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Posts from the ‘Irrigation’ Category

Chalk Talk: Intensive vs. Extensive Variables

Learn the difference between intensive and extensive variables and how they relate to soil water potential vs. soil water content in our new Chalk Talk whiteboard series. In this video series, Dr. Colin S. Campbell teaches basic principles of environmental biophysics and how they relate to measuring different parameters of the soil-plant-atmosphere continuum.

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To learn more about measuring water potential vs. water content read: Why soil moisture sensors can’t tell you everything.

Video transcript

Hello, my name is Colin Campbell. I’m a senior research scientist here at METER group. And I teach a class on environmental biophysics. Today I wanted to talk about something we teach in the class: the difference between extensive and intensive variables. I’d like to do this with the goal of relating it to the difference between volumetric water content and water potential. 

Here, I have a picture of a ship moving through the ice and some metal that’s been heated in a furnace. I think we would agree the ship has the highest amount of heat in it compared to this very small piece of metal. And if we placed that piece of metal onto the outside of the ship, despite the fact that there is more heat in the metal, we know the heat would not move from the high amount of heat (ship) to the low amount of heat (metal). It would actually move from the highest temperature to the lowest temperature. Why is that?

The reason is that heat moves because of temperature and not because of heat content or the amount of heat in something. Heat content defines an amount or an extent. And we generally term something that defines an extent or an amount as an extensive variable.An extensive variable depends broadly on the size of something or how much of something there is. 

This differs for temperature. Temperature doesn’t depend on size. The temperature could be the same in a very small room or a very large room, but the amount of heat or heat content in those rooms would be quite different. When we describe temperature, we talk about intensity, which is why we call these types of variables intensive variables. This is because they don’t depend on size or amount. 

Let’s talk about another example. Here’s your heating bill. Maybe it’s natural gas. What you’re paying for is the amount of heat you put into the house. But the question is, are you comfortable in the house? And from this bill, we can’t tell. Maybe you put in 200 heat units, whatever those might be. We can’t tell if that’s comfortable because we don’t know the size of the house or the type of insulation. All those things would influence whether you were comfortable. 

Alternatively, if the temperature is 71 F that’s quite comfortable. That’s equivalent to about 22 degrees Celsius. So the intensive variable, temperature, is different than the extensive variable, heat content, that tells us how much heat we put in. And that’s important because at the end of the day, that leads to cost. 

On this side, we don’t know how much we paid to keep it at 22 C because heat content doesn’t tell us anything about that. But the intensive variable temperature does tell us something about comfort. So both of these variables are critical to really understanding something about our comfort in the house. 

Now let’s talk about the natural environment. Specifically, we’re going to talk about soils. We’ll start with the extensive variable. When we talk about water in soil, the extensive variable is, of course, water content. Water content defines the amount of water. Why would we care about water content? Well, for irrigation or a water balance.

The intensive variable is called water potential. What does water potential tell us? It tells us if soil water is available and also predicts water movement. If this soil had a water content of 25% VWC and another soil was at 20% VWC, would the water move from the higher water content to the lower water content? Well, that would be like our example of the ship and the heated piece of metal. We don’t know if it would move. It may move. And if the soil on either side was exactly the same, we might presume that it would move from the higher water content to the lower water content, but we actually don’t know. Because the water content is an extensive variable, it only tells us how much there is. It won’t tell us if it will move. 

Now, if we knew that this soil water potential was -20 kPa and this soil water potential over here was -15 kPa, we would know something about where the water would move, and it would do something different than we might think. It would move from the higher water potential to the lower water potential against the gradient in water content, which is pretty interesting but nonetheless true. Water always moves from the highest water potential to the lowest water potential.

This helps us understand these variables in terms of plant comfort. We talked about the temperature being related to human comfort. We know at what temperatures we are most comfortable. With plants, we know exactly the same thing, and we always turn to the intensive variable, water potential, to define plant comfort.

For example, if we have an absolute scale like water potential for a particular plant, let’s say -15 kPa is the upper level for plant comfort, and -100 kPa is the lower level of comfort, we could keep our water potential in this range. And the plant would be happy all the time. Just like if we kept our temperature between 21 and 23 Celsius, that would be comfortable for humans. But of course, we humans are different. Some people think that temperature is warm, and some think it’s cold. And it’s the same for plants. So this isn’t a hard and fast rule. But we can’t say the same thing with water content. There’s no scale where we can say at 15% water content up to 25% water content you’ll have a happy plant That’s not true.If we know something about the soil, we can infer it. But soil is unique. And we’d have to derive this relationship between the water content and the water potential to know that. 

So why would we ever think about using water content when we measure water in the soil? One reason is it’s the most familiar to people. And it’s the simplest to understand. It’s easy to understand an amount. But more importantly, when we talk about things like how much we’re going to irrigate, we might need to put on 10 millimeters of water to make the plants happy. And we’d need to measure that. Also if we want to know the fate of the water in the system, how much precipitation and irrigation we put on versus how much is moving down through the soil into the groundwater, that also relates to an amount.  

But when we want to understand more about plant happiness or how water moves, it’s going to be this intensive variable, water potential that makes the biggest difference. And so with that, I’ll close. I’d love for you to go check out our website www.metergroup.com to learn a little bit more about these measurements in our knowledge base. And you’re also welcome to email me about this at colin.campbell@meter group.com.

Soil moisture: ECH20 vs. TEROS, which is better?

See how the new TEROS soil moisture sensor line compares with METER’s trusted ECH20 sensor line.

TEROS 12 soil moisture sensor

Volumetric water content—defined

To evaluate the performance of any water content sensor, you need to first understand its technology. In order to do this, it’s necessary to understand how volumetric water content (VWC) is measured. Volumetric water content is the volume of water divided by the volume of soil (Equation 1) which gives the percentage of water in a soil sample.

So, for instance, if a volume of soil (Figure 1) was made up the following constituents: 50% soil minerals, 35% water, and 15% air, that soil would have a 35% volumetric water content.

The percentage of water by mass (wm) can be measured directly using the gravimetric method, which involves subtracting the oven-dry soil mass (md) from the mass of moist soil (giving the mass of water, mw) and dividing by md (Equation 2).

The resulting gravimetric water content can be converted to volumetric by multiplying by the dry bulk density of the soil (b) (Equation 3).

Why capacitance technology works

Volumetric water content can also be measured indirectly: meaning a parameter related to VWC is measured, and a calibration is used to convert that amount to VWC. All METER soil moisture sensors use an indirect method called capacitance technology. In simple terms, capacitance technology uses two metal electrodes (probes or needles) to measure the charge-storing capacity (or apparent dielectric permittivity) of whatever is between them.

Table 1 illustrates that every common soil constituent has a different charge-storing capacity. In a soil, the volume of most of these constituents will stay constant over time, but the volume of air and water will fluctuate.

Since air stores almost no charge and water stores a large charge, it is possible to measure the change in the charge-storing ability of a soil and relate it to the amount of water (or VWC) in that soil. (For a more detailed explanation of capacitance technology watch our Soil Moisture: methods/applications webinar.

Capacitance today is highly accurate

When capacitance technology was first used to measure soil moisture in the 1970s, scientists soon realized that how quickly the electromagnetic field was charged and discharged was critical to success. Low frequencies led to large soil salinity effects on the readings. Over time, this new understanding, combined with advances in the speed of electronics, enabled the original capacitance approach to be adjusted for success. Modern capacitance sensors, such as METER sensors, use high frequencies (70 MHz) to minimize effects of soil salinity on readings.

The circuitry in capacitance sensors can be designed to resolve extremely small changes in volumetric water content, so much so, that NASA used METER’s capacitance technology to measure water content on Mars. Capacitance soil moisture sensors are easy to install and tend to have low power requirements. They may last for years in the field powered by a small battery pack in a data logger.   

TEROS and ECH20: same trusted technology

Both TEROS and ECH20 soil moisture sensors use the same trusted, high-frequency (70 MHz) capacitance technology that is published in thousands of peer-reviewed papers. Figure 3 shows the calibration data for the ECH20 5TE and TEROS 12.

Read the full article….

Soil Moisture 301—Hydraulic Conductivity Why you need it. How to measure it.

New Live Webinar

Hydraulic conductivity, or the ability of a soil to transmit water, is critical to understanding the complete water balance.

Soil hydraulic conductivity impacts almost every soil application.

In fact, if you’re trying to model the fate of water in your system and simply estimating parameters like conductivity, you could get orders of magnitude errors in your projections. It would be like searching in the dark for a moving target. If you want to understand how water will move across and within your soil system, you need to understand hydraulic conductivity because it governs water flow.

Get the complete soil picture

Hydraulic conductivity impacts almost every soil application: crop production, irrigation, drainage, hydrology in both urban and native lands, landfill performance, stormwater system design, aquifer recharge, runoff during flooding, soil erosion, climate models, and even soil health. In this 20-minute webinar, METER research scientist, Leo Rivera discusses how to better understand water movement through soil. Discover:

  • Saturated and unsaturated hydraulic conductivity—What are they?
  • Why you need to measure hydraulic conductivity
  • Measurement methods for the lab and the field
  • What hydraulic conductivity can tell you about the fate of water in your system

Date: August 20, 2019 at 9:00 am – 10:00 am Pacific Time

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Can’t wait for the webinar? See a comparison of common measurement methods, and decide which soil hydraulic conductivity method is right for your application. Read the article.

Soil Moisture 101: Need-to-Know Basics

Harness the power of soil moisture

Researchers measure evapotranspiration and precipitation to understand the fate of water—how much moisture is deposited, used, and leaving the system. But if you only measure withdrawals and deposits, you’re missing out on water that is (or is not) available in the soil moisture savings account. Soil moisture is a powerful tool you can use to predict how much water is available to plants, if water will move, and where it’s going to go.

Soil moisture 101 explores soil water content vs. soil water potential

What you need to know

Soil moisture is more than just knowing the amount of water in soil. Learn basic principles you need to know before deciding how to measure it. In this 20-minute webinar, discover:

  • Why soil moisture is more than just an amount
  • Water content: what it is, how it’s measured, and why you need it
  • Water potential: what it is, how it’s different from water content, and why you need it
  • Whether you should use soil moisture sensors, water potential sensors, or both
  • Which sensors measure each type of parameter

Watch the webinar

Soil moisture 201 coming soon

Sign up to attend the live webinar:  Soil Moisture 201: Moisture Release Curves—Revealed June 11, at 9am PST.

Why mesonets make weather prediction more accurate

The staggering cost of Montana’s “flash drought”

Some people figured it was climate change. One statistician said it was a part of a cyclical trend for poor crop years. Whatever the cause, the 2017 flash drought that parched the entire state of Montana and most of South Dakota, severely impacted the profitability of ranchers and farmers. In western Montana, fires burned some of the largest acreages in recent history. It resulted in one of the biggest wildfire incident reports (over one-million acres) and caused virtually 100% crop loss in northeastern Montana. The U.S. Dept. of Agriculture estimated the crop loss to be in the hundreds of millions of dollars, and one question was on everybody’s mind—why did no one see it coming?

Figure 1. Montana drought conditions August 2017 (Source: Montana State Library website: https://mslservices.mt.gov/Geographic_Information/Maps/drought/)

Getting the right weather data

The 2017 Montana Dept. of Natural Resources and Conservation spring drought report indicated plenty of water: “By the end of the month, almost all drought concern was removed from the state, with the exception of Wibaux and Fallon Counties….As of May 9, 2017, Montana was 98.45% drought free.” But in late May, an abrupt shift in weather conditions led to one of the hottest, driest summers on record.

The problem, says Kevin Hyde, Montana State Mesonet Coordinator, lies not only in the need for more weather data but in obtaining the right kind of data. He says, “One of the reasons drought was missed was because we’re still thinking you measure drought by snowpack and how much water is in the river, which is really great if you’ve got water rights. But we’ve got a lot of dryland out there.”

In addition to weather monitoring, Hyde is a big proponent of adding soil moisture and NDVI measurements to each of the Montana Mesonet stations he oversees. He says, “The conventional weather station only measures atmospheric conditions. But ultimately, to make any decisions, we’ve got to know not just how much water comes into the system, but how much goes into the soil. And even that’s not enough…because what we really need to know is how the water situation is going to affect plants.”

Hyde says more data are needed to warn growers and ranchers about upcoming weather risks. He points to the fact that increasing evapotranspiration got missed leading up to the summer of 2017. “We realized that if we were looking carefully at reference ET, we might have seen it about a month earlier. What would people have done? They would have changed their calf purchases. They would have figured out what kind of forage they needed to buy. These are the types of decisions people can make if they know the information sooner.”

Was the drought over? Soil moisture illuminates the bigger picture

Heavy rains came mid-September of 2017, which led some people to believe the drought was over. However, changes in soil moisture told a different story. Very little of the rain made it into the soil. “At the Havre, MT station you can see we had some heavy precipitation events. Then we had early October snows. So people expected good soil water recharge. But at the end of the day, we didn’t get it. On Sept.15th, soil moisture sensors showed a big soil moisture response at the surface but only a marginal response at 8 inches.” The melt of early October snows onto the soil, still damp from the September rain, drained to 20 inches or more. But as the snowmelt dissipated, there was minimal net gain going into the winter.

Figure 2. Soil moisture traces at the Havre, MT weather station

Predictive models need more coverage to be effective

Typically in the U.S., the National Weather Service (a division of NOAA) puts out a network of weather monitoring stations spaced out across the country, and that data gets fed into forward-looking models that help predict the weather. Dr. Doug Cobos, research scientist at METER says, “What people are finding out is that putting in a sparse network of very expensive systems has done really well. It’s been a good thing. But the spatial gaps in those networks are a problem, especially for agriculture producers and ranchers. They need to know what’s happening where they are.”

Hyde agrees, adding that we need better predictive tools that help growers and ranchers make practical decisions based on data rather than guessing. “January 1st is when the decision has to be made—do I buy cows? Do I sell cows? Do I need more pasture? But many predictions start on April 1st. As one rancher puts it, ‘We don’t bother with Las Vegas. We sit around the dining room table at the beginning of the year and put a million dollars on one shot.’”

Mesonets improve spatial distribution

Mesonets present a practical solution for the need to fill in data gaps between large, complex weather stations. The Montana Mesonet currently has 57 stations interspersed throughout the state, and through partnerships with both the public and private sector, they’re adding more stations every year.

Figure 3. Map of MT Mesonet weather stations (source: http://climate.umt.edu/mesonet/)

At each location, the Montana Mesonet team installs METER all-in-one weather stations, soil moisture sensors, NDVI sensors and data loggers that integrate with ZENTRA Cloud: an easy-to-use web software that seamlessly integrates into third-party applications through an API. He says the system enables better spatial distribution and reliability. “When we were deciding on equipment we asked ourselves: What kind of technology should we use? It had to provide high data integrity. It had to be easy to deploy and maintain. And it had to be cost effective. There’s not a lot of people in that sector. METER systems are low profile, they’re affordable, and the reliability is there. I look at some other mesonets, and they cannot afford to build out further because they are relying on large, complex, expensive systems. That’s where the METER system comes into play.”

Figure 4. Montana Mesonet station setup (Photo credit: Kevin Hyde)

Betting on the future

The Mesonet team and its partners are excited to see how their data will mesh with the available predictive tools to be the most useful and practical for growers and ranchers throughout the state, and they realize that there is still much work to do. “It’s not enough just to get the instrumentation out there. The overall crux is: how do we build the information network, and how do we build a relationship with the producers so that we can have an iterative and interactive conversation?” says Hyde. “We know there needs to be an education in how to use and interpret the data. For example: what is NDVI, and what can we learn from it? A lot of what we need to do is translate science into practical terms.” But he adds that it doesn’t need to be perfect. “What the farmers have said to us is, ‘We don’t need exact numbers. We’re gamblers. Give us probability. Teach us what it means, and we’ll make the decision.’”

Find more information on the Montana Mesonet here.

See performance data for the ATMOS 41 weather station.

Just released: ATMOS 41 comparison testing data

Climate parameters such as precipitation, air temperature, and wind speed can change considerably across short distances in the natural environment. However, most weather observations either sacrifice spatial resolution for scientific accuracy or research-grade accuracy for spatial resolution.

weather station

ATMOS 41 all-in-one weather station

The ATMOS 41 represents an optimization of both. It was carefully engineered to maximize accuracy at a price point that allows for spatially distributed observations. Additionally, because many researchers need to avoid frequent maintenance and long setup times, the ATMOS 41 was designed to reduce complexity and withstand long-term deployment in harsh environments. To eliminate breakage, it contains no moving parts, and it only requires recalibration every two years. Since all 14 measurements are combined in a single unit, it can be deployed quickly and with almost no effort. Its only requirement is to be mounted and leveled on top of a pole with an unobstructed view of the sky.

Comparison testing and sensor-to-sensor variability data

METER released the ATMOS 41 in January 2017 after extensive development and testing with partnerships across the world, in Africa, Europe, and the US. We performed comparison testing with high-quality, research-grade non-METER sensors and conducted time-series testing for sensor-to-sensor variability.

See performance data for the ATMOS 41 weather station

Data collection: 8 best practices to avoid costly surprises

Every researcher’s goal is to obtain usable field data for the entire duration of a study. A good data set is one a scientist can use to draw conclusions or learn something about the behavior of environmental factors in a particular application. However, as many researchers have painfully discovered, getting good data is not as simple as installing sensors, leaving them in the field, and returning to find an accurate record. Those who don’t plan ahead, check the data often, and troubleshoot regularly often come back to find unpleasant surprises such as unplugged data logger cables, soil moisture sensor cables damaged by rodents, or worse: that they don’t have enough data to interpret their results. Fortunately, most data collection mishaps are avoidable with quality equipment, some careful forethought, and a small amount of preparation.

Before selecting a site, scientists should clearly define their goals for gathering data.

Make no mistake, it will cost you

Below are some common mistakes people make when designing a study that cost them time and money and may prevent their data from being usable.

  • Site characterization: Not enough is known about the site, its variability, or other influential environmental factors that guide data interpretation
  • Sensor location: Sensors are installed in a location that doesn’t address the goals of the study (i.e., in soils, both the geographic location of the sensors and the location in the soil profile must be applicable to the research question)
  • Sensor installation: Sensors are not installed correctly, causing inaccurate readings
  • Data collection: Sensors and logger are not protected, and data are not checked regularly to maintain a continuous and accurate data record
  • Data dissemination: Data cannot be understood or replicated by other scientists

When designing a study, use the following best practices to simplify data collection and avoid oversights that keep data from being usable and ultimately, publishable.

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5 ways site disturbance impacts your data—and what to do about it

Lies we tell ourselves about site disturbance

When it comes to measuring soil moisture, site disturbance is inevitable. We may placate ourselves with the idea that soil sensors will tell us something about soil water even if a large amount of soil at the site has been disturbed. Or we might think it doesn’t matter if soil properties are changed around the sensor because the needles are inserted into undisturbed soil.

site disturbance

The key to reducing the impact of site disturbance on soil moisture data is to control the scale of the disturbance.

The fact is that site disturbance does matter, and there are ways to reduce its impact on soil moisture data. Below is an exploration of site disturbance and how researchers can adjust their installation techniques to fight uncertainty in their data.

Non-disturbance methods don’t measure up—yet

During a soil moisture sensor installation, it’s important to generate the least amount of soil disturbance possible in order to obtain a representative measurement. Non-disturbance methods do exist, such as satellite, ground-penetrating radar, and COSMOS. However, these methods face challenges that make them impractical as a single approach to water content. Satellite has a large footprint, but generally measures the top 5-10 cm of the soil, and the resolution and measurement frequency is low. Ground-penetrating radar has great resolution, but it’s expensive, and data interpretation is difficult when a lower boundary depth is unknown. COSMOS is a ground-based, non-invasive neutron method which measures continuously and reaches deeper than a satellite over an area up to 800 meters in diameter. But it is cost prohibitive in many applications and sensitive to both vegetation and soil, so researchers have to separate the two signals. These methods aren’t yet ready to displace soil moisture sensors, but they work well when used in tandem with the ground truth data that soil moisture sensors can provide.

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Hydrology 301: What a Hydraulic Conductivity Curve Tells You & More

Hydraulic conductivity is the ability of a porous medium (soil for instance) to transmit water in saturated or nearly saturated conditions. It’s dependent on several factors: size distribution, roughness, tortuosity, shape, and degree of interconnection of water-conducting pores. A hydraulic conductivity curve tells you, at a given water potential, the ability of the soil to conduct water.

One factor that affects hydraulic conductivity is how strong the structure is in the soil you’re measuring.

For example, as the soil dries, what is the ability of water to go from the top of a sample [or soil layer in the field] to the bottom. These curves are used in modeling to illustrate or predict what will happen to water moving in a soil system during fluctuating moisture conditions. Researchers can combine hydraulic conductivity data from two laboratory instruments, the KSAT and the HYPROP, to produce a full hydraulic conductivity curve (Figure 1).

Hydraulic conductivity curve

Figure 1. Example of hydraulic conductivity curves for three different soil types. The curves go from field saturation on the right to unsaturated hydraulic conductivity on the left.  They illustrate the difference between a well-structured clayey soil to a poorly structured clayey soil and the importance of structure to hydraulic conductivity especially at, or near, saturation.

In Hydrology 301, Leo Rivera, Research Scientist at METER, discusses hydraulic conductivity and the advantages and disadvantages of methods used to measure it.

Watch the webinar below.

 

 

Get more info on applied environmental research in our

Lab versus in situ soil water characteristic curves—a comparison

The HYPROP and WP4C enable fast, accurate soil moisture release curves (soil water characteristic curves-SWCCs), but lab measurements have some limitations: sample throughput limits the number of curves that can be produced, and curves generated in a laboratory do not represent their in situ behavior. Lab-produced soil water retention curves can be paired with information from in situ moisture release curves for deeper insight into real-world variability.

soil water characteristic curves

Soil water characteristic curves help determine soil type, soil hydraulic properties, and mechanical performance and stability

Moisture release curves in the field? Yes, it’s possible.

Colocating water potential sensors and soil moisture sensors in situ add many more moisture release curves to a researcher’s knowledge base. And, since it is primarily the in-place performance of unsaturated soils that is the chief concern to geotechnical engineers and irrigation scientists, adding in situ measurements to lab-produced curves would be ideal.

In this brief 20-minute webinar, Dr. Colin Campbell, METER research scientist, summarizes a recent paper given at the Pan American Conference of Unsaturated Soils. The paper, “Comparing in situ soil water characteristic curves to those generated in the lab” by Campbell et al. (2018), illustrates how well in situ generated SWCCs using the TEROS 21 calibrated matric potential sensor and METER’s GS3 water content sensor compare to those created in the lab.

Watch the webinar below:

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