Surprises that leave you stumped

Soil moisture data analysis is often straightforward, but it can leave you scratching your head with more questions than answers. There’s no substitute for a little experience when looking at surprising soil moisture behavior.

Understand what’s happening at your site

METER soil scientist, Dr. Colin Campbell has spent nearly 20 years looking at problematic and surprising soil moisture data. In this 30-minute webinar, followed by a ~15-minute Q&A, he discusses what to expect in different soil, environmental, and site situations and how to interpret that data effectively. Learn about:

• Telltale sensor behavior in different soil types (coarse vs. fine, clay vs. sand)
• Possible causes of smaller than expected changes in water content
• Factors that may cause unexpected jumps and drops in the data
• What happens to dielectric sensors when soil freezes and other odd phenomena
• Surprising situations and how to interpret them
• Undiagnosed problems that affect plant-available water or water movement
• Why sensors in the same field or same profile don’t agree
• Problems you might see in surface installations

Next steps

• See slide share
• Webinar: Part 2-How to Interpret Soil Moisture Data
• Article: The researcher’s complete guide to soil moisture
• Article: Why soil moisture sensors can’t tell you everything you need to know
• Related webinar: 8 Research-ruining data mistakes
• Request a ZENTRA Cloud live demo

Questions?

Our scientists have decades of experience helping researchers and growers measure the soil-plant-atmosphere continuum.

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• Request a quote

Presenter

Dr. Colin Campbell has been a research scientist at METER for 20 years following his Ph.D. at Texas A&M University in Soil Physics.  He is currently serving as Vice President of METER Environment. He is also adjunct faculty with the Dept. of Crop and Soil Sciences at Washington State University where he co-teaches Environmental Biophysics, a class he took over from his father, Gaylon, nearly 20 years ago.  Dr. Campbell’s early research focused on field-scale measurements of CO₂ and water vapor flux but has shifted toward moisture and heat flow instrumentation for the soil-plant-atmosphere continuum.

Transcript

BRAD NEWBOLD 0:06
Hello everyone, and welcome to How to Interpret Soil Moisture Data. Today’s presentation will be about 30 minutes, followed by about 10 minutes of Q&A with our presenter Colin Campbell, whom I’ll 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 these for the Q&A session toward the end. Second, if you want us to go back or repeat something you missed, or if there happens to be any technical difficulties in any way, don’t worry, we’ll 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 Dr. Colin Campbell, who will discuss problematic and surprising soil moisture data, what to expect in different soil environmental and site situations, and how to interpret that data effectively. Colin Campbell has been a research scientist at METER for 19 years following his PhD at Texas A&M University in soil physics. He is currently serving as Vice President of METER Environment. He is also adjunct faculty with the Department of Crop and Soil Sciences at Washington State University, where he coteaches environmental biophysics, a class he took over from his father Gaylon nearly 20 years ago. Colin’s early research focused on field scale measurements of CO2 and water vapor flux, but has shifted toward moisture and heat flow instrumentation for the soil plant atmosphere continuum. So without further ado, I’ll hand it over to Colin to get started.

COLIN CAMPBELL 1:42
Thanks, Brad. And thanks to all of you for attending today. I’m so excited to spend a little time with the nearly 1000 of you who signed up for this seminar. It’s truly exciting to know that there are so many people out there wanting to talk about soil moisture data and discovering some of the things that it means. And I hope that when we talk today, you can just sit back and relax and enjoy the discussion. There’s going to be a lot of data here. And there’s no way you can know everything about these data. It’s kind of like looking at pictures from a friend who’s gone out on holiday somewhere. You’re never going to know all the context behind the pictures. But as you sit and look at them, you can certainly enjoy some of the meaning and some of the interesting things that were discovered during that time. So let’s get started. I titled this, How to Interpret Soil Moisture Data, and subtitled it, Discovering the Meaning Behind the Traces. Because in a lot of cases, initially looking at the data, I really didn’t have any idea exactly what was going on, and had to spend a little time really thinking about the problem, and about where the sensors were, what soil they were in, what crops or what native vegetation was growing on top, etc, etc. And that’s what we’re going to do together today. Now, one more caveat before I start. I know many of you have different backgrounds and different experiences. And it may be difficult in some cases for you to understand the datasets I’m talking about. But I’ve tried to throw in a lot of different datasets here, so that whether you’re from an agricultural background, a ecological background, even a background in geotechnical engineering, that you might be able to have some datasets that will be of interest to you.

COLIN CAMPBELL 3:32
So let me just start off by a story. Many years ago, I was working in the support area of METER, formerly Decagon. And I talked to a young lady who was a graduate student, who was frustrated by the strangeness of her water content curves over time. And one of the biggest things she was asking questions about is, Why do I see so many different water contents in the same profile that I buried these sensors in? And I dug back through my archives and found these data. I don’t think they’re from the specific location that she was asking about. But basically, I said, you know, you got to expect the unexpected. You can’t know beforehand what your water content sensors are going to tell you. If you did, then there’d be no reason to go out and experiment. And in this specific location where we had this wide range of water contents, I encouraged her to go out and sample the soil and learn a little bit more about what was going on. When she dug down, she found that some of her sensors were buried in a quite sandy soil, and some were buried in these clay ribbons that were running throughout the soil. And the water content ended up being relatively high. And she didn’t know that at the time she installed the sensors first, but found it when she went down and dug in this former riverbed. So we’re going to talk about a lot of different things today. First, we’re going to go through how soil moisture sensors behave in different soil types. We’ll just use a couple here, a loamy sand and a clay loam. Then we’ll talk about soil infiltration and water in two different cases where we have a really dry soil where water is infiltrating and a saturated soil where we actually got flooding. We’re going to talk about soil cracking, then talk about soil freezing and what happens to our measurements. We’re going to talk about spatial variability and temperature effects on water content. We’re going to move into diurnal patterns that look like temperature effects that may be something like hydraulic redistribution. And then we’re going to finally finish off with, what does a broken sensor look like? And what happens when there are installation problems, and how can I spot them? So let’s jump in.

COLIN CAMPBELL 5:52
So first of all soil type effects. We’re going to first look at a loamy sand. And I want to orient you to the graphs that I’m going to be showing you. Each graph will have a little box up here on the top right hand side. It’s going to tell you the soil type, and it’s going to tell you the crop type or the native vegetation that’s on top. The whole goal here is that if I forget to tell you exactly what we’re working in, you’ll be able to quickly reference that soil type. In this case, we’re in a loamy sand. This is an engineered soil, and the cover crop is a turf grass with a fairly shallow root zone between about— the middle of the root zone is about six centimeters, and the bottom of the root zone is about 10 centimeters. And in this case, we’re going to look at this soil over time in this experiment where we were in a relatively wet condition to start with in June and into July, in a fixed drying condition in July and into August, and then a drying until we saw cessation of water uptake in August and in September. This was a larger project that I don’t have time to explain, but our interest was trying to improve irrigation in turfgrass. Here we’re showing two soil moisture types. One is volumetric water content which is going to be on the left y axis and matric potential or water potential, which is going to be on the right y axis. And here’s time on the x axis, going from early summer into just the start of fall. But we’re going to look at each of these datasets a little bit more closely. So this gives you a taste, and now we’re going to talk about it. Here are some wet conditions that we’re going to start off with. And what we’ll notice is that in this loamy sand, the water potential sensors didn’t respond much at all, while the water content sensors really show some incredible detail. Each irrigation event—and we’re talking on the order of days here—we can see that we irrigated at night and we see a nice spike up when the water hit the sand here at six centimeters, and also we see a little spike down at 15 centimeters or the bottom of the root zone. Even at 30 centimeters, we see an increase in water content, although the curve is not so spike shaped, it’s more rounded off. But we see that through the profile. Here in the water potential, we don’t see any change at all. It’s so wet in the soil that and the particle sizes are so large that we just don’t get any holding of that water by the particles that we can see with this sensor. If we change now and look at what happens in this loamy sand at optimal conditions, we see some pretty cool detail. Here in this six centimeter level, we see a flattening off during the night and a drop during the day of our water content signal. And this happens day after day and gives us an idea of how much water the plants are taking up. At 15 centimeters or right at the bottom of that root zone, we also see this daily drop but it’s not so pronounced because we’re right on the edge of where the roots are taking up water. Down at 30 centimeters, now we’re not washing so much water down through the profile, which is a really good thing. We do see a little peak here. But in a subsequent irrigation event we don’t see it here. But again, this loamy sand is very responsive to the water that’s being applied. Here in the water potential data we do finally see a small response at the six centimeter level. This doesn’t indicate any stress, as it’s only dropping down into the maybe negative 200 to 400 kPa range. But we do finally see something going on at that level. Now in drought conditions, we again see this beautiful water uptake at the six centimeter level, and also more gradual but at the 15 centimeter level as well. But the interesting thing about this data set is we can see this uptake on a daily basis until suddenly, we stop here. And this is the point that the grass starts to not be able to take water from the soil. And essentially, we’re changing that grass from actively growing into dormancy. And of course, here in our water potential, we see a really interesting curve now, where we’re moving the water potential down into the lower regions near negative 1500 kPa or permanent wilting point. So we’ve actually caused a dormancy in this grass because the water just isn’t available for the grass to take up. And again, we’re seeing all this great picture out of the water content sensors, and we’re seeing what’s happening in terms of matric potential, and the fact that there isn’t water to take up, but they didn’t respond until the soil got very dry.

COLIN CAMPBELL 10:55
Now let’s shift our attention to a different soil type. What happens when the soil is not a loamy sand, but now a pretty fine textured soil, a clay loam? This is a clay loam, as I mentioned, that’s growing seed potatoes in southern Idaho. This is a field here from a satellite view, it’s almost 700 meters in diameter. And in that field, we installed six sites with water content sensors at 15, 45, and 75 centimeters. And in this graph, I’m only showing you the water content sensors at 45 centimeters, which actually was the most active region in this study. So now we have water content on the y axis and time going from early June all the way to the end of August when the potatoes were killed in preparation for harvest. In this graph, we see that the water content changed very little. So all the way across the season, we did get a little water content change, but it was on the order of two to three percent. And the grower who I was working with asked me, Well, how do I determine when it’s time to turn the water off? And frankly, it was really hard for me to tell him anything about this. Now, the question is, Does that mean that there was no stress in the field and that all the water content sensor— or all the potatoes were irrigated at an expected level where they wouldn’t have stress? Well the reality is, no. Here’s matric potential data for that same field across the same time. So again, we’re still in clay loam, still in seed potatoes in the same field. But now we’re looking at matric potential over on the y axis, and the same time on the x axis. I also included a little table at the bottom to help you see the ranges that we expect plants to be at optimal versus when they’re going into stress. So from negative 100 down, plants are in stress. And we see that three of those locations that we were testing out of the total of six, were actually in stress for most of the season. The question is, were these sensors even working? Is this real stress because the water content sensors didn’t show much? Well, in terms of the leaf temperature, which was much higher than the air temperature for these locations, and in terms of the yield from those locations, which only produced about 75% of what the yield did in these nonstress locations, we can answer that question of yes, these sensors were working and they were telling us important information that we didn’t see in the water content sensors.

COLIN CAMPBELL 13:45
Let’s talk a little bit about infiltration. So our first case is a sandy loam soil. This sandy loam is found in a desert. And this is in the fall, well the summer and fall of last year, of 2019. So the “crop” quote unquote, that’s on top of here is actually is lots of desert invasive species. So of course, this is a native system, where it’s just growing things like cheat grass, and we’re trying to understand how cheat grass and other plants like halogeton are out competing the native grasses. And here, I looked at this data set and started thinking about the fact that I couldn’t really see in the gray line here, which is the five centimeter water content level and comparing it to 10 centimeter here in the red, and the yellow is 20 centimeters. I couldn’t see these events where we had rain falling, and it’s not an insignificant amount of rain. It’s about four millimeters of rain on several occasions here. The maximum is around there. So why didn’t I see the rain events show up in my water content sensors? Well, I think there are two reasons we’re not seeing this. One is that we’re just after a very long, hot and dry summer, where it’s very likely that the heat, or the temperature of the soil, which got up over 40 degrees Celsius, pretty much every day was actually hydrophobic. So the infiltration of water would first require that the surface be wetted by that precipitation event before we get any infiltration. And during this time, there was still an incredible evaporative demand that was drying that water into the atmosphere and evaporating it instead of actually allowing it to infiltrate into the soil. The other point is that the soil was powdery dry, so any water that fell on that soil was getting taken up right there at the surface and held before evaporating again. Now moving to another time period, this is earlier in the year, May into June. And this is actually before we got our precipitation sensor set up. These data are from ZENTRA Cloud, METER’s cloud software that helped me visualize it, and it said it was there, but we hadn’t quite installed it. So that is not quite correct that we have the precipitation. These are all precipitation events that have happened there, presumably. And we see an interesting pattern where this five centimeter sensor begins to increase in its water content at three places, but we only see a resulting increase in water content in the lower regions on the second event here at 10 centimeters, and only on the third event here at 20 centimeters. So why does that happen? Well, of course, the upper regions of the soil, the upper volume of the soil is taking up water, that’s one reason. And it simply takes time for that water to fill that region and allow some flow, especially when the soil is very dry, to reach the lower levels. And again, we have a really high evaporative demand there, which is taking up much of the water that falls on the surface. And it doesn’t make it down there. Interestingly enough, we have a precipitation event here that wets the five centimeter. It wets the 20 centimeter a little bit, but the 10 centimeter only goes up slightly. Well, why does that happen? It’s a little bit confusing. Well, I don’t know exactly. But one thing to remember is that when water infiltrates in the soil, it doesn’t do it as a block. In our minds, we probably imagine that water is just flowing into the soil with this uniform front that’s going down through, but that’s really not true. Water actually moves in the soil in fingers. And there have been a lot of studies to show that with dye tracers that, in fact, it doesn’t just go as one giant block, but it comes down in fingers down into the soil. And it’s likely that one of those fingers reached down and maybe went to this 20 centimeter sensor and wasn’t necessarily quite as connected to this 10 centimeter sensor. So I’m not surprised to see that. But I will be watching this over time to make sure we do get consistent infiltration in those three to make sure there’s not some problem with infiltration above that 10 centimeter sensor.

COLIN CAMPBELL 18:28
What about going all the way to the other end of the spectrum and looking at what might happen during a flood event. So this is on a silt loam, and it was actually located right here in Pullman, Washington, where METER is located. About a year ago, we had a terrible flood event where in a very small stream that’s in the center of this picture, we suddenly got water overflowing its banks and turning our main street, Grand Avenue, into a giant river of muddy water flowing down. Several businesses were flooded and it wasn’t particularly great for the community. Here’s another picture from the other side that shows the Missouri Flat Creek now turned into a torrent and flooding those businesses. So how did this happen, especially when we look at some of the details behind it? Here’s the precipitation over time for the week preceding the flood. And if you look at that and look at the scale here on the y axis, this is in millimeters. So our biggest rain was only about three millimeters. And after looking at that desert soil analysis, maybe you’re wondering, Well shoot, that’s not much rain. Why did it flood? I mean, do we measure it right? And yes, there are multiple rain gauges here and they’re all saying the exact same thing in terms of the moisture that precipitated. And so what is going on?

COLIN CAMPBELL 19:55
Well, here’s the volumetric water content over time for five sensors buried on a hillslope not far from where the Missouri Flat Creek runs. On the y axis here is the water content. And notice the range is not very large, 32% up to 44%, and the water content at 20, 40, 60, 90 centimeters, and then one buried all the way down 120 centimeters. And what we’re looking at is just the change in this water content over time. And those initial precipitation events, we didn’t see much going on. But around April 6, we got a pretty good rain event. And we see the increase in water content at the 20 and the 40 centimeter level, and the 60 centimeters starts to increase. But what I found so interesting about this dataset was not that we are seeing water contents increase up in the upper horizons, that’s pretty typical. But we see this table topping effect where we see a water content rise all the way up, and then flatten out here at the top. And when I’ve seen that before, I always wonder to myself is, Are we having a sensor that works? Is there some problem that it’s maybe jumping up too high and then stop reading? But this is a situation where we can get a pretty good feeling that we’re likely seeing some saturation in the soil. But the question then is, If we saw saturation here at the 60 centimeter level, why didn’t it flood on April 7? Well, up here at the surface level, by the time we saw saturation down here at 60 centimeters, and a little bit here at 40 and at 20 centimeters, we also see this decreases. The water content drains into the lower regions of the soil. And I suppose that must be why. Now we see several events hitting a little closer together. As it rained a little bit more frequently, we see that the 60 centimeters filled up to a pretty high level, not its maximum. And then all these other sensors start to flatten out. And I think that should be a little bit of a warning sign that we may be approaching saturation in the soil. And then we had this long, sustained rains starting here on April 9, and in fact, it only took about an hour and a half of that continuous rain till each of these levels that we’ve been watching, the 20, 40 and 60 saturated, and we got flooding, which we can see here. Well, here’s that rain event. And you can see how continuous was not a ton of rain over time. I was out and it was certainly wet, and we got pretty soaked, but it wasn’t kind of epic rain that I felt. But here’s the water depth in the Missouri Flat Creek just down from those water content sensors. And you can see that that we quickly reached this maximum water level up from about one meter all the way up over two meters. And that’s when it flooded. So an interesting thing happening, interesting to see that behavior in the soil as we jump up to these maximums and tabletop out and how that resulted in some flooding.

COLIN CAMPBELL 23:22
Okay, from flooding to cracking, this is ships clay, a high shrink swell clay. And this is located down in south Texas. And we installed several sensors in there just to observe what would happen as we measured water content. And the reason we’re interested in this is to really discover what our readings might be, should we have some cracking. So at each depth, we buried a couple sensors. So I want to pay particular attention to these 20 centimeter sensors. These are TEROS 12 sensors. So you’re measuring other things like temperature, and electrical conductivity. But here we’re only focused on the water content. And so these these sensors, if you notice way back here, they start about the same water content every time we get a wetting event from precipitation. But what happens after we wet is that one sensor has a trajectory that looks about like what I expect, but the other drops off very quickly, more like it was in a sand and then flattens out over time. And the question we started asking ourselves is, What’s going on? This behavior is quite uncommon to see in a clay. And we’ve come to the conclusion that in fact the problem is that this sensor here, the lower sensor, actually has soil around it, has a high shrink swell capacity that when it starts to dry, it’s pulling away from the sensor, and it’s pulling away enough to actually produce air gaps, and when an electromagnetic sensor senses an air gap, it actually doesn’t read as high, as you probably already know. And so what we’re seeing here, this really precipitous drop here in a clay, again with a native grass growing on top, is that essentially we have a problem with cracking. And that’s something to look for if your water contents are going down much faster than you thought in your fine texture soil, you may have cracking around the sensors, and it’s an important time to go out and look and see what’s going on and maybe try to do a better job of installation.

COLIN CAMPBELL 25:37
Okay, what about freezing? Now this example is in a sandy loam. So we’ve already talked about this site. This is a sandy loam soil with invasive species on top, and of course, they’re dead. This is from last winter into just this spring, from early November into just fairly recently, in March, when I collected these data— or collected them, I just took a screenshot of ZENTRA Cloud as I prepared for this virtual seminar. So here are precipitation events, and I was just watching this data as it went along. That’s the great thing about as ZENTRA Cloud. I can do that from my desk. So I log in regularly and just see what’s going on. And I started to see some very strange things that we’d have a spike up here, and then we’d have this just jagged line dropping down to a low point, then it’d jump up, then it dropped down. And I’m like, Man, did this sensor get unburied? Is there some problem with the circuitry? What’s going on with that? You know, here, we just jump up, and I thought, Oh, this makes sense. Here’s a irrigation event but— or sorry, a precipitation event. But oddly enough, it kind of trends up, trends down, but it’s spiky, what’s going on here? And so I went in and added in temperature and I hid that from you, just for effect. I started looking at temperature and that black line I just drew in here in PowerPoint by hand. But the point of it is just to show you where the zero degrees C line. So this is our these are TEROS 11s that have water content and temperature. So I could measure the temperature at each one of these locations. And you see this green line is temperature and every time it freezes, the water content drops down. When it thaws, it jumps up; when it freezes, it jumps down. And now I saw this relationship between the temperature and the water content. It all makes sense to me that when water freezes, and the more it freezes, the more the water molecules themselves disappear to the electromagnetic sensor that is trying to polarize those water molecules in a magnetic field. And so when they disappear, to a greater or lesser degree, then we see the water content drop down. Now all the water isn’t frozen, and therefore, the water content doesn’t go to zero. But it does go to a much lower number here. And we can see it going up and going down. And then finally, when we thaw for the season, we see the water content just smooth out and look like what we had seen before, as it did way back here in November. So here’s a little bit more close up, we see this behavior, here’s zero degrees. It’s thawed here, it’s frozen here. And you can just get a feel for what that looks like when we have a freezing temperatures. And now I’m going to show you a big picture. This is from an entire year’s worth of data. So you can see all the trending in the water content data, it looks just like it should for the summertime, we see the temperature going up and going down out there. We see even in that soil down at five centimeters, we reached almost 40 degrees Celsius. And we see these beautiful curves in the water content data. But suddenly, when we get out here and get so freezing, it’s just jumping around like crazy. I drew a dotted line in here to show you what should be the trends in water content here. And then in fact, when we do finally get thawing out there, the water content goes back to normal.

COLIN CAMPBELL 29:12
Okay, what about this question of spatial variability? We’re going to look at this in terms of a clay loam. So this is a project where we put sensors in seven different fields that are all fairly close together. They’re all within about three or four kilometers of each other. Now when you look at those different water contents for the various fields, again, these are growing seed potatoes, but this is clay loam. We see a variation that when I look at it, I don’t see that much variation, but maybe when you look, maybe it’ll seem like quite a bit. It’s about a range of about 7% water content. And of course through the season we see a lot of variation, but right here, these sensors are all installed after a pretty wet winter where we had a lot of snow, and then we had some rain. So all of these soils, I would expect to be field capacity. And yet, suddenly, we’re looking at water content that varies quite a bit. Why does that happen? Well, it’s really a function of soil type. And it’s something to remember when we go and install water content sensors, that they’re not all going to read the same. They’re going to be reading differently. And so we need to be able to set full and refill points based on the soil types and not just based on a specific value. If we want specific values, we might decide to use water potential. Here are all those seven same fields at the same depth, 30 centimeters, and we can now look at the water potential of all those sites. And from this graph, we see that the water potential is all within plus or minus 10 kPa in all of those fields, which is just incredible. It all starts basically at the same place. But it’s something we’d also assume based on our knowledge of water potential, that these fields, because of being wet from the overwinter precipitation, should be all starting about the same energy state.

COLIN CAMPBELL 31:13
What about temperature sensitivity? Let’s jump back to our sandy loam soil out in that desert. And so this is a little bit of summertime data. Now, our invasive species, our cheat grass has pretty much died off, the water content down at kind of nominal level. And we see this little pattern, day and night, going up and down. And we wonder, Well, what’s causing that? Is there some water moving around in the soil? Is there something happening? And here, I would depend on a statement my dad made many years ago, which he said, You know, it’s true, we love sensors. But most sensors, beyond what they measure themselves, are also usually pretty good temperature sensors. And that’s really true, I found in a lot of the sensors I’ve worked on. And so we measure really well our water content here. And it does quite nicely. But with the change in temperature in this region in mid August being a range of 13 to 14 degrees Celsius down here at the five centimeter level, our water content is changing about a little less than 1%. And it’s not that big a deal. As you look at it, you see it going up and down, it was something I really did expect. And when we do the numbers, it’s actually three hundreds of a percent water content per degree c, which isn’t much, but it’s there. And we need to recognize that it happens. We also need to avoid making any assumption about those data that might relate to actual water movement. Because at five centimeters, we’ve got to see temperature change, and it’s very likely going to affect our measurement a little bit. But we also have to be a little careful not to just paint everything with that same brush.

COLIN CAMPBELL 33:00
So I’m going to show you a clay loam soil and talk about what I believe is hydraulic redistribution. Here are four graphs that I’m going to show you in succession, that show water content at 15, at 45, and at 65 centimeters in a clay loam, this time growing irrigated wheat. And so these sensors are all in the same field, all within about 500 meters of each other. And we have the temperature measured at 65 centimeters here too. And I’m going to try to show you what I believe is happening is actually water being taken up by the roots. So here’s 15 centimeter water content, we see precipitation and irrigation events across the year. And now we’re running from mid April into the end of August. And we actually turned the irrigation system off in late July. These are a couple of precipitation events that happen. And we see these interesting diurnal pattern in the data. And if we’re sitting at 15 centimeters, maybe our first reaction is to say, Oh, those are definitely temperature related. But remember it certainly into June and in July, we have a full wheat canopy growing on top that’s fully irrigated. So we have a Leaf Area Index of probably four to five and very little radiation making it down to the soil surface. And it’s probably unlikely that we’re getting much of a temperature change at that depth. We see this happening all the way down until interestingly, we’ve turned the water off and the wheat has taken up all the moisture it can there at the 15 centimeter level, and suddenly those diurnal patterns disappear. What about at 45 centimeters? Well, in the June time, we almost see none of those diurnal patterns and they start a little bit in July for some of these locations and especially after we turn the water off in late July and early August, we’re seeing these diurnal patterns where we see a drop in the day and a flattening at night. Now, this doesn’t seem to me to be temperature related, but related to uptake by the wheat. And notice, as we went, the uptakes here in June and early July, we’re seeing this uptake in late July, and then going down at the 65 centimeter level. All these patterns are happening in late July and August when the soil is getting quite dry because of a lack of precipitation and irrigation. Early on, we see almost none of these patterns, but late we’re seeing at several locations across the field, not all of them, but most of them. And just one final thing to try to prove my point here, if we look here at the end of July data, the stair stepping down in this pink line here, and then compare it to the temperature. Notice here that same temperature at 65 centimeters is almost completely flat. It’s not a wide range of temperatures anyway, but we see a flatline here, and this diurnal change here, not in my opinion, related to temperature.

COLIN CAMPBELL 36:14
Okay, a couple of last things before we finish off. Let’s talk about what a fail sensor looks like in a silty clay loam. These are data that were just sent to me recently, and I said, Hey, can I use these in my presentation? And they said, Sure. So lots of buried sensors here going along all reading fairly similarly. I’d look at the screen sensor, if I were running the experiment, just to see if there, you know, we didn’t quite get that one installed right. It’s quite a bit lower than the other ones especially looks like a fairly wet soil. I’m not exactly sure. But I’d certainly look at that one. And this guy was running along just fine and suddenly bouncing around at negative 50% water content. Now, obviously, that’s not true. And obviously, we have a problem. The cool thing is, we’re using ZENTRA Cloud here, and it alerted us and we know what’s going on before we waited several months to see what’s going on. And so my suggestion was, hey, go take a look at that sensor. Maybe there’s a problem with the electronics. Just got the word just before the seminar that in fact the plug on the sensor had been pulled out somewhat, and once they plugged it back in, it started working again. So good news on that, the system is back up and running. There’s no failed sensor, but it’s certainly something to look at anytime we see things like that that aren’t related like to freezing or an issue like we’ve talked about, something you need to go quickly and look at.

COLIN CAMPBELL 37:47
And finally installation problems. This is one of our sites we recently installed here. And this is in our silt loam, it’s pretty wet out there. And we’re getting some readings between 0 and 5% and around 10%. And this is a big concern. If we’re in a silt loam soil with a bare soil surface, so we’re not getting any uptake by any plants, then seeing these data where we get water contents down at 10% and below has to send a red flag to us that maybe there’s a problem. Because silt loam, that’s what should be reading up above or at 30%, in terms of what I expect, so this would be a great time to go check those sensors and maybe reinstall them. Okay, that’s it. That was a fast run through a lot of data. I want to share with you a few final thoughts on this. There are many things to discover about soil moisture in the field. The fact that so many of you joined us today for this virtual seminar must mean that you are also interested in this exciting subject, and I hope we continue the conversation. I want to tell you first, expect to find the unexpected. If you go out and experiment, if you’re a scientist or if you’re someone who wants to grow great crops, the reason you’re putting sensors in is because you don’t know everything. So don’t expect you know everything. And let the sensors tell you some of these things. Be open for new ideas. Soil moisture data behaves differently in coarse soils and fine soils, so spend a little tim, understand the soils you have, and then work with an expert to make sure you get the right measurements you need. A soil’s ability to take up and store water can stop water infiltration, so don’t assume that when you get precipitation, you’re always going to see it in the sensor. Or it can cause soils to flood, and be on the lookout for that when you see sensors table topping. There might be issues down in the soil. Some soils, typically clays, can crack and cause cracks to form around the sensors. And it can really cause problems with results. So be on the lookout for those. That’s why using ZENTRA Cloud, for example, will help you see some of these issues quickly and go fix them. Freezing also can cause soil to disappear to the sensor, and results in strange behavior. And so data that I collected out there at that desert site are really going to have to be reviewed. And some of those data are going to have to be removed from the dataset, if we want to be able to use that for publication purposes. Soil water content has high spatial variability. And so just assume that when you install sensors across the field, if they all read the same, that’s a reason to be concerned about it. If you install water potential sensors, and they all read the same, you’re probably doing something right. So be happy about that. Diurnal patterns in the data can be related to temperature, and be sure to check that, especially if they’re near surfaces. Don’t make any blanket statements saying, Oh, we saw water moving around the soil right near the surface. It may not be true. But don’t disbelieve the sensors. If you’re getting these diurnal changes down, much lower where temperature isn’t changing, that could be root water uptake and redistribution. And problems will arise from poorly installed sensors and also those that fail electronically. Be on the lookout for those and make sure you get them fixed early, because you don’t want to lose those data over time. Okay, my final notes, I use the ZENTRA system a lot. in these data that I’ve shown you today. For me, it’s a key tool to getting the most out of my system. And it’s funny, because now I just expect to be able to drop into my seat and look at these data at any point. It gives you near real time data, it’s simple and fast to visualize. And most important for me, I use those alerts in the critical ranges to make sure everything’s going well in my study. If I took the time to go install these, and pay for the systems to get out there, why not get the best data possible? And also, I just loved being able to share these with my collaborators. I work with a lot of people at a lot of different universities, and without being able to share, w’d forever being passing Excel files back and forth, like we used to. One more thing, METER has collected some of the greatest scientists to be here to support your efforts. And I would strongly encourage you to simply use them. METER scientists know a lot about what you’re trying to accomplish. So involve us in your research. A lot of times we learn a lot more, and we have fun together. We want to be your partners for that. So don’t hesitate to email us at [email protected]. With that, I’ll take questions.

BRAD NEWBOLD 42:40
Awesome. Thank you, Colin. All right. Looks like we’ll have some time for questions here. Maybe we’ll take another 10 minutes or so. Thanks again to everybody who has submitted questions already. There’s still plenty of time to submit those. And I will make a mention of this again at the end, but we definitely will not get to all the questions. We just don’t have time to go through all of them. But we do have them recorded. So even if you want to just submit your question, and all those that we do not get to during our live recording here, Colin or somebody else from our METER Environment team will be able to get back to you and address your question directly. So just wanted to make that known for everybody. All right. Let’s see. We’ve got a bunch of questions coming in. But here’s one, there’s some questions when you talked about soil freezing, Colin. One, is there any way to get around— Is there any way to mitigate the effects of of soil freezing on the water content? And then secondly, how does that affect— does the freeze thaw cycle— How does that affect the the instruments themselves, the sensors themselves?

COLIN CAMPBELL 44:02
Yeah, great question. So the first part of that, there’s not a lot you can do when you’re using a electromagnetic sensor to avoid a problem if the soil freezes, because it’s just measuring the vibrational rotation of the water molecule in the magnetic field when it polarizes that water and then it unpolarizes. That’s how it’s measuring the amount of water there. So if the water freezes, it can’t polarize those water molecules and it’s going to essentially disappear to the sensor. Now, that’s not the only way we can measure water content in the soil. So there are other options to us, they’re just not really easy to use. So my recommendation would be to— Well, there are two things. Number one, the same water that froze in the soil is going to be there when it thaws. So I drew that line to kind of show you the idea of how the water changes over time. And we can fairly well predict what water is there just by knowing the fact that during soil freezing, the water doesn’t go away—when it thaws, it’s still there. So we can extrapolate. And that may be the best idea to try to deal with that. The other point is just bury them lower. Soil doesn’t freeze— in most locations, certainly at this research project, we are seeing a little freezing down at the 10 centimeter, but not much. And that’s really not very deep in the soil. Most of my research projects have sensors buried at least at 15 centimeters. And so we typically don’t see that. The other point is no, the freezing doesn’t hurt the sensors at all. All the sensors, the water potential, and water content sensors that I was using, which are the TEROS 21 for water potential and the TEROS 10, 11 or 12 for the water content. None of those are hurt and it won’t pull the soil away from the sensors that I’ve experienced. Once we get thawing, everything seems to look good.

BRAD NEWBOLD 46:04
Okay. We had one question here. They’ve got an installation in an arid pine forest. They’re looking at water potential. And they’re getting some really low readings down to negative 10,000 to negative 80,000 kPa. And they’re just wondering about how can they interpret water potentials that low?

COLIN CAMPBELL 46:26
Yes, it’s a great question. So what to do with water potentials when they’re really, really dry? Well, it tends to depend on what you’re wanting to do. Of course, past negative 1500 kPa, there’s not a, you know, there’s very little available for plants. Xerophytes may be able to get at a little bit of that water, but really, it’s basically unavailable. So interpreting water below that point really is, you know, for plant life is not terribly important. But when we get water potential so low, there are two things I think about. Number one, just make sure that you’ve got good soil to sensor contact. We could get drying that low just because the water is pulled away from the matric potential sensor, and we’re not equilibrating with the soil very well. The other interpretation is for very dry situations like that, sometimes it’s better to use the PF scale. Now, I don’t have time to talk about that scale right now. But it’s basically like a pH for acids and bases. It’s a scale that’s logarithmic. So we don’t see these incredible drops like that, because it’s on a logarithmic scale. And we can kind of interpret that a little bit more easily. So those— essentially what you’re seeing is you’re close to air dry in the arid forest. And that’s important. But as we get that low, it doesn’t have a real plant basis.

BRAD NEWBOLD 47:55
Okay. We had a couple of questions about some installations, especially with cracking soil, as well as installing on surfaces. Do you have any insights, any recommendations for if you know that, for instance, if you know that your soil is going to be cracking during the season? Is there anything that you can do to get a better insulation?

COLIN CAMPBELL 48:22
So the cracking challenges is not an easy one. And we have some people that are quite familiar with this process here. So I’d love to be able to deal with this kind of on an individual basis. We’d love to get your questions into the support group here, as I talked about earlier, and then we can kind of deal with that, because you need to be very careful with your installation. The lower sensors, as I showed you, are a little less susceptible. It’s only because drying, you know, and that cracking is more of a surface problem. But it certainly happens at 50 centimeters. Talking to a good friend who did does a lot of this work and Texas, and she told me that it’s a problem down there, and it’s a problem for anytime you have to dig into the soil and install a sensor. So you know, it’s something that you need to prepare for. And it might be good just to deal with that problem specifically and individually. The other question about surface measurements, surface measurements are not terribly easy. And again, another problem where I’d encourage you to reach out to our support group because, you know, without taking some care in terms of calibration, measuring on surfaces is very difficult. Whether you’re just pressing the sensor up against a surface, or maybe you’re just near the surface, like my five centimeter sensor, there’s a lot of things that you give up when you do things that close to the surface, even having some of your electromagnetic field extend into the year. So again, I don’t want to sidestep that question, but it’s something that that I’d love to be able to get one of our scientists working directly with you to make sure you get the right answer there.

BRAD NEWBOLD 50:03
Okay. We’ll do a couple more here, we’re going to try to squeeze in as many as we can. One question was asking about soil salinity. And do you have a general gauge as to when soil salinity will begin to affect water content?

COLIN CAMPBELL 50:18
You know, soil salinity is something I’m always on the lookout for. So that’s why I really love to use the TEROS 12 water content sensor, because it also adds salinity and EC measurement in there to make sure to just get us an idea of what’s going on. The, you know, as we talk about it, when we get up above five decisiemens per meter in pore water, or in saturation extract, something that I start looking at to make sure we’re not having issues, we test all our sensors up to eight decisiemens per meter saturation extract to make sure they perform consistently between zero and eight. And if you are getting a TEROS sensor, it’s been tested and we see good performance in that region that you don’t have to recalibrate. If you know your electrical conductivity is pretty high, my strongest suggestion would be to do an individual soil specific calibration for that sensor. And then you can just be sure. That’s something I would do in any time where I was a little bit worried about soil EC. METER does offer that as a service, if you want us to do it, or we have some great literature that you can access to do it on your own. Again, an email to our support group, we’ll be able to get you kind of comfortable with how to get that done.

BRAD NEWBOLD 51:42
Okay. I think we’ll have time for one more question. I’m going to squish a couple questions in here as well. When it comes to trusting the data, so we’ve got the installation, how quickly can we trust that data coming in? Right after the install? And then also, how long as the sensors age, how long can we trust that data?

COLIN CAMPBELL 52:07
Yeah, great question. So initially, the water content sensors, if they’re installed well, you can trust those data virtually instantly after you put them in. Now you’ve got to make sure your install was done well, and it needs to go into undisturbed soil. And if you’ve opened up, you know, if you’ve used the METER borehole installation tool, which I love to use in my research, and you’ve backfilled, you know, there’s going to be a little time where just the native system, kind of springs back to where it was. So, you know, I start looking at the data, I certainly am trying to make choices with the data. But I realize that there’s a certain amount of time, just in the natural system, that needs to settle again. Now I’ve used sensors installed in the field now for 5 or 10 years. And the performance of the sensors, the water content sensor, simply hasn’t changed. And my expectation is, as long as the sensor is running, I’d expect it to perform like it came out of the factory, because we’re just simply using just electronics to do that. There’s nothing that would change over time, unlike something like a pyranometer that I use in my research and we’ll calibrate that or at least check its calibration every year or two. Water content sensors aren’t going to be under the same problem. And the thing that I would remember there is that I would much rather just go with the same water content sensor over time, where we’re at the same point and where we’re not disturbing the system for as long as I absolutely can.

COLIN CAMPBELL 53:46
And Brad, I did get one other question by email, I’ll give a shout out because, asked about, Hey, can we install these sensors deeper than say a meter? And although a lot of my data just showed down to close to a meter, or a little deeper than a meter, I have installed these sensors down to two meters in certain situation especially where we’re working in dry regions where we’re expecting native plants to reach down and start grabbing the water at two meters. I have colleagues in Italy who are going into levees, who are installing these down at five meters. They’ve just made themselves a specialized borehole installation tool that kind of used our design but extended it down to five meters. So and a lot of people who are doing slope stability studies, they will go down to these depths and so people have modified installation systems to do that. It happens. I know about several studies. I in particular have not done it, but there are people who have. So there’s a little— that was a question that came in earlier.

BRAD NEWBOLD 54:48
Okay, great. That’s gonna do it for us. We’re gonna have to wrap it up there. We’ve gone well over our time, but we appreciate everybody who stuck around. By our records, this has been our most popular webinar and I’m sure that we’ll have a lot of interest going forward. Thank you again for all your questions. We’ve got dozens of questions that we did not get to, some great ones about using soilless media, water content versus water potential, other sensor calibration, lots of things. Again, we have those questions recorded, and someone from our METER Environment team will be able to get back to you and answer your question directly. Please consider answering the short survey that will appear after this webinar is finished to tell us what other types of webinars you’d like to see in the future. And again, for more information on what you’ve seen and heard today, if you have interest in in the sensors or further knowledge in the subjects that we’ve covered. If you’d like to check out a demo of ZENTRA Cloud, please visit us at metergroup.com, and we’ll be able to help you out there. Finally, look again 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.