Transcript:

BRAD NEWBOLD 0:10
Hello, everyone, and welcome to how to interpret soil moisture release curve 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 and 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. Alright, with all that out of the way, let’s get started. Today we’ll hear from METER research, scientists Leo Rivera, who will discuss how to harness all the most powerful insights from soil moisture release curves. 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 meters 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. So without further ado, I’ll hand it over to Leo to get us started.

LEO RIVERA 1:41
All right. Thanks, Brad. And thank you everyone, for joining today. I’m excited to talk with you all about interpreting soil moisture release curve data. This is something that I’ve spent a lot of time in both generating my own soil moisture, release curve data, and also helping people like yourselves interpret what’s happening and what their soil moisture release curve data means. And so this is some of those learnings from that. And I’m excited to talk a little more about that. But before I dive into the soil moisture release curve data, I really think it’s important to go over some fundamentals first. And I do this because I’m trying to stress the importance of why we even make these measurements. So in nature, we need to understand two variables, these, we need to understand the difference between extensive and intensive variables. And so in nature, two variables are necessary to describe the state of matter or energy in the environment, you have the extensive variable, which describes the extent or the amount of matter or energy. So you can think of this of things like volume, we can all very, very easily think about volume and what that means. water content is another extensive variable. And we’ll dive more into that. And heat content is another extensive variable. Okay, the other variables that you need to understand is the intensive variable. And this describes the intensity, or quality of matter or energy. So we can think of our intensive variables being things like density. Water potential is another intensive variable. And we’ll dive more into that here in a minute, and temperature. And the reason I, there’s a reason I have heat content and temperature, here is two examples of extensive and intensive variables. It’s because these are all things that we can very easily understand in our day to day lives. And we can relate to these two variables and what they mean. So let’s look at this next example here to just kind of keep trying to bring this home. So here we have on the left a ship in the Arctic Ocean, as you can imagine, the temperature is pretty low there. And then on the right, we have a rod that’s being heated by some coals, and, and its temperature is going to be quite high. So if we were thinking about these two examples, the temperature of the rod is quite a bit higher than the temperature is significantly higher than the temperature of the ship, as you can imagine, in the Arctic Ocean, however the heat content of the ship is much higher than that of the rod. Now, if we were to put these two items into contact with each other, which way do you think energy would flow? Well, it’s gonna flow from the hot rod to the ship, even though the heat content is significantly higher in the ship. Temperature is that variable that governs the way that energy moves in the environment. And so what I’m trying to bring stress is that heat contents great to understand it is an important variable to understand. But it doesn’t really tell us much about what’s going to happen and how things interact in the environment. It’s very similar to these next two examples. So when we think about soils and water in soils, the first thing that we think about is water content. water contents really easy to understand it’s the amount this is our extensive variable. And water content is simply the is the ratio of the volume of the water to the total sample volume. And you can see here are some examples of different water contents 42% 10% and 1%. water content knowing what that looks like in soil. This is really easy to understand and really easy to measure. But does this tell us a lot about how plants are going to feel in this environment? How microbes are going to feel how water is going to move from point A to point B? Not really. And that’s because we really need to understand this next variable, which is our intensive variable, which is water potential. So most commonly, when we think about water potential, we’re typically thinking about matric potential. And matric potential is the sum of matric forces both adhesive and cohesive. Now, there are other forms of water potential, we have osmotic gravitational and pressure potential, these three are less significant. Typically, we don’t worry too much about these, especially in unsaturated conditions. And osmotic potential might play a role in more saline environments. But typically, it’s not something we have to worry too much about. So typically, when we’re talking about water potential, we’re talking about matric potential. Now, these forces, these adhesive and cohesive forces, lower the energy of the water due to the binding with the soil surface. And the lower the water content, the more tightly bound that water is going to become. And this all depends on the makeup of the soil, how how much surface area that is, how small the pores are, all these things govern the way that looks. Now as we lower the water potential, it makes it more difficult for plants to get water. Okay, this is a much more simple concept to understand. And it’s a lot easier to this really helps us identify whether or not water is going to move in the environment or whether it’s gonna be available to plants. And so we really need to understand both of these variables. And the way we often do that is through a soil moisture release curve. And the soil moisture release curve is how we relate these extensive and intensive properties in soil. And the reason it’s we need to make these measurements and understand them is as you can imagine, this is different for every soil type. And for every way it’s made up and put together. And and we often refer to the soil moisture release curve as the soil fingerprint. And so here we can see three different examples of three different soil types of silt loam, loamy fine sand, sorry, yeah, let me find sand and the fine sandy loam. And how those curves all look different. And we’ll come back to these examples here in just a minute. But before we do that, I do want to talk a little bit about how we make these measurements and, and what and what you need to understand, especially when it comes to limitations. So one of the reasons we most commonly refer to water content instead of temperature, or instead of water potential, is because it’s a lot easier to measure. Water content, we’ve been making these measurements for decades, I mean, for more than decades, and a lot is a lot easier to measure. Water potential, on the other hand, is a much harder measurement to make. One of the issues is there’s not an instrument that covers the full range of water potentials. Now we’re continuing to make strides and make improvements in these tools in the way they measure. But this is a limitation. And, and because of that, water potential is always been a lot harder to measure, especially whether we’re measuring it in the lab or in the field. It’s just it’s a harder measurement to make us because of these limitations. But we’ve continued to make advancements in this and we’re getting closer, we now have the ability to measure the full range of the soil moisture release curve combining two instruments together. Here you can see an example of a moisture release curve that’s generated from these two tools on the right, the HYPROP and the WP4C. Now, I’m not going to dive super deep into the details of how these instruments work. We’ve done that in past webinars, I highly encourage you to go check those out. And we have more content, I believe we’ve attached them some spec sheets on these devices as well if you’re interested. So feel free to check out the attached handouts if you want to learn more about these devices. But what you can see here, we’ve now given ourselves the ability to measure the full range of the soil moisture release curve in the lab. Now this is this is lab data. And so we then need to figure out how to translate to the field. And in some ways we can make these measurements in the field. And I’ll briefly touch on that. But now we have these tools that allow us to make these measurements. And then more and more we use these tools and more and more we’re learning about what these data mean. And this is what now what we’re going to dive into is how can we use these tools and the information that we get and interpret what the soil moisture release curves mean for us. And we’re continuing to learn more and more about this every day. So, first, I’m going to start with some really basic examples, one of the most common ways that we use soil moisture release curves. And that’s to make irrigation decisions. soil moisture release curves are really powerful tools to help us take all these different soil types that we have to grow plants in and utilize this information to make smarter decisions about irrigation. And the reason for that is to make wise irrigation decisions, we need to understand two things need to understand volumetric water content, which tells us how much irrigation we need to apply to hit specific thresholds and water potential which tells us the availability of the water to the crops. It also helps us not over water and potentially keep the soil to saturated put it putting our our crops at higher plants at higher risk of disease, and things like that. So these are two really powerful tools that we need to understand. Now again, these there are ways to make these measurements in the field to see it here, you can see two examples on the right of tools that we use in the field, the soil moisture sensor on the top in a water potential sensor on the bottom. Now, these are really powerful tools. But we don’t always have these at our disposal. Sometimes we only have a water content measurement in the field. And so

if we only have a lot of content measurement, because there it’s usually the easier measurement to make, we need to understand more about the soil to make smarter irrigation decisions. And this is where soil moisture release curves come in. So here we’re gonna dive back into those three examples that we showed earlier. With these three different soil moisture release curves, so again, we have our silt loam soil and the dark blue, we have a fine sandy loam and the light blue, and we have a loamy fine sand in the gray. First we’re going to focus on this silt loam soil. And I’m really trying to stress is just how different the water content requirements are for these two different soils. So let’s say we have a plant and we know our happy ranges for this plant are minus 33 kilopascals to minus 100 kilopascals. So we can utilize our soil moisture release curve to then say, okay, we know where that happy water potential range is. And we can identify what that means in terms of water content, let’s just say our lower end of the threshold is minus 100 kilopascals. And we know with the silt loam soil, that means we have an ideal target water content of about 24%. And then we know our upper threshold is minus 33 kPa, and that ideal water content is going to be about 32% volumetric water content. Okay, so we now know our targets. This then gives us the information with the water content. Let’s say we have an irrigation zone 15 centimeters or 5.9 inches. If we want to get to our target, upper threshold, well, we’re not going to overwater and push to and potentially lose water due to drainage or, or keep their plant or soil to saturated. This means we have irrigation requirements to hit that threshold. Based on pretty simple math that you can see here. In order to hit that threshold, we need to irrigate at 1.2 or apply 1.2 centimeters of irrigation or about 0.47 inches. So this gives us more information for the silt loam soil. Now, does this apply can we use the same required irrigation requirements for our fine sandy loam. Let’s take a look. So here we’re going to focus now on that light blue curve. And again we have the same plant that we’re growing so we know our target range, our lower end of the threshold is minus 100 kilopascals in the in the fine sandy loam in terms of water content, that means we’re targeting about 10% volumetric water content. Okay, and we know for our upper threshold is minus 33 kPa. And in the fine sandy loam, that means we’re targeting about 16% volumetric water content. Alright, so now we’ve identified our target ranges, in terms of water content for this fine sandy loam. Again, same irrigation zone to 15 centimeters or 5.9 inches. And if we use the same math that we used before, we now know that in order to hit these ideal ranges for the fine sandy loam, we need to apply about point nine centimeters of irrigation or point three, five inches. So here we have very different target ranges that we need to hit. And because we understand the soil moisture release curves, we better understand the irrigation needs for the plants in these different soil types. So that’s really the one of the powerful ways we can use soil moisture release curves when it comes to making irrigation decisions. Okay, now, let’s go a little bit beyond this and think about what other factors might limit plant water availability. And so here, I’m bringing up an example. This is from a research project we worked on many years back, we had a researcher reach out to us and say, Hey, we’re growing plants in this non soil media. So this is a particular A form of or a type of potting soil. And we’re starting to see our plant stressing at about minus 10 kilopascals. And you’re like, it makes you think what that doesn’t make sense, we wouldn’t expect plants to stress at minus 10 kilopascals, that’s still quite wet. So we started diving into this to try to better understand why this material was we were seeing plant stress at about minus 10 kilopascals, which is not well above wilting point, or well above the stress point. So what what’s going on there, and one of the first things we did is we actually generated a soil moisture release curve for the soil. Now, before I dive into the curve, and show that I want to bring up this this concept of soil grading, okay, so typically, when we think of soil grading, which is has to do with pore size distribution, we often break things out into categories, like being a well graded soil, which means it has a good even distribution of different pore sizes, as you can see in that example here, or a uniformly graded soil, which means we have a similar distribution of very similar pore sizes. So so not, not many other ranges of pore sizes, or gap graded soil, where we have a good distribution of some of the bigger pores and a good distribution of some of the smaller pores. But we’re missing some of that pore size distribution of in the in the middle part of the in the middle range of the pore sizes. And what does that why does that matter? And well, it turns out that it does, especially depending on how big that gap is. And that’s what we started to learn with this example from the soil moisture release curve that we generated for this soilless media. And so here you can see the soil moisture release curve for that soilless media. And we we assumed that this was going to be slightly gap graded, because you have bigger pores that are generally created by the structure of the media itself and and the materials in there. But then we also have a lot of fine pores that are contained within the media itself, the bark and those things. And what happens because we have that kind of distribution, we wind up with this bimodal soil moisture release curve. And you can see that here, this soil moisture release curve is extremely bimodal. And what that means is you have that that kind of stairstep action in the soil moisture release curve. And why does that matter? Well, it turns out that as we start hitting that lower entry point, so we’re seeing here is two entry points essentially, is the water actually started to become less available because the hydraulic conductivity was actually becoming a limiting factor. And we learned this first just by looking at the soil moisture release curve and saying, Hey, this is this is interesting, what’s what’s actually happening here. And so the plants actually started stressing because although the bulk of the media was around minus 10 kilopascals. As the plant started using water, the water was no longer able to redistribute as quickly around the roots, which meant that the areas right around the reach were actually experiencing lower water potentials when we’re stressing more and the water wasn’t redistribute, redistributing fast enough for those plants to not stress. So this was a really interesting application of how we utilize these soil moisture release curves to understand the impact that this material had on the plant water relations. And now we’re seeing more a broader use of the soil moisture release curve to better understand different soilless medium mixes and to better refine and optimize the soilless medium mixes to make better decisions and make better videos to grow plants. And so this is these are pretty basic examples. The the the plant stress ones were hydraulic conductivity was a limiting factor is a little bit beyond a traditional use of a soil moisture release curve. But what else can we do? I mean, what else can we learn from from soil moisture release curves, there’s got to be more information in the soil moisture release curve than just some of these plant water relations. Well, it turns out that as we started developing better tools to understand soil moisture release curves and better better describe the the way these the soils behaved at these different water potential ranges, we started seeing that there is more information. And so one of these tools that I’m talking about is a tool called the vapor sorption analyzer. This is a tool that operates on very similar principles to the WP4C, but is able to automatically generate these wetting and drying curves. And you can kind of see an example of what the chamber looks like in the middle there. Again, I’m not gonna dive super deep into the details of how the device works. But what I really want to focus on is these soil moisture release curves on the left here. And what was fascinating is that we were able to see so much more detail now in the soil moisture release curve as we’ve continued to develop these tools. It helped us realize wow, there’s way more information you In the soil moisture release curve, than we realize, especially as you start seeing wild, some of the soils have more hysteresis on the dry end than others, the slopes are different, we always knew that we could use the slopes in some ways to interpret some other soil properties. But this tool, these advancements of these tools are allowing us to even dive deeper into what the information what information is bound within the soil moisture release curves. And so let’s dive into that a little bit deeper. So how can we use soil moisture release curves to, you know, go beyond traditional uses, expand our predictions of soil properties, and use these to actually predict some fundamental soil properties. And here we have a good example on the left. This was from a research project that we worked on, probably starting in around 2012. And this was published in unsaturated soils research and applications from proceedings from a conference in 2014. And, in this research, what we were doing was taking soils that we had well characterized in the lab

there how expensive they were based on the coefficient of linear extensibility coefficient of linear extensibility is a is a tool that we use, the higher the COLE value, the more expensive the soil is. And this is a well known tool used in the lab, we’ve used COLE values for decades to to measure how expansive soils are. But these measurements take a long time it takes these can take months to complete a COLE value. So can we use a soil moisture release curve that we can generate in typically 24 hours to predict how expansive the soil is going to be? And it turns out this I mean, we’ve there’s been literature on this by McKean and others that we can use the slope of the soil moisture release curve to predict these properties. And so we took these soils and and ran the soil moisture release curves in the VSA and took the slope of those curves, and predicted based on the McKean method, how expansive the soils are. And it turns out that that did compared quite well with our COLE values. And so here you have the core values on the y axis and the slope of the soil moisture release curves on the x axis. And you can see those different classifications of how expansive the soil is, as it goes as the slope. That’s the slope decreases. So. So yeah, that was a really easy way to use a soil moisture release curve to predict how the how expansive the soil is. But can we go beyond beyond that? Can we use the information in the soil moisture release curves to predict cation capacity, or soil specific surface area? All these measures are different measurements that take time and sometimes use chemicals like all these, can we use these, this information from one measurement to predict all these properties? Well, turns out there’s quite a bit of work being done on this. And here’s an example of some of that work being done by some of our colleagues at Colorado School of Mines. And, and at University of Wisconsin and others that are working in this area. So here we took that those same curves that I showed earlier. So here you see those curves on the right, and took those soils and apply the tools that are being developed by these researchers to predict soil specific surface area to predict cation exchange capacity and to protect and predict the swelling potential. This is a little more advanced utilization of these of these curves to predict the potential based on the internal surface area, or sorry, the internal cation exchange capacity. And you can see in this table, and I’m not gonna dive super deep into all of the results. But you can see in this table how the VSA predicted results like cation exchange capacity compares with the cation exchange capacity results from the lab. And what you see is that they actually compare quite well. Now, there’s still more work to be done on this to improve these models and give them more data to learn from. But what we’re seeing is that that we can use these tools to predict these properties like cation exchange capacity and dive even deeper into it. And separate out things like external and internal cation exchange capacity, which tells us quite a bit about the clay behavior and those things in the soil. We can also use them to predict soil specific surface area. Now we don’t have lab data to compare with this, where you can see examples of what those predictions look like and, and as you would imagine, for certain soils, especially more expansive soils, they’re likely going to have a higher higher soil specific surface area. And that is the trend that we see with these results. And it also compared well with our COLE estimates of how expansive the soils were. So what I’m trying to say here is that there’s a lot of work being done in how we can utilize soil moisture release curves to predict some of these fundamental soil properties. And there’s a growing body of work being done in this area by researchers like Ning Lu, and all the researchers listed here on this. On these references, I just wanted to throw these up here, if you want to learn more about the way these tools are being developed and what’s being done, there’s others that are working on this as well. So this is not the only body of work to pull from. But just wanted to give you some examples to utilize if you’re interested in learning more about that. Okay, so we’ve talked a lot about how we can utilize soil moisture release curves to learn a lot about our soils. But one of the most common ways we use soil moisture release curves is to generate models. And then to put those models into our hydro- hydrology models where we’re using this information to predict water movement in soils, predict the risk of flooding, and all of these different things as we’re trying to understand data on hydrology, and one of the things that I think is really important to understand is, it’s great that we have these tools, but it’s really important to understand how to use the models and which models are best for your data. So I’m going to talk a little bit about how these models, how these models behave, and how to how to optimize and choose the right model for your data. So there are several different models available to fit soil moisture release curves, you can see some of those examples of the models that we use in the in the laboratory SoilView software. You have Brooks and Corey for the Xing, Kosugi, Van Genuchten unconstrained and constrained models, and the different variants of those models that are available. Because researchers have continued to work on improving and making these models better. But it’s really important that when you’re thinking about all these different models, there are some things you need to consider. One is how are you using the model is your soil bimodal, do you see that by modal behavior that’s similar to what we should have behaved before, you need to understand some of those things. But also, there might be limitations on which models you can use for your tools. So that is also something to think about. But I wanted to show a couple examples of different soil types, and how some of these models fit those soils. And how we chose the right model to to best describe the behavior of that soil moisture release curve. So here we have an example of a mineral soil, this is going to be a little bit coarser, textured soil. And one of the most common models that people would typically use is one of the traditional Van Genuchten equations. And you can see on that, on the left here, what that model looks like and how it fit the data. One of the challenges with a traditional Van Genuchten equation is they use a, there’s a variable in there called data are are the residual water content. And that’s kind of the lower end, which we expect the soil to dry down to. Now historically, we thought that there was truly a residual water content in there was like a max limit that the soil can dry down to. And so that’s why that was put into the model. And what we’ve learned over time is that actually, that’s not true, the soil can dry to a true zero water content. But this is what the model uses. And this is one of the most common models used in a lot of hydrology models and things like that. So so as you can see, it doesn’t quite fit the drying behavior of the soil very well. It also has some challenges on the wet end, as well. And I did try fitting some of the different Van Genuchten variants to the soil. But turns out that the model that best describes the behavior of this soil was the Fredlund Xing PDI variant. Now is Fredlund Xing going to apply to all soils? No, the different models fit the behavior of different soils better, but in the case of this soil, the Fredlund Xing PDI variant was the better model to use. So that’s what we went forward and what we chose to fit the data for the soil. Now if I didn’t have the Fredlund Xing equation in maybe the tool that I was using to apply these data to, then I might have to choose a different model to fit it. But ideally, we’d have different models available to us to best describe the behavior of the soil. Okay, so we’ve focused on a mineral soil. Let’s now think about a non soil media. So here we have an example of a soilless media where we are where we have two different models that we’re trying to fit to it again, again, we have the traditional Van Genuchten equation on the left, and you can see that the very traditional Van Genuchten equation does an okay job again on the wet end just like it did on the mineral soil. But it really struggles on the dry end describing that behavior. And one of the other challenges is that most soilless mediums have some sort of a bimodal behavior. And so, so really this the this traditional Van Genuchten equation wasn’t the best fit again for the soils. And it turns out for this soil, the best fit to describe the behavior of this was the Van Genuchten bimodal PDI variant of this equation. And here you can see it describes it quite well, the fit the fit is very, very strong. And, and this is the best equation to use to fit these data. Now, the bimodal equation is a little more complex, which is always a little more challenging. But if we’re trying to best predict our data, this is what I would use. And so, so it’s really important to understand that there, these models all behave differently, and they fit different soils differently. And so, ideally, we can optimize and choose the best model to fit our data, if not, try to try to just use the best model that works within constraints that you have.

So to close things out, I just want to summarize a few things. Soil moisture release curves are really powerful tools, we can use them to characterize souls for very traditional uses, like irrigation decisions. But we’re learning that we can go well beyond our traditional uses of soil moisture, release curves to more quickly characterize soil, and really predict some of these fundamental soil properties. And there’s a ton of work that needs to be done on this. And so the second point is that there is more work that needs to be done. So we can better understand how we can learn from these tools, utilize information that’s within the soil moisture release curve, and build our capabilities. And this has continued to be done. And I’m really looking forward to seeing what additional work comes out of this, how we can use these powerful tools. And then lastly, choosing the right tools and the right models will make a big difference in our ability to utilize soil moisture release curves beyond what we’ve done in the past. Choose choosing the right measure sensors and instruments to make your measurements makes a big difference, especially in really refining our ability to characterize and understand what the how these souls behave and what these curves look like. And again, it’s always you know, there’s a lot of models available. Choose the right model that fits with what within your application and what you can do, and best describes your data. And with that, thank you,

BRAD NEWBOLD 32:25
all right. Thanks, Leo. So we’d like to use the next 10 minutes or so to take some questions from the audience. And thank you to everyone who’s sent in some questions that we’ve had several come in already. And there’s still plenty of time to submit your questions now if you’d like and we’ll get to as many as we can before we finish. One caveat is if we do not get your questions, don’t worry. We do have them recorded and Leo or somebody else from our METER environment team will be able to get back to you to answer your question directly via email. All right. So our first question today, they’re asking, is there a technically valid distinction between matric potential and sorptive potential?

LEO RIVERA 33:10
That’s a good question. Don’t you when we typically describe water potential, we focus on four variables matric osmotic, gravitational and pressure potential. And in unsaturated conditions, it’s primarily matric and osmotic. And I believe sorptive potential is just a function of, of matric potential and it depends on just as matric potential does. It depends on the water content and and the makeup of that soil. And how well that water is going to how it’s going to pull that soil in different directions. But they’re they’re very related. I don’t know the exact distinction between an helmet and how much those how I would define the difference between those two.

BRAD NEWBOLD 34:03
Fair enough. Really quickly, is it possible to develop an accurate release curve using only volumetric water content?

LEO RIVERA 34:10
Oh, that would be really challenging because we’d need to characterize the water potential. Now, there are tools available that you can use to predict soil moisture release curves using pedotransfer functions. And if you only have water content data, that’s the way I would go. Now, you need to understand that there are limitations and how well those pedotransfer functions work. Rosetta, for example, is one that you can use and there are there other ways to predict the soil moisture release curves. But the more information you can put put into those models, the better they’re going to predict the way those those soil moisture release curves would behave. But again, they’re just predictions.

BRAD NEWBOLD 34:53
All right. Next one, can we extrapolate the curves of the smrc for deeper depths, for example, 10 or 20 feet, understanding uniform soil profile assumptions, and this is specifically for stormwater management bio remediation applications.

LEO RIVERA 35:10
Yeah, absolutely. You can if you have well characterized the soils down to that depth, which is pretty deep. But I know in some applications, you do do that, you can absolutely take this information and extrapolate the way the curves, but not only that, but also the way the water is going to distribute within the profile. And if you have this information, if you know, the typical soil moisture release curve, it makes it a lot easier to predict what that distribution looks like if you know that soil makeup to those depths, though, yeah, you can absolutely do that.

BRAD NEWBOLD 35:46
All right. This individual is asking regarding the usage of SMRC, for irrigation that you showed how to the wetting curves, and not just the drying curves influence irrigation requirements?

LEO RIVERA 35:57
That’s a great question. And I did not touch on hysteresis in the wet and not wet range hysteresis does make a difference. Especially we know that soil has what we call scanning curves. And as we go between different wetting and drying portions of the curve, you’re gonna see slightly different behavior. It depends on the soil, some soils have larger histories of sleeps than others. And it’s kind of hard to predict that now, if we can make these measurements in the field and measure both water content and water potential simultaneously. That makes it a lot easier because you know, the behavior and what’s happening in the soil as we go through these different wetting and drying cycles. But it is kind of hard to do that just based on a wetting curve, or a drying curve, which the examples I showed were drying curves. But yeah, that’s a great question. And it’s something that definitely poses challenges that we didn’t really quite touch on and are a little bit harder to deal with just based on one set of data.

BRAD NEWBOLD 37:02
All right. This next individual, I think, is asking if you could go into more detail about moisture retention behavior of poor granulated soils and how that varies.

LEO RIVERA 37:14
I’m thinking okay, the if I want to make sure I’m interpreting the question, right, but I’m assuming they mean, poorly graded soils is likely what they’re what you’re referring to, and poorly graded soils, which typically you’re going to have fairly uniform pore size, or, yeah, it’s going to have a lot of the same pore sizes, you typically see much steeper responses in the soil moisture release curve as those pores drain, because typically what’s happening with the soil moisture release curve, you see that response as we start draining out the bigger pores and you for well graded soils, we see a pretty smooth response in the soil moisture release curve. Whereas a poorly graded soil, you’re typically going to see a really dramatic change in the water potential as we drain the larger pores, because once you drain the larger pores, they’re going to drain quickly. And then you’re going to be left with just what’s the some of the smaller pores that remain. And so we typically see this, especially with like sandy soils, and things like that, that they you know, they have very steep and dramatic responses and soil moisture release curve.

BRAD NEWBOLD 38:23
Alright, I think we’ll get to a couple more. This, this next one’s asking you about about air entry. And could you please tell us about the importance of air entry and how they might be able to see those on the curve?

LEO RIVERA 38:37
Yeah, it’s so great question. I mean, there definitely several implications as an entry point. Because that’s usually, once we hit the air entry, that’s where we start to see the saturation of the soil. But it’s really easy to find the air entry point in the soil moisture release curve. And, and you can see that when you start desaturate in the soil, there’s typically a very flat initial response in the soil moisture release curve. And as soon as we start to see that, that dip in the curve, that means we’re hitting that airentry point. And if for example, we showed that bimodal occur where you’re actually hitting two air entry points of the pores. So you can use that information to soil moisture release curve to identify where the air entry point is. But there are a lot of implications of that. Probably well beyond what I can cover today.

BRAD NEWBOLD 39:29
All right, two more. This individual is studying soil science particularly interested in the impact of wildfires on forest soils. Could you just elaborate on how soil moisture release curves might be used to interpret the changes in soil moisture behavior in forest soils affected by wildfire?

LEO RIVERA 39:48
Yeah, that’s a great question. And that’s a really growing area of research. As you can imagine, we have a lot of challenges with with soils impacted by forest fires. Soil moisture release curves can definitely be valuable just in terms of understanding the the water potential changes in soils. Now the more important pieces is that these soils that are impacted by forest fires will see a change in their behavior in the in the soil moisture release curve. And so that’s the thing that’s really important to understand is, as the soils are burned, we see changes in the pore makeup and the organic makeup of the soils. And, and also just the the soils become hydrophobic typically. And so you do see very different behaviors of these fire impacted soils. Actually, I think we were working with one of a student at University of Idaho as a part of we teach an environmental biophysics course at Washington State and their class project was measuring soil moisture release curves of somebody’s burn, burned soils, and they solve some pretty interesting behavior in those in those soils.

BRAD NEWBOLD 41:04
All right. Final question. And again, for those of you that did not have your questions answered, we do have the recorded, we will be able to get back to this last one. And this is something that you’ve touched on a little bit. They’re asking, Does METER have any instruments to measure the wetting curves for the wet ends, but maybe you could touch on some of the other instrumentation that we have for for this measurement here?

LEO RIVERA 41:26
Yeah, that is a great question. And, and the only tool that we have that will get kind of into the wet end, where we can do a wedding curve is is the WP4C. Because we can can do wetting, we can wet soils to different points to measure them in the WP4C. However, the high props of the tools that are in the very wet end, only measure on the drying curve currently. And ideally, you know, in the future, we would like to see the ability to do both wetting and drying crews with tools on the wet end. But we don’t have that yet. We do have tools like the VSA, obviously, they can do wetting and drying curve and the dry end. But that doesn’t really cover that. And you can do this in the lab, if you use tools like the the HYPROP uses many tensiometers, that are the same as what we use for the TEROS 31. And you can use those tools to generate wedding curves in the lab, in columns. And so that is one way to do it by combining the sensors and actually, especially if you can combine them with water content sensors, which we’ve seen some people do to actually generate wish release curves in columns that way. So we don’t have any automated tools, but there are ways to use some of these sensors to generate the information in a lab.

BRAD NEWBOLD 42:52
Okay, that’s going to wrap it up for us today. Thank you again for joining us. We hope that you enjoyed this discussion. And thank you again for all your great questions. 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 for more information on what you’ve seen today, please visit us at metergroup.com. Finally, look for the recording of today’s presentation in your email and stay tuned for future METER webinars. Thanks again, stay safe and have a great day.