that we estimate those values well.

However, with a multiple ponded head analysis approach, where we infiltrate at two different pressure heads, you can see how this equation is written with the second pressure head and second infiltration measurement at that pressure head. And what’s beautiful about this is we can solve this equation, as you see below, where it actually eliminates or solves for that lambda value. It really simplifies the analysis. We no longer have to make an estimate of this parameter, we can just directly get at it through this multiple ponded head analysis approach. And if you’re interested, you can actually go through and calculate what lambda is and get your sorptivity values that way. But yeah, this is the beauty in this approach is it really simplifies the analysis. And so when we do that, you can see that now our hydraulic conductivity is a function of delta again, which is those correction factors for geometric corrections. With the infiltration rate at the first pressure head, and the second pressure head divided by the pressure head the ponded at heights at those two different pressure head, yeah, the two different infiltration measurements at the different pressure heads.

So to kind of further illustrate what this looks like. Typically, we’re going to have two measurements. We have our pressure head, which you can see in the graph on the left, and we’re infiltrating in this example at 5 and 15 centimeters. And we’re cycling back and forth between that. And you can see how that impacts the flux measurement in the graph on the right. And what we’re going to do is then take and average those pressure heads over that time, and we’re going to plug them into the equation on the bottom. And we’re going to take our average infiltration rates at those two different pressure heads or the average fluxes at those two different pressure heads and plug them into the equation here. And then our delta correction is purely based on the geometric factors of the rings, which is really simple to calculate. And we’re able to solve and get our hydraulic conductivity value. So this is the operational theory behind the multiple ponded head analysis approach. So that’s great.

Now, how does that look in application, what are the data from that actually look like when we’re making measurements? So let’s do a deep dive into what those data look like. And of course, we’re going to start with an ideal measurement. So this is what an ideal typical measurement is going to look like from the multiple ponded head analysis approach from a tool like the SATURO. And here you can see our steady decay over time and the flux. As we’re approaching steady state, as the soil is becoming saturated, we see that the infiltration rate decreases and eventually approaches a steady state. And you can see the increases in the infiltration curve, or the flux curve, due to the increased pressure heads, and they correlate really nicely which we would expect. And then as we reach our steady state, we’re then going to take that final cycle and plug that into the equation. And then we’re going to get our infiltration rate at the high pressure head and our infiltration rate at the low pressure head. And then of course, we have the d values based on our pressure head measurements to plug into the equation. And we can simply calculate our hydraulic conductivity. Everything is great and simple when it looks like this. There’s really no confusion. It’s a very straightforward measurement. So this is an ideal measurement. Now, if everything looked like this in the field, we wouldn’t even be having this discussion today. But they don’t always look like this. So let’s dive into some different scenarios where we’re seeing different infiltration curves and trying to understand what’s happening.

So before we dive too deep into some of those, I do want to talk about what happens when things don’t look exactly perfect. How much of an impact does that actually have on the measurement? So this is another measurement where we’re infiltrating water, things look pretty ideal at the beginning, we’re seeing that typical decay. The only thing about this measurement is, we just don’t see much of a difference between the low and the high pressure heads. You can kind of see that here, where you see a slight increase in the infiltration in the flux curve, but you’re not seeing much change between the two. And the risk with a measurement like this is that you might have higher error in your estimates of hydraulic conductivity because you might not be doing as good a job of assessing what that lambda value is going to be based on the pressure heads. Now, in the case of this measurement, you can see the error here actually still is not bad. It’s several orders of magnitude smaller than the hydraulic conductivity, which is what we’d like to see. But you can also see this is a relatively low infiltration, or low hydraulic conductivity, it’s 2.5 times 10 to the minus four centimeters per second. So pretty low hydraulic conductivity. And in this measurement, they only used a pressure head of five and 10 centimeters. And so what I’m trying to say here is ideally, in a measurement like this, we’re going to have a higher pressure head difference, maybe 5 and 15 centimeters to induce a higher indifference in the flux measurement. That way, we can ensure that we have a good, accurate estimate of our lambda value. And so that’s the one thing I would do different here is increase that difference. Other than that, this is still a relatively good looking measurement. And the error still is relatively low from this measurement. So not too bad.

Now let’s take a look at a different scenario. So here we have a really beautiful looking measurement at the beginning of infiltration, we can see this soil is likely fairly wet already. And so we reached our steady state relatively quickly, we don’t see that typical decay in the infiltration curve. And then we increase our pressure head here, and we see a good measurement, a good steady state measurement. Then all of a sudden, we see a stair step drop in the infiltration curve in the flux measurement. And you can see that all of a sudden, it drops quite a bit lower for the low pressure head here. And even when we go back up to the higher pressure head, it’s not anywhere near what it was initially in that first cycle. And same, but here, we’re steady again on this last low pressure head cycle. So what’s happening here, and this is something we’ve seen happen in a few occasions, where initially we’re infiltrating into one of the surface layers, and then all of a sudden, we hit a more limiting layer, deeper in the profile as the wetting front is making its movement, making its way through the soil. And that’s what’s happening. That’s what we’re measuring here. So this is not an uncommon occurrence. This happens, especially if we have things like plow layers or an argillic layer that’s higher in clay content, or if we have some compacted layers down below that we’re unaware of, or some lithologic discontinuity, where we’re going from maybe a sandy soil to some type of fine textured clayey soil, this will happen.

And so the question is really what’s the correct measurement to make? Well, I would argue that both components of this measurement are important. This first component is telling us the infiltration rate and the hydraulic conductivity of that surface layer. And this second part is telling us hydraulic conductivity of that deeper layer and how that’s impacting the overall flux through the profile. Obviously, hydraulic conductivity is going to be— overall, or at the end of the day, hydraulic conductivity is going to be limited by the most limiting layer. So if we’re trying to understand max capacity, this is really the part that we need to understand. But understanding these early components is also important in understanding, you know, earlier in the season, when the soil is drier, how much water can the soil infiltrate, versus as it gets wetter during wetter seasons? What’s that actual value going to be? These all play really important roles in our understanding of soil hydraulic properties. So this is a good example of deeper limiting layers. Now, let’s move on to a different example where we see the exact opposite occurrence.

So here we have an infiltration curve, where, again, the soil is likely relatively wet because we reach steady state fairly fast. And then we’re cycling through our pressure heads. Then all of a sudden about 21 minutes — 21, 22 minutes — we start seeing an increase in the infiltration curve over time. And then as we increase our pressure head, that increases even faster, and then starts to steady back out. Now, we see this happen on occasion. And this typically is indicative of unstable pore structures in the soil, whether it’s maybe some pores that are clogged and starting to open up as we increase the pressure heads, which is something we do have to be a little careful with sometimes, especially in higher hydraulic conductivity soils. Or yeah, just these weaker soils start opening up. And so these are things that we have to be careful with, we see this happen on occasion, and this is a challenging one to interpret because sometimes, you know, is that unstable pore really what we want to quantify in our hydraulic conductivity measurement? So do we want this later portion of the measurement? Or do we actually, are we more interested in this early portion of the measurement where the pores were still relatively stable before they started to open up? And so it’s kind of a question I’m gonna leave up to you to answer because it’s really dependent on your research and what your research goals are. And if this early portion was really what we wanted, what we would do is take this early part of the measurement and recalculate our hydraulic conductivity based on that value, because we already are at a relatively steady state. So we should be good to make a hydraulic conductivity measurement there. But in some cases, you might be more suited in this latter portion, which is great. So it just depends on your research goals, and what you’re trying to do.

Now let’s jump to another example, where we see a kind of similar effect, maybe not quite as drastic. Again, we do see your kind of typical initial infiltration curve, where it starts out high and starts to decrease as the soil gets wet, or saturated. And then we see our increase in pressure head, and then the increase in the overall flux rate. And we dropp to our lower pressure head, but then all of a sudden, again, we’re starting to see that steady increase in the flux rate of the soil. And then we see that increase in pressure head. And again, that last cycle is still higher than some of the earlier cycles were. Now in this example, because it’s so gradual, you know, I question, maybe this is impacted by two different things, it could be impacted by again, unstable pore structure. Or we could actually be looking at temperature influences on the measurement as well. As we know, hydraulic conductivity is of course, also a component of the viscosity of the water that’s infiltrating into the soil. And if that water is heating up — so this could have been a measurement on a really hot day, and that water was initially quite a bit cooler. If that water is heating up over time, we could start seeing an increase in the infiltration over time as that viscosity decreases, and it makes it easier for that water to work its way through the soil. So I don’t know the exact answer in this scenario, what was actually happening. One of the things I highly encourage is metadata, taking temperature measurements of your water. And hopefully in the future, this is something that will be added as well to the SATURO. I hope to get this implemented. But taking temperature measurements of your water, taking antecedent soil moisture measurements, and taking soil temperature measurement as well because these all play a role in the end result that you get in your hydraulic conductivity value. So I always highly encourage taking as much metadata as you can, even site assessment pictures, you know, the more metadata the better. So another good example of just some steady increases. This is not a bad measurement, it’s not making that much of a difference in the final result that we would have gotten. But it is something that you need to understand that can be impacting your measurement.

Okay. So in this example— well, here we see, and we graphed this a little differently because I’m trying to show a slightly different approach here. Here we see an infiltration curve over time, at about a 60 minute measurement. It’s a typical decay as we see, with a higher infiltration rate initially and decreasing over time. The problem with this measurement is it wasn’t ran for long enough to get a steady state value. And we often get the question like well, how can I speed up my measurements? Because sometimes we don’t have time to spend hour and a half, 90 to 120 minutes, out in the field making these measurements because they are time consuming measurements. Even with automated tools, it still takes time to make the measurements, especially if the soil is drier and initially to get to that steady state value. So how can we speed this up? Or can we speed this up and maybe assume some amount of error is going to come out of speeding up the measurement? And the answer to that question is, yes, there is an approach that you can take. So if we assume that the change in flux or infiltration over time as we approach steady state is exponential, we can fit an exponential curve to the infiltration curve, and use that to solve for our infiltration rate, say, at time 90 to estimate what our infiltration rate would have been at when we were at a steady state value. And so you can see that example here, we fit infiltration, or we fit an exponential fit to both the low pressure head data and the high pressure head data up here. You can see those two equations here. We then solved for our infiltration at the 90th minute, and we can see that value here for the high flux, the flux at the high pressure head and the flux at the low pressure head. We can then use those values and put them into our hydraulic conductivity equation just like you showed earlier. And you can see all those values that go into the corrections here and the pressure heads. And we can use that to solve for hydraulic conductivity. Now when we do that, we need to know that depending on how much decay we have, and how early on we take these measurements and stop these measurements, there is going to be some amount of error that is going to come with that measurement. And so if you’re willing to accept that, you can speed up the measurements and still get a decent hydraulic conductivity estimate. Obviously, the longer you can run it, the better. You’re going to get a better fit and better estimate of your infiltration and get a better estimate of your hydraulic conductivity at the end of the day. But this is an approach you can take. I highly encourage you to play with this if this is something you want to do. I’m happy to talk more about how to do this approach. And maybe this is something that we’ll work on a little bit more down the road as well. But yeah, if you have questions about this, please let me know.

So we’ve done a deep dive into measurements, and what different measurements look like. And now I want to jump into how we’re taking those measurements and using them to understand different processes and what’s happening and how different things are impacting soil hydraulic property. So I’m going to talk about two case studies today. Both are things that I’ve been involved with in my time. And first, we’re going to talk about this first case study here. And this is a fairly old case study for me now, but this is based on my graduate research when I was at Texas A&M, where we were working on making a semi automated double ring infiltrometer system. And with that system, we had to make over 200 hydraulic conductivity measurements across three different fields. And the goal of this study was to evaluate two things: we wanted to look at land use impacts on soil hydraulic properties, and we also wanted to look at how soil hydraulic properties change across landscape positions. And we refer to this as the catena effect. So as you go across the hill slope, you go from your summit across the back slope down to the foot slope of that hill, we know that soil formation processes change, due to the way that water moves and is stored in that landscape. And so we wanted to quantify, how does this impact soil hydraulic properties. And so for this study, we were fortunate enough to be able to work on three different land uses, we have an improved pasture, which you can see a picture of in the upper right corner, which is being grazed. Conventional tillage field that was in a corn/corn/wheat rotation, and you can see that picture in the middle. And then finally a tall grass native prairie in the bottom right hand corner where you can see where we’re actually making some of the measurements. So you might ask, Where can we find three distinct land uses that have been in these land uses for a long enough time to actually make for a good research site that also has these catenas that you’re talking about where you have these good hillslopes to actually make measurements? Well fortunately, we had access to the USDA-ARS Riesel Watershed which is located in the Blackland prairie of Texas, which has been in these soil, in these land uses since the 1960s. And these land uses have not changed, and the site’s been studied heavily since that time and has three beautiful watersheds that we were able to measure on, where they’re actually measuring the runoff coming from these sites as well. And the soils on these sites are predominantly mapped as Houston Black and Heiden Clay. And just for reference, Houston Black and Heiden Clay are relatively similar soils, the biggest difference, I think between the two, is the depth of parent material. But outside of that they’re relatively similar soil types.

And so, of course, I’m just going to briefly jump into some of the data and some of the examples. But first, you know, one of the things we had to decide is okay, where are we going to make these measurements across the landscape? Well, first, we need to, of course, measure the distinct hillslope positions, the summit, the back slope, and the foot slope. So where we chose to make our measurements. But from there, where are the best locations to make measurements across the field? And one of the things we wanted to do is like, well, let’s try and map where our distinct areas of variability are. So one of the tools we use for that is an EM-38, where we’re able to go out and scan the field and make a bulk EC measurement across the field. And through our assessments, we found that the bulk EC change was predominantly due to the change in depth of parent material. And this fit really well with where the Heiden Clay and Houston Black clays were mapped on these soils, which again, are predominantly due to the depth of parent material changes. And so we use these zones of variability to set up where we’re going to make our measurements. And we did this across all three fields. This is just a map of the conventional tillage field on the left. But we did a similar mapping across the other two fields as well. And so this helped us establish where we were going to make our measurements. And then we need to go out and just make as many measurements as possible to try and quantify the variability across the field and get a good assessment of the hydraulic conductivity. So, again, we made approximately 200 measurements, and a lot of that work and a lot of the learnings from that work is what actually led into the development of tools like the SATURO. But so that was a really great experience to get to work on that and learn some of the advantages and disadvantages of these different tools and try and make a better tool.

But now let’s actually look at some of the results that we saw from this study. So here we see the hydraulic conductivity, some box plots of the hydraulic conductivity across the three fields, also broken down by the landscape position. And what you see here is, we do see some differences due to land use, as we would expect. The native prairie had higher hydraulic conductivity overall than the improved pasture, which would make sense due to the compaction in the improved pasture from the grazing and the improved structure in the native prairie. And then in the conventional tillage field, we also saw higher hydraulic conductivity measurements. Now, one thing I would point out about this is some of this has to do with actually the timing of when we made our measurements. Most of the measurements made in the conventional tillage field were made after the fields had been plowed. So that was still relatively loose soils. And that was before they’ve had time to settle and compact. And if I had my way, if I had time to do it again, we would make measurements later in the season when the soil had time to settle and see what that looked like. And I would expect that that soil, we’d see decreased infiltration rate, and it would be lower than the native prairie site. But again, timing of your measurements really plays a key role in what you’re going to actually see. So we did see some land use differences. But the other really interesting fact, or interesting thing that we saw, was there were some distinct differences across the landscape positions as well, especially in the improved pasture and native prairie sites where we did see typically higher hydraulic conductivity on the back slope than what you saw on the foot slope. And typically, we saw higher hydraulic conductivities— or the lowest hydraulic conductivities in the foot slope positions, which was pretty fascinating. And I don’t have an exact explanation for why you see those differences, but we do see some impacts in the landscape position, except for the conventional tillage field. And again, some of that might just have to do with the plowing that’s occurring and how that changes the soil structure. So really fascinating. Something that would be fun to see more work done on. But yeah, we did see some impacts from land use and landscape position on the soil hydraulic conductivity.

So now let’s jump to our second case study. And here we’re looking at tillage effects in the Palouse, and we were looking at two things, we wanted to look at lab and field measurements and see if they differed, but wanted to also look at no till versus tillage effects on soil hydraulic properties. Fortunately, we were able to go out and do this at the Cook Agronomy Research Farm. And to make these measurements, we used the KSAT device in the lab and we took core samples, and we use the SATUROS in the field to make the field hydraulic conductivity measurements. And so we’re just briefly jump into this just because we’re getting a little short on time. The goal of the study, of course was to look at tillage effects, and then also compare the lab and the field measurements. What’s really interesting that came out from this short study, which hopefully we can expand on more in the future, is when we look at the no till versus the conventional tillage field, we do see a jump in the median values of hydraulic conductivity in the no till field, which we would mostly expect due to the improved structure and increased pore structure from the decaying rich channels that don’t get destroyed. And it was consistent, very consistent, in both the lab and the field measurements that our median values were both higher, and they were very close to each other across the two methods. The one interesting thing that came out from this is, in the conventional tillage fields, we saw higher variability in the lab measurements. And in the no till field, we saw higher variability in our field measurements. And I think some of that has to do with the scale and size of the measurements that we’re making. And, you know, smaller cores in the lab measurements versus larger rings in the field measurements. But also just how the samples are retained, especially in a conventional tillage field where we might see more loss of structure as we take core samples, because they’re not going to be very stable. So I don’t have all the answers to what we saw here. It was a really interesting study to take a look at. Hopefully, we’re gonna continue to expand on this and collect more data in the future. But I thought this was a really interesting result to take a look at.

So I want to finish up with a couple of things. There’s always other things that you need to consider when making measurements and how to get the best measurements and how to best understand your data. One of the things that we have to account for is spatial variability. And we refer to this term representative elementary volume, or REV, which is the smallest volume of soil that can represent the range of microscopic variations. It’s really important that we understand this and that we know that how our size of the measurements and the number of measurements that we’re taking, allow for us to quantify this. This is especially important for water flow processes because representative elementary, the REV there is predominately based on soil structure. And so we need to understand that and have enough scale measurements and big enough punishments. And we briefly cover this in a previous webinar that was done by two researchers out of Villanova. If you’re interested in taking a look at that I highly recommend going back to some of our past webinars and listening to what they talked about and how many measurements they needed. And then of course, assessing time domain and seasonal changes. Again, we talked about antecedent soil moisture can have an impact on hydraulic conductivity values. Changes in vegetation over time can have an impact on that. And we need to understand that. That’s why metadata is so critical. And of course, land use impacts. So understanding how land use changes in land use impacts are going to affect the soil hydraulic properties.

Lastly, just a few takeaways are really, you know, I hope this shows that what you saw was, we can optimize our measurements for location and application. And we need to understand that when making measurements and understand that if you have lower hydraulic conductivity soils, increase the pressure head to get better differences. And vice versa of really high infiltration rate soils, we should decrease the pressure heads to reduce the chance of destroying pore structure. And it makes the measurements easier and uses less water too. So last couple of things, learn from past mistakes. Look at previous measurements. If you see issues, make adjustments in your measurement settings to try and get the best measurements possible. And then thirdly, comparing lab versus field measurements can be challenging. We’ve always known this. This is well documented. However, I think if you have enough measurements, you can still quantify the median values well and get a good understanding of your soil. So I think number of measurements will solve a lot of these issues. And with that, I think that’s it.

All right. Thank you, Leo. So we’ll use the next few minutes. We’ll take some questions, we’ll see how many we can get through. Thank you, everybody. Thank you to all those who have submitted your questions already, we’ve got several who have come in, and there’s still plenty of time to submit your questions. Please enter them into the questions pane, and we’ll get to as many as we can before we finish here. If we do not get to your question, we do have them recorded. And Leo or one of our other experts in hydraulic conductivity will be able to reach out to you via email to answer your question directly.

All right. So the first question we’re going to address today actually came via email from one of our registrants, and we’d like to hit this one first, before we get into the live questions. And we’re trying to simplify this question. It was a good, long, in-depth question. So we’ll just go through this. And then Leo will answer, and we’ll see where we go from there. So they’re basically saying that, as you know, the estimate of K includes an assumed geometry of the wetting front so that a quantitative estimate of the effective depth over which conductivity is being measured can be computed. It would be extremely helpful if you could address this implicit scale dimension. My interest in this is in part that tension infiltrometers are increasingly being used to estimate infiltration for urban soils, especially so called “urban forest patche”s, and the near saturated conductivity value is being treated as though it characterizes the effective conductivity of the entire urban soil column. So hit that one, Leo.

This is a good question. And fortunately, I knew it was coming, so I’ve had a little time to think about it. And I think we addressed some of this in the presentation, where we show how as the wetting front moves through the soil, we can see changes, especially if we have more dense layers below, which you might see in some of these urban sites, where you have some applied topsoil, and then potentially a more compacted layer down below. So yes, that geometric wetting front, is really, it’s important that you understand that. And when you do hit deeper layers, you’re gonna see a change in your instrument. But the beauty is, if we’re quantifying all those measurements over time, we can see when that occurs and understand what’s happening in the soil, and try to tease out, okay, when I’m infiltrating through the surface layer, this is what it’s going to look like. And when we hit that more limiting layer down below, that’s what it’s gonna look like. So I think that covers that a little bit. And there’s a lot more to this question that I hope to cover in the future. But the last piece is kind of the second part of the question using tension infiltrometers, to assume a steady state value. One of my biggest concerns with that, actually, is the fact that they’re using tension infiltrometers to make that assumption measurement. Because as you saw in the infiltration curves before, tension infiltrometers don’t measure saturated hydraulic conductivity, they measure unsaturated hydraulic conductivity. And you can do your best to push them near saturation, but it’s never truly near saturation. And if you have soils that have good macropores, you’re never going to measure that with a tension infiltrometer. And so you can significantly underestimate your hydraulic conductivity value with that measurement. And so that’s one of my biggest concerns, actually, with that. It’s not the fact that they’re trying to use that, and the concerns with it hitting deeper layers, because I think we can quantify that. It’s the fact that I think they’re using the wrong tool to try and make this measurement and understand how urban hydrology is going to look like or the you know, that these urban forests, so.

All right. Thanks, Leo. All right. So let’s hit some of these. We got quite a few questions, we’ll see how many we can get through. Again, if we don’t get through all of them, we do have them recorded. One, this first one here was asking about the data and they’re just asking if the data that’s received through the SATURO or other means can link directly with stormwater management calculations for drawdown. So again, the application of that data, how do they collect that data? And can it be then inputted into their own calculations?

Yeah, that’s a great question. Certainly I think they can and this is pretty commonplace that we see being done. Actually, that webinar that I talked about, where they kind of helped us understand the number of measurements that we need was specifically for stormwater applications. And they’re using these measurements to quantify some of these catchments that are designed for infiltrating and capturing that stormwater to increase infiltration and reduce runoff, so they’re using those tools to quantify these basins. Now, one of the key factors there I think, if you’re going to use this to make these overall stormwater estimates is you need enough measurements to cover the area and understand the overall ability of the area to infiltrate water. Now if we’re then also trying to quantify incoming stormwater, or potential runoff into the site, we also need to quantify the watershed as well that’s going to be feeding all of that soil water. And so I hope I’m addressing this question correctly in terms of what they’re asking. But yes, you can. And typically, what you’re going to do in your models is you’re going to take the hydraulic conductivity value, that final K value, and then put that into your models. Now, if you also need unsaturated hydraulic conductivity for your model, then you might have to look at incorporating some other tools like a tension infiltrometer to get that unsaturated K value. But it depends on what parameters you’re inputting into your model.

All right. This next one, you talked about comparing field versus lab measurements, and they were asking for the lab measurements, were those based on undisturbed or repacked sediment cores?

Great question. Those were based on undisturbed cores. So as we were taking the hydraulic conductivity measurements in the field, we went out with a core sample and took intact cores. I will say it’s always, you know, taking core samples can still be challenging, because you run the risk of compacting the core. Or as you’re transporting the core, if it has poor structure, you can see changes in the pore structure. You also see, sometimes you can have open ended pores in a core sample that aren’t open in the field, or vice versa, we can line up closing pores off in a core sample that wouldn’t normally be closed off. So yeah, it’s always challenging. But yeah, those were intact cores, not undisturbed. I don’t typically use undisturbed samples when trying to compare with field measurements, because you’re not going to learn anything that way if the sample’s not undisturbed.

Let’s see. All right, this next one. And we’ll take a couple more. We’ll stretch this one out a little bit. They’re asking what is the depth range over which a device like the SATURO integrates measurements of conductivity?

Another really good question. And it varies. The biggest thing that impacts that is, well, it’s your time of measurement, how long are you actually running the measurement? How much water are you infiltrating? And so how deep is that wetting front moving through the soil? So if you’re taking quick measurements, it’s going to be relatively shallow, but if you’re taking long measurements and letting it get steady state, so ideally, if you’re running it for 100, you know, 120 minutes, then you’re going to integrate a much deeper portion of the soil. But that’s also limited by the hydraulic conductivity of the soil, of course. So the higher the hydraulic conductivity, the deeper the water is going to move through the profile and the more you’re going to integrate. The lower the hydraulic conductivity, the more shallow it’s going to be, and you have to run it for longer to integrate more of the soil profile. So it just really varies, depending on on the measurement factors.

All right. Okay, I think this is gonna be our final question. This kind of flows from that one as well, is they’re asking if you have experience measuring conductivity at multiple soil horizons. Can you infer conductivity at multiple horizons from a single run at the surface if you run it long enough? Or does that become a slippery slope?

That is, yeah, these have all been great questions today. It’s challenging, I’ll just say that. Ideally, if you’re really trying to quantify, really get down to the the horizon impact — and we just had this discussion with some folks at the NRCS — the best thing to do is excavate down to those different layers and make measurements at the different layers. And you can do that with a tool like the SATURO. Or you can use other tools like a borehole method. Or you can take core samples and take samples from those different horizons. Again, each of these approaches are going to have challenges. If you’re using just a surface measurement to try to quantify the different horizons, it’s going to be challenging. Because again, ultimately that measurement is predominantly controlled by the most limiting layer. So if your most limiting layer is right at the surface, then you’re never really going to see if your lower layers have higher hydraulic conductivity. But in those examples, where we do have limiting layers that are deeper, if we run our measurements for long enough, we should be able to see the impacts of those subsequent horizons, especially if they are more limiting. So you can kind of get at it with a surface measurement, but if you really want to dig into it and understand the different horizons, the best thing to do is actually just get to those horizons and make the measurements there. So, yeah. But fun question, fun challenge. And I’ve seen a lot of different approaches to it.

Right, so good luck with that one. All right. I think that’s gonna wrap it up for us. Thank you again for joining us today. We hope that you enjoyed this discussion. And thank you again for such great questions. Again, final reminder, we do have all your questions. We only touched, we scratched the surface of all these questions. So, Leo’s got his work cut out for him later on to get back to answer your questions directly. Also, please consider answering the short survey that will appear after the webinar is finished, just to let us know what types of webinars you’d like to see in the future. And again, for more information on what you’ve seen today, please visit us at Finally, look for the recording of today’s presentation in your email. And stay tuned for future METER webinars. Thanks again, stay safe, have a great day.

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