Posts tagged ‘Sensors’

Oct 23

Measuring Water Potential in Concrete

Trevor Dragon, a former METER Research and Development Engineer, was pouring concrete at his Beeville, Texas, farm one day and wondered if he could measure moisture in concrete with a matric potential sensor instead of the more traditionally used volumetric water content sensor (VWC) to get more accurate readings. Dragon says, “We had about five concrete trucks come in that day, and we poured five different slabs. Every truck had a different amount of water added. One particular batch of concrete was really wet and soupy, and I became curious to measure it and compare it to the other slabs.”

Concrete slab drying down at Trevor’s Texas farm.

Why Measure Moisture in Concrete?

As concrete hardens, portland cement reacts with water to form new bonds between the components of the concrete. This chemical process, known as hydration, gives concrete its characteristic rock-like structure. Too much or too little water can reduce the strength of the concrete. Adding excess water can lead to excessive voids in concrete while providing too little water can inhibit the cement hydration reaction. Thus, when you pour a slab in south Texas, where it’s exposed to high wind and intense heat, sufficient water must be added, and precautions must be taken to minimize evaporation of water from the slab surface as the concrete hardens.

Better Readings:

Dragon chose the matric potential sensor because he wondered if it would be more accurate than a VWC measurement. He says, “I knew that VWC sensors were calibrated for soil, and because of that they would lack accuracy. But the water potential sensor is calibrated for the ceramic it contains. I figured it would be closer to the real thing without having to do a custom calibration.”

Moisture in concrete has been difficult to measure because the high electrical conductivity early in the hydration process throws off water content sensor calibration. So, Dragon was surprised when his data turned out to be really good. He comments, “The dry down curve of the matric potential sensor was a perfect curve. There was a nice knee (drop from saturation) after about 200 minutes, and it just went down from there. We’re kind of stumped because we are trying to understand why the data came out so well and why the curve looks so good.”

Water Potential in Concrete

The scientists at METER sent the drydown curve to Dr. Spencer Guthrie, a civil engineering professor, to see what he thought. He explains, “I suspect that the concrete is experiencing initial set at around 200 minutes. This is a very normal time frame by which finishing operations need to be complete. At this stage in cement hydration, the concrete becomes no longer moldable. A rigid capillary structure is forming, and individual pores are taking shape. As hydration continues, the pores become smaller and smaller, which may explain the decrease in matric potential.”

New Methods:

One theory Dragon and his colleague Dr. Colin Campbell came up with was that perhaps Dragon’s unique method of inserting the sensors made a difference in the measurements. He explains, “The first thing I did was look for the rebar in the concrete, and I placed the sensors in the exact center of one of the squares to avoid the influence of metal on the sensor electromagnetic field. Also, I didn’t insert the sensors the same way you would insert them into soil. In soil, you put the sensors in vertically; I placed the water potential sensor horizontally because in this case, I was not interested in how water was moving in the slab but how it was being used over time.

What Does It Mean for the Future?

The behavior of the water potential sensor embedded in the concrete clearly indicated a drying process where water becomes less available over time. However, the implications are still unknown. Can the quality of the concrete be determined from the speed or extent of water becoming less available? Hopefully, this opportunistic experiment by Dragon will lead to more tests to show whether this approach is useful to others.

Dr. Guthrie agrees the idea should be explored further and comments, “The matric potential measurements were not redundant with the water content measurements. Instead, they offered additional, interesting information about the early hydration characteristics of the concrete. In the context of construction operations, the water potential data indicated what is normally determined by observing the impression left in the concrete surface from the touch of a finger. In the context of research, however, the use of a water potential sensor may yield helpful information about how certain admixtures, for example, influence the development of hydration products in concrete over time.”

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Oct 16

Measuring Frozen Water Potential: How and Why?

In China recently, a fellow scientist asked Dr. Colin Campbell if matric potential sensors work in frozen soils. His answer? Sort of. In this blog, he explains what he meant by his enigmatic reply: When water freezes in the soil, most matric potential sensors won’t work accurately because frozen water essentially disappears to the measurement. For example, in a dielectric measurement circuit, most of the water that was polarized in the electromagnetic field solidifies in the ice matrix. Thus, because dielectric measurements determine the charge that is stored when water is polarized, ice is not measured. But, many matric potential sensors contain a component that will measure frozen water potential: the temperature sensor.

Horseshoe prints in frozen soil.

How Does Temperature Measure Water Potential?

The temperature of a frozen matrix like soil has a fundamental thermodynamic relationship to the energy state of that water. For every one degree C below freezing, the water potential decreases by 1.2 MPa. For example, if the soil drops down to -4 C, the soil water potential will be -4.8 MPa. However, one thing many people don’t understand is that there is still liquid water in frozen soils.

Where is the Liquid Water in a Frozen Soil?

Some liquid water will always be associated with soil surfaces because water, as a polar molecule, is attracted by opposite surface charges. Ice is a collection of water molecules that have slowed enough that they are arranged in a crystal-like structure. When ice arranges in that structure, it will attract and use all those water molecules that are available but will have difficulty stealing away water bound to soil surfaces. That water will remain liquid. As soil temperature drops, water layers closer and closer to soil particle surfaces will slow and join the ice structure.

Why Worry about Frozen Water Potential?

Previously, we’ve discussed the importance of water potential in determining the availability of water for plant growth. But below freezing, plants are either dormant or expired, so why measure frozen water potential?

There are a couple of reasons frozen soil water potential may be interesting to scientists. Liquid water in frozen soil still has the possibility to move. So, knowing soil temperature will allow models to predict water flow.

Even more interesting is what could be done with a temperature sensor and a measurement of water content using dielectric permittivity. As we mentioned earlier, ice essentially disappears to a dielectric measurement. Thus, a dielectric sensor water content measurement should provide the amount of liquid water in the soil. Using the temperature sensor to infer water potential (assuming the soil begins wet enough that its pre-frozen state has not reduced WP significantly), we can combine the WP and VWC measurements over a range of temperatures to generate an in situ moisture release curve. This idea was developed into a prototype instrument that appeared to have promise as a new laboratory technique to obtain moisture release curves.

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Oct 9

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Water Content helps Turf Growers find Water/Nutrient Balance

Many athletes don’t like artificial turf. They say it’s hot, uncomfortable to run on, causes burns when you slide or fall on it, and changes the way a ball moves. Professional women’s soccer players even started a lawsuit over FIFA’s decision to use artificial turf in the 2015 Women’s World Cup.

Soccer players running after the soccer ball on a green field

Soccer players on natural turf.

Some universities—including Brigham Young University—have responded to athlete concerns by using natural turf fields for practice and in their stadiums. But the challenge is to develop plants and management practices for natural turf that help it stand up to frequent use and allow it to perform well even during the difficult fall months. It’s a perfect research opportunity.

BYU turf professor and manager of BYU sports turf, Bryan Hopkins and his colleagues in the Plant and Wildlife Department, have been able to set up a new state-of-the-art facility to study plants and soil in both greenhouse and natural conditions. The facility includes a large section of residential and stadium turfgrass.

Before Soil Sensors

Initially, BYU maintained the turf farm grass on a standard, timer-based irrigation control system, but over time they realized that understanding the performance of their turf relative to moisture content and nutrient load is crucial. Last year during Memorial Day weekend their turf farm irrigation system stopped working when no one was around to notice. During those four days temperatures rose to 40 C (100 F), and the grass in the field slipped into dormancy due to heat stress. In response, Dr. Hopkins began imagining a system of soil moisture sensors to constantly monitor the performance of the turf grass. He wanted not only to make sure the turf never died but also to really understand the elements of stress so they could do a better job growing healthy turf.

Sensors Give a Clear Picture

Soon afterward, a team of scientists, including fellow professor Dr. Neil Hansen, installed volumetric water content (VWC) and matric potential sensors at two different sites: one in the sports turf and one in a residential turf plot. Each plot had two installations of sensors at 6 cm and 15 cm, along with VWC only at 25 cm, to measure water moving beyond the root zone. Combining these measurements, they could clearly see when the grass was reaching stress conditions and how quickly the turf went from the beginning of stress (in terms of water content and time) to permanent wilting point. In addition, ancillary measurements of temperature and electrical conductivity provide an opportunity for modeling surface and root zone temperature as well as fertilizer concentration dynamics.

Researcher digging a dirt canal and installing sensors

Installing water content sensors at the BYU turf farm.

Errors Revealed

What the researchers learned was that they were using too much water. Dr. Colin Campbell, a METER research scientist who worked with BYU on sensor installation, comments, “We found in the first year that the plants never got stressed at all. So this year, the researchers allowed the water potential (WP) at 6 cm to drop into the stress range (~ -500 kPa) while observing WP at 15 cm (-50 kPa to -60 kPa). We hope this approach will reduce irrigation inputs while creating some stress in the grass in order to push the roots deeper.”

What’s happening with the water?

Dr. Campbell’s favorite part of the sensor data was the detailed picture it gave of what was happening with the water in the sandy soil (Figure 1). He says, “Most people believe that they have an intuitive feel for water availability in soil. If we were only using water content sensors, seeing a typical value of 20% would lead us to believe we were comfortably in the middle of the plant available range (A). But in this study, using our colocated soil water content and soil water potential sensors, the data showed readings over 15% VWC were too wet to affect the WP (B). However, once WP visibly changed, it quickly moved toward critical stress levels (C, -1500 kPa is permanent wilting point); it only took two days for the water potential to change from -8 kPa to -1000 kPa. A subsequent dry period (D) shows similar behavior, but this time the 15 cm WP drops to near -1000 kPa.”

Figure 1

The plant stress levels were reached surprisingly quickly in this soil because its sand composition has a lot of large pores and not very many small ones (Figure 2). Campbell explains, “The large pores store water that is not held tightly due to low surface area, so the water is freely available. But at around 10% VWC all the water from the large pores is used up. As the soil dries beyond that, the water is held tightly in small pores and becomes increasingly unavailable. This is clear in the moisture release curve. We see almost no change in water potential as the soil dried to 16% VWC, but from 10% down to 7%, the water potential reached permanent wilting point, and it happened in just over a day.”

Figure 2

What the Future Holds:

The researchers wanted to make sure that if they went down to certain stress levels, they wouldn’t cause harm to the plants, so this year, they installed a weather station to monitor evapotranspiration and calculate irrigation application rates. They also began measuring spectral reflectance to monitor changes in leaf area (NDVI) and photosynthesis (PRI). This will enable them to see the impact on the plants as the turf is drying down. “In the future,” says Campbell, “we hope that both commercial and residential turf growers will be able to more effectively control their irrigation and nutrients based on what we find in this study.”

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Oct 2

Soil Moisture and Temperature Sensors Aid Landmine Detection

Anti-personnel landmines are one of the most dangerous environmental hazards worldwide. Each year thousands of people are injured by landmines buried in eighty different countries. Ben Wallen, Ph.D. candidate and active military officer at the Colorado School of Mines, is using soil moisture and temperature sensors to model, simulate, and predict how environmental conditions affect landmine detection performance.

Researcher and an army graduate student standing next to "The U.S Army Corps of Engineers, Engineer Research and Development Center" sign

Landmine research conducted at the Engineer Research and Development Center (ERDC) by LTC Benjamin Wallen, a graduate student at Colorado School of Mines, and Stacy Howington, a senior research engineer at ERDC.

Landmine Detection

Anti-personnel landmines are difficult to detect. They are small and often contain very little metal. It is difficult to differentiate between a landmine and, for example, a rock.

Success depends on many factors, including the landmine’s physical composition and how long it’s been in the ground. The numerical and analytical models used to find the mines rely on detailed data about conditions in the subsurface. Wallen and his Ph.D. advisor, Dr. Kate Smits, realized that changing environmental conditions—particularly changes in soil moisture content—were commonly overlooked in developing these models. By gaining a greater understanding of these dynamic environmental conditions, Wallen thought he could better calibrate the numerical models used in detection technologies such as ground penetrating radar.

Researcher and army engineer student working on an installation site

Installing METER sensors at landmine detection field site at the Engineer Research and Development Center (ERDC) by LTC Benjamin Wallen and Matthew Geheran, a student engineer at ERDC.

Comparisons

The goal of Wallen’s research was to improve understanding of the complex flow processes of water, water vapor, and air in the shallow subsurface. He installed soil moisture and temperature sensors in a field site in order to understand how landmines buried at different depths affect spatial patterns of soil moisture. He compared holes with mines at a shallow depth (2.5 cm) to more deeply buried mines (10 cm). He also measured the environmental response to shallow empty holes roughly the size that you’d dig for the placement of a mine. He realized if there was an identifiable response between a disturbed hole with nothing in it and a hole with a mine buried, researchers would be able to do experiments with different soils in a lab without needing a buried landmine in order to investigate the environmental response associated with a buried landmine.

Results

Wallen was able to see differences in the “with mine” and “without mine” treatments. He says, “The soil moisture in the disturbed soil 2.5 cm below the surface with no landmine inserted matched very well to a shallow-buried mine. The only time it really deviated was when there was a saturation event. At that point, there was a break from that relationship, but then, in 36 hours, the soil moisture returned to matching very closely between the disturbed soil hole and the shallow-buried mine.” Wallen says there was also a relationship in the case of the more deeply buried mine. He adds, “For a deeply buried mine, both the soil moisture and temperature in the disturbed soil 2.5 cm below the surface had a strong correlation with the response to the dug, disturbed hole.”

An Array of Sensors is Crucial

Ben says it was important to his study to use a suite of measurement tools that complimented each other. In addition to soil moisture and temperature sensors, he used an IR camera to detect surface temperature differences prior to the saturation event, during saturation event, and then afterward, helping identify the different scenarios of shallow-buried mines, deep buried mines, and the disturbed soil. He comments, “There are numerous global climate models that may be used to predict evaporation from energy balances in order to understand what is occurring. By combining the sensors in this minefield detection scenario, we were able to really understand what was going on at different depths with soil moisture and temperature, and that enabled us to better understand how the system responds.”

The Next Step

Now that Wallen has done a soil characterization of the site, he wants to incorporate the data into a 3D model to ensure that the model accurately represents the actual physical conditions he’s observed. The next step is modeling under different climatic conditions: seeing what the environmental response is for various mine scenarios in a different soil environment.

Making the World a Safer Place

The goal, according to Wallen, is to provide pertinent information that will improve landmine detection technologies. Understanding how temperature contrast impacts remote sensing technology and understanding how the soil moisture signature impacts ground penetrating radar. Ben says, “Ideally, this information takes us one step farther in being able to identify potential locations for landmines, but there is a long way to go. This is just one piece of the pie, but every step forward moves us toward the goal of making the world a little bit safer for everyone.”

Acknowledgments:

This research was made possible through sensors provided by Decagon (now METER), funding from the Society of American Military Engineers Denver Metro Post, field site access from the Waterways Experimentation Station (WES), and assistance with equipment and research support from scientists and engineers at the Engineer Research and Development Center (ERDC) in Vicksburg, MS. Their support and knowledge based upon over a decade of research exploring disturbed soil for threat detection and environmental effects on sensor performance was essential to enable quality research at their site.

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Sep 25

Can a Leaf Wetness Sensor Distinguish Fog From Dew?

The Namib Desert on the Southwestern coast of Africa is hyper-arid in terms of rainfall but experiences frequent coastal fog events. The fog has been suggested to provide sufficient water for survival to certain plants which are endemic to the Namib, some of which occur only in the fog zone (up to 60 km inland).

Dr. Keir Soderberg wanted to measure how much fog water plants were taking up either through surficial roots or their leaves.

Dr. Keir Soderberg, former researcher at the University of Virginia (now a consultant at S.S. Papadopulos & Associates), wanted to use stable isotopes to measure how much fog water plants were taking up either through surficial roots or their leaves. To enrich his data set, he decided to use leaf wetness sensors to show when the fog was occurring. He also wondered if he could use the leaf wetness sensors to distinguish between fog and dew.

The Namib Desert

Keir set up five fog monitoring stations along a climate gradient in the central Namib. Each measured leaf wetness, air temperature, and relative humidity measurements along with solar radiation and soil parameters (moisture, temperature, and electrical conductivity). Stable isotope analysis of samples was also used to help quantify the amounts of fog, groundwater, and soil water that plants were using.

Dew or Fog:

Keir says, “We began collecting one-minute data to look at the different patterns of how the water was being deposited on the leaf wetness sensor. The dew tended to be more of a gradual wetting, but with the fog you would see these cyclical waves of steep wetting and then a little bit of a drying on the sensor.” Keir says he could look at those patterns and correlate them with visual evidence from his visits to the Namib during fog or dew events, though those wetting patterns may be specific to this location.

Measuring Volume:

Keir also tried to determine the volume of water deposited on the leaf wetness sensors. He did a calibration in the lab by spraying water on the sensor and then weighing it. He said, “It was sort of a trial and error thing. I found the performance was definitely sensor specific. You have to get an individual calibration, but I felt the uncertainty could be controlled.”

In comparing different methods of measuring fog deposition, Keir concluded that it is difficult to compare across measurement methods. “There’s a lot of variability between methods, even if you are confident in your own device and its accuracy.” This gives the advantage to the most common measurement device, the Standard Fog Collector, since much of the work done through the years has used these instruments. However, the cylindrical-style collectors have the advantage of being insensitive to wind direction.

Future Data:

In spite of this, Keir admits he’s still interested in seeing if he can get good dew collection data from leaf wetness sensors. He says, “I went on from Namibia to a research station in Kenya where we had an eddy covariance flux tower. Though there is no fog in Kenya, I convinced them to put leaf wetness sensors up and down the tower to collect data on dew deposition. We left the sensors out there and have been collecting one-minute data for a while. There’s this massive dataset out there that we still need to look at.”

Keir collaborated on a paper for The Journal of Arid Environments, called “The Nature of Moisture at Gobabeb, in the Central Namib Desert,” a compilation of different fog and dew collection techniques over the years, including leaf wetness sensors, for automating the identification of fog events. You can find it here. New fog monitoring stations are going up in the Namib through the programs FogLife and FogNet.

For a basic understanding of the role that fog plays in plant and ecosystem processes, read this article by Dr. Chris Still, who has studied this issue for many years in the Channel Islands National Park off of the coast of California.

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Sep 19

Assessing Erosion Risk after Forest Fires

As forest fires throughout the Northwest die down, one scientist’s work is just beginning. An article from our archives details the important research that takes place in the aftermath of the flames:

Forest on fire with sun shining through the smoke

In 2015, over eight million acres of forest burned in the United States. Major fires burned in five northwestern states: Washington, Idaho, Montana, Oregon, and California.

Flagstaff, Arizona is typically a dry place. But in August 2010, churning rivers flowed down roadways and around—and through—homes in the Flagstaff area. The floods were caused by a fire—the 15,000 acre Shultz fire that raged around Flagstaff from April to July, 2010.

One might not ordinarily think of a fire causing a flood, but to Forest Service research engineer Dr. Peter Robichaud, the setup is classic. “After a fire, you’ve changed the hydrology of the hillside,” he says. “Normally in an unburned area, rain gets soaked up by forest floor material on the ground and then it soaks into the soil. After a fire goes through, there’s no forest floor material to soak up the water and the soil may become water repellent due to heat from the fire.”

Reduced infiltration means increased runoff and erosion. As Robichaud explains, “If you have a steep slope and high velocities, along with very erodible soil, things converge rather quickly and you can generate debris flows and mudslides. It’s not just a 100% increase. It’s orders of magnitude increase.”

Burned trees standing in a swampy area covered in water

After a fire, soil commonly becomes hydrophobic, just one factor in increased runoff.

One of Robichaud’s research interests is in designing a model for post-fire erosion. The model helps land managers and assessment teams in the field to evaluate the risks such erosion might pose. “It lets them see what might be affected if they have an erosion event,” he says.

“Is it going to affect the municipal water supply, affect a road crossing, an interstate highway, a school that happens to be at the mouth of a canyon? Once they can estimate the amount of erosion that might occur, they can use the model to help pick treatments to reduce the risk.”

Often practitioners will use the model to establish an early warning system to areas that will be affected.

Along with developing the model, Robichaud has also looked for ways to help postfire assessment teams gauge the water repellency of the soil after a fire. Historically, soil in a burned area was evaluated using the water drop penetration time test, or WDPT. Team members would place a drop of water on the surface of the soil and time how long it took to be absorbed. This seventies-era test was easy to do in the field, but Robichaud wanted something more representative.

Trees and a street covered in a pool of water

One of Robichaud’s research interests is in designing a model for post-fire erosion to help land managers and assessment teams in the field evaluate the risks such erosion might pose.

“I’ve always felt we could do a better job of characterizing the changes in soil condition,” he says. “[The WDPT] doesn’t really represent the physical process of the water infiltrating, because you put a single drop of water on the surface… The ideal method is a rainfall simulator, but it’s not practical in the field. [You] can’t expect every assessment team after a fire to set up a rainfall simulator for a couple of weeks.”

As he looked for alternatives, Robichaud started using a Mini Disk Infiltrometer. Practitioners all over the world use infiltration measurements along with Robichaud’s model of post-fire erosion to assess the impacts of a fire, predict erosion, and make plans to manage and reduce the associated risks. Robichaud’s online Erosion Risk Management Tool allows researchers and assessment teams alike to use scientifically solid analysis. He’s currently involved in refining and validating the model, improving assessment techniques, using remote sensing technology to perform assessments, and looking at alternative post-fire treatment options to reduce erosion risk, among other things.

To see what Dr. Robichaud’s been up to recently, read his 2014 paper, The temporal evolution of wildfire ash and implications for post-fire infiltration, published in the International Journal of Wildland Fire. Find out more about Robichaud’s research, methods for use of the Mini Disk Infiltrometer for changes in infiltration characteristics after fire, or access the Erosion Risk Management Tool, by visiting the Moscow Forest Sciences Laboratory website.

Learn more about wildfire and soil moisture

See how soil moisture information could improve assessments of wildfire probabilities and fuel conditions, resulting in better fire danger ratings here.

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Sep 11

Scientists and Greenhouse Growers Collaborate to Help the Environment

Bodies of water across the world face extreme pressure from non-point source pollution. It’s easy to get overwhelmed by the sheer enormity of this problem, but it didn’t daunt Dr. John Lea-Cox, Research and Extension Specialist for horticulture at University of Maryland. Dr. Lea-Cox was acutely aware that agriculturally applied fertilizers threatened serious harm to the Chesapeake Bay area near his home. Using an early version of METER’s water content sensors, he began to put together a system that could monitor water status in nursery operations. The effort was based on the work of Dr. Andrew Ristvey (now a colleague at Maryland) who showed water savings of more than 50% during his PhD work using TDR sensors in pots growing ornamental plants. Dr. Lea-Cox and his colleagues wanted to ultimately develop a network of soil moisture and environmental sensors that would help greenhouse and nursery growers know when to turn on and off their water. Their goal was to reduce nutrient and water use through more efficient application.

Close up of a yellow flower with red tipped petals

How did Dr. John Lea-Cox, Research and Extension Specialist for horticulture at University of Maryland, convince nursery growers to reduce water and fertilizer use?

Convincing Growers

One hurdle facing Dr.Lea-Cox was that water savings didn’t resonate with all growers. But he soon realized that better irrigation control influenced things growers did care about: higher quality crops, lower mortality rate, and less spending on pesticides. Dr. Lea-Cox discovered that when he showed growers their moisture sensor data, they were hooked. One snapdragon grower, who found that he could use the sensors to produce a more lucrative A grade crop, said he would not like to go back to the days before sensors. “My gosh, it would be like going back ten years. It would be like trying to measure the temperature in a room without a thermometer. We are totally dependent on them.”

Pink orchids growing in a nursery super green nursery

Orchids grown in a nursery.

Finding Collaborators

Dr. Lea-Cox was not only good at convincing growers, but scientific collaborators as well. Building on this team’s initial findings, he organized a project to develop water retention curves to tie the amount of water in pots to what was actually available to the plant for several different mixes of potting soil. He realized that moisture measurements were practically useless to growers without a mechanism for viewing them all in one place, so he began to look for collaborators who could build an integrated, wireless system to get root zone information to the nursery grower’s computer and allow them to set irrigation limits and automate their systems based on soil and weather data.

The resulting collaboration was a group of diverse scientists and commercial growers who could study root behavior, plant-environmental interactions, the performance of the plants, and individual grower interaction with the system. After a few years of testing, the group received $5M in funding from the Specialty Crops Research Initiative (SCRI) Program over five years to improve horticulture for ornamental plants grown in the U.S.

Lauren Crawford, METER’s soils product manager, says that the resulting collaboration was unique. “It was amazing that an instrumentation company, a research group, and commercial growers were able to work so well together. It was because of the trust we had for each other. We were very transparent about what we were doing, even when we knew that transparency would be difficult. The result was that we were able to make tremendous progress in both science and technology.”

Watch two virtual seminars highlighting SCRI research given by scientists Marc Van Iersel and John Lea-Cox.

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Sep 4

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Examining Plant Stress using Water Potential and Hydraulic Conductivity

Many scientists rely on water potential alone to measure plant water stress. Leo Rivera, a METER soil scientist, shows how a two-pronged approach, using hydraulic conductivity as well as water potential, can make those measurements more powerful.

Green tomato plant with three bright red tomatoes

Measuring hydraulic conductivity in nursery plants shows why plants are stressed.

Soil moisture release curves can give you incredible detail about water movement, allowing you to understand not only that plants are stressed, but WHY they are not getting the water they need.

Recently, we ran into a mystery where this method was useful. Growers at a Georgia nursery noticed that plants growing in a particular soilless substrate were beginning to show signs of stress at about -10 kPa water potential, which is still really wet. They wanted to know why.

We decided to create the unsaturated hydraulic conductivity and soil moisture release curves for the substrate (using the Wind Schindler technique [HYPROP lab instrument]) and found that it had a dual porosity curve: essentially, a curve with a “stair step” in it. The source of the “stair step” can be explained by considering the substrate, which was made up of bark mixed with some other fine organic materials. In the bark material there were a lot of large and small pores, but no medium-sized pores (this is called a “gap-graded” pore size distribution). This gap in the pore size distribution reduced the unsaturated hydraulic conductivity and caused the stress. Even though there was available water in the soil, it couldn’t flow to the plant roots.

Nursery seedlings

That would have been pretty hard to understand without detailed hydraulic conductivity and soil moisture release curves—curves with more detail than most traditional techniques can provide. Our measurements showed that unsaturated hydraulic conductivity can have a major effect on how available water is to plants. Our theory about the soilless substrate was that as the roots were taking up water, they dried the soil around them pretty quickly. In a typical mineral soil, the continuous pore size distribution would allow water to flow along a water potential gradient from the surrounding area to the soil adjacent to the roots. In the bark, the roots dried the area around them in the same way, but the gap in pore size distribution created low hydraulic conductivity and prevented water from moving into the soil adjacent to the roots. This caused plants to start stressing even though the substrate was still quite wet.

We were pretty excited about this discovery. It shows that water potential, though critical, may not always tell the whole story. Using technology to measure the full soil moisture release curve and the hydraulic conductivity in one continuous test, we discovered the real reason plants were wilting even when surrounded by water. In the past, it took three or four different instruments and several months to take these measurements. We can now do it in a week. For more information about creating these kinds of curves, check out the app guide: “Tools and Tips for Measuring the Full Soil Moisture Release Curve.”

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Aug 28

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Low Impact Design: Sensors Validate California Groundwater Resource Management

Michelle Newcomer, a PhD candidate at UC Berkeley, (previously at San Francisco State University), recently published research using rain gauges, soil moisture, and water potential sensors to determine if low impact design (LID) structures such as rain gardens and infiltration trenches are an effective means of infiltrating and storing rainwater in dry climates instead of letting it run off into the ocean.

Body of water with rain droplets hitting the surface

Can Low Impact Design Structures store rainwater?

Low Impact Design Structures

Global groundwater resources in urban, coastal environments are highly vulnerable to increased human pressures and climate variability. Impervious surfaces, such as buildings, roads, and parking lots prevent infiltration, reduce recharge to underlying aquifers, and increase contaminants in surface runoff that often overflow sewage systems. To mitigate these effects, cities worldwide are adopting low impact design (LID) approaches to direct runoff into natural vegetated systems such as rain gardens that reduce, filter, and slow stormwater runoff. LID hypothetically increases infiltration and recharge rates to aquifers.

Infiltration and Recharge

Michelle and the team at San Francisco State University, advised by Dr. Jason Gurdak, realized that the effects of LID on recharge rates and quality were unknown, particularly during intense precipitation events for cities along the Pacific coast in response to inter-annual variability of the El Niño Southern Oscillation (ENSO). Using water potential and water content sensors she was able to quantify the current and projected rates of infiltration and recharge to the California Coastal Westside Basin aquifer system. The team compared a LID infiltration trench surrounded by a rain garden with a traditional turf-lawn setting in San Francisco. She says, “Cities like San Francisco are implementing these LID structures, and we wanted to test the quantity of water that was going through them. We were interested specifically in different climate scenarios, like El Niño versus La Niña, because rain events are much more intense during El Niño years and could cause flash flooding or surface pollutant overflow problems.”

Sensors Tell the Story

The research team looked at the differences in the quantity of water that LID structures could allow to pass through. Michelle says. ”The sensors yielded data proving LID areas were effective at capturing the water, infiltrating it more slowly, and essentially storing it in the aquifer.” The team tested how a low-impact development-style infiltration trench compared to an irrigated lawn and found that the recharge efficiency of the infiltration trench, at 58% to 79%, was much higher than that of the lawn, at 8% to 33%.

Rain Gauges Yield Surprises

Though it wasn’t part of the researchers’ original plan, they used rain gauges to measure precipitation, which yielded some surprising data. Michelle comments, “We were just going to use the San Francisco database, but it became necessary to use the rain gauges because of all the fog. The fog brought a lot of precipitation with it that didn’t come in the form of raindrops. As it condensed on the leaves, it provided a substantial portion of the water in the budget, and that was surprising to me. The rain gauge captured the condensate on the funnel of the instrument, so we were able to see that a certain quantity of water was coming in that is typically neglected in many studies.”

Future El Niño Precipitation

Michelle also found some really interesting results regarding El Niño and La Niña. She says, “I did a historical analysis to establish baselines for frequency, intensity, and duration of precipitation events during El Niño and La Niña years. I then ran projected climate data through a Hydrus-2D model, and the models showed that with future El Niño intensities, recharge rates were effectively higher for a given precipitation event. During these events, in typical urban settings, water runs off so fast that only these rain gardens and trenches would be able to capture the rain that would otherwise be lost to the ocean. This contrasts with a La Niña climate scenario where there’s typically less rain that is more diffuse. Most of that rain may eventually be lost to evaporation during dry years. So using sensors and 2D modeling we have validated the hypothesis that LID structures provide a service for storing water, particularly during El Niño years where there are more intense rainstorms.”

Michelle’s research received some press online and also was featured in the AGU EOS Editor’s spotlight. Her results are published in the journal Water Resources Research.

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Aug 14

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The Potential of Drones in Research

Someday soon, multi-rotors will execute pre-programmed flight paths over several hundred research plots collecting daily data and sending it back to a computer while researchers sip their morning coffee. Researchers and growers won’t need to know anything about flying: the drones will fly themselves. This is the dream.

One UAV (unmanned air vehicle) industry leader at the above drone demonstration commented, “The truth is that this is where agriculture (and research) is going, and I don’t mean ‘Tomorrowland’ going–I mean it’s pretty much there. The only thing that’s holding us back is a permit from the FAA for autonomy, and that’s because the FAA is slowly backing into this UAV piece because we have the busiest general aviation sky in the world. But really, what you should have in your mind is multiple units operating with a single operator in a control vehicle.” The above UAV was extensively tested in California’s NAPA valley with results soon to be published online.

In this blog, a METER scientist and an instrumentation engineer give their perspectives on what needs to happen before drones reach their full research potential.

Drone Hexacopter

What are the advantages of drones for researchers?

Dr. Colin Campbell, research scientist-

One of the biggest challenges of work in the field is variability: low spots, high spots, sandy soil, clay soil, hard pans beneath the surface in some areas and not in others. This results in highly variable performance in crops. In addition to that, even when you have good homogeneity in a field, you might have differences due to irrigation or rainfall. If we want to improve agriculture, one thing that we have to do is be able to come out with better tools to be able to visualize the field in more than a single dimension. In order to do this right now, students go out and take plant measurements all day, every day, all summer long. The advantage of a drone is that you could do flyovers of a field, monitoring the traits that you’re interested in using reflectance indices that would normally take days of work.

What are the obstacles to progress?

Greg Kelley, mechanical engineer, and drone hobbyist-

Recently, the FAA has come out with a set of guidelines for the industrial use of drones: flying machines have to stay under a certain ceiling (500 ft; 150 m), and they have to be flown in the line of sight of the operator. The naive thing about those policies is: how much control does the operator have over the drone anyway? It used to be that with your remote control, you were moving the control surfaces (flaps, rudder, etc) on the aircraft, but this is changing. The onboard computer performs things like holding a stable altitude, maintaining a GPS location, or auto-stabilization (it keeps the aircraft level, even when a gust of wind comes). Those are degrees of control that have been taken away from the operator. Thus, according to the level of automation that the operator has built into the system, he may not be in direct control at all times. In fact, these machines are being developed so that they can fly themselves. From my perspective, the FAA regulations are going to have to evolve along with the automation of drones in order to allow the development of this technology in an appropriate way.

Drone with eight rotors.

What needs to happen before drones reach their full potential?

Dr. Colin Campbell–

Even if we get the flexibility required with drones, we’ve got to get the right sensor on the drone. On the surface, this seems relatively simple. Sensors to measure spectral reflectance are available in a package size that should easily mount on a drone platform. But, there are still many challenges. First, current spectral reflectance sensors make a passive reflectance measurement, meaning we’re at the mercy of the reflected sunlight. Clouds, sun angle, and leaf orientation, among other things, will all affect the measurement. There are several groups working on this (just search “drone NDVI” on the internet), but it’s a difficult problem to solve. Second, drones create a spectral reflectance “map” of a field that needs to be geo-referenced to features on the ground to match measurements with position. Once data are collected, the behavior of “plot A” can only be determined by matching the location and spectral reflectance of “plot A.” Different from the first challenge, this is more related to programming than science but is still a major hurdle.

Despite these challenges, drones promise incredible benefits as an agricultural and environmental measurement tool. As one industry leader at the drone demonstration put it, “the complexity of the problems that agriculture faces and the opportunities for efficiencies are vast. It will require ongoing engagement, next year and the year after that. There are a lot of questions to be answered and the efficacy is yet to be determined, but it’s exciting to watch the UAV helicopter and where it’s going.” Both Campbell and Kelley agree that significant advances will be made within the next few years.

Read about an ROI calculator that’s been created to help growers quantify whether the benefits of using a drone will exceed their costs.

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Measuring and Modeling the Environment

Posts tagged ‘Sensors’

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