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Posts tagged ‘Water content’

Dec 4

Understanding the Influence of Coastal Fog on the Water Relations of a California Pine Forest

Forests along the California coast and offshore islands experience coastal fog in summer, when conditions are otherwise warm and dry. Since fog-water inputs directly augment water availability to forests during the dry season, a potential reduction of fog due to climate change would place trees at a higher risk of water stress and drought-induced mortality.  Dr. Sara Baguskas completed her Ph.D. research in the geography department at UC Santa Barbara on how variation in fog-water inputs impact the water relations of a rare, endemic tree species, Bishop pine, located on Santa Cruz Island in Channel Islands National Park. The goal of her study was to enhance our ability to predict how coastal forests may respond to climate change by better understanding how fog-water inputs influence the water budget of coastal forests.

Fog on Trees

Dr. Baguskas’ study seeks a better understanding of how fog-water inputs influence the water budget of coastal forests.

Fog Manipulation

Santa Cruz Island supports the southern extent of the species range in California, thus it is where we would expect to see a reduction in the species range in a warmer, drier, and possibly less foggy future. To advance our mechanistic understanding of how coastal fog influences the physiological function of Bishop pines, Dr. Baguskas conducted a controlled greenhouse experiment where she manipulated fog-water inputs to potted Bishop pine saplings during a three-week drydown period. She installed soil moisture (VWC) sensors horizontally into the side of several pots of sapling trees at two different depths (2 cm and 10 cm) and exposed the pines to simulated fog events with a fog machine.

In one group of plants, Baguskas let fog drip down to the soil, and in another treatment, she prevented fog drip to the soil so that only the canopies were immersed in fog.  She adds, “Leaf wetness sensors were an important complement to soil moisture probes in the second treatment because I needed to demonstrate that during fog events, the leaves were wet and soil moisture did not change.” Additionally, Baguskas used a photosynthesis and fluorescence system to measure photosynthetic rates in each group.

Fog in pine trees from the ground

The fog events had a significant, positive effect on the photosynthetic rate and capacity of the pines.

Results

Dr. Baguskas found that the fog events had a significant, positive effect on the photosynthetic rate and capacity of the pines.  The combination of fog immersion and fog drip had the greatest effect on photosynthetic rates during the drydown period, so, in essence, she determined that fog drip to the soil slows the impact of drydown.  

“But,” she says, “when I looked at fog immersion alone, when the plant canopies were wet by fog with no drip to the soil, I also saw a significant improvement in the photosynthetic rates of these plants compared to the trees that received no fog at all, suggesting that there could have been indirect foliar uptake of water through these leaves which enhanced performance.”  An alternative interpretation of that, Baguskas adds, is that nighttime fog events reduced soil evaporation rates, resulting in less evaporative loss of soil moisture.

Dr. Baguskas says her “canopy immersion alone” data are consistent with other research: Todd Dawson, Gregory Goldsmith, Kevin Simmonin, Carter Berry, and Emily Limm have all found that when you wet plant leaves, it has a physiological effect, suggesting the plants are taking water up through their leaves and not relying as much on soil moisture.  (These authors performed different types of experiments, but their papers serve as reference studies). Baguskas says, “My results suggest that is what’s going on, but it’s not as definitive as other studies that have actually worked on tracking the water through leaves using a stable isotope approach.”  

Lessons Learned

Though Dr. Baguskas did not monitor soil temperature in this study, she says that in the future, she will always combine temperature data with soil moisture data.  She comments, “Consistently, the soil moisture in the “canopy-immersed only” plants was slightly elevated over the soil moisture in the control plants.  It made me wonder if this was a biologically meaningful result. Does it support the fact that if plants are taking up water through their leaves, they don’t rely on as much soil moisture?  Or did my treatment change soil temperature, and is that having a confounding effect on my results?  What I’ve learned from this, is that in the future I will always use soil probes with temperature sensors because you may not know until you see your results if temperature might be important.”

Future Fog Studies

Baguskas is a USDA-NIFA postdoctoral Research Fellow working with Dr. Michael Loik in the Environmental Studies Department at UC Santa Cruz. She continues to study coastal fog, but now in strawberry fields. Her current research questions are focused on integrating coastal fog into water-use decisions in coastal California agriculture. She loves the work and continues to rely on soil moisture sensors to make meaningful and reliable environmental measurements in the field and greenhouse.

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Nov 9

Screening for Drought Tolerance

Screening for drought tolerance in wheat species is harder than it seems.  Many greenhouse drought screenings suffer from confounding issues such as soil type and the resulting soil moisture content, bulk density, and genetic differences for traits like root mass, rooting depth, and plant size. In addition, because it’s so hard to isolate drought stress, some scientists think finding a repeatable screening method is next to impossible. However, a recent pilot study done by researcher Andrew Green may prove them wrong.

An automatic irrigation setup with green plants sticking out

Automatic Irrigation Setup

The Quest for Repeatability

Green says, “There have been attempts before of intensively studying drought stress, but it’s hard to isolate drought stress from heat, diseases, and other things.”  Green and his advisors, Dr. Gerard Kluitenberg and Dr. Allan Fritz, think monitoring water potential in the soil is the only quantifiable way to impose a consistent and repeatable treatment. With the development of a soil-moisture retention curve for a homogeneous growth media, they feel the moisture treatment could be maintained in order to isolate drought stress.  Green says, “Our goal is to develop a repeatable screening system that will allow us to be confident that what we’re seeing is an actual drought response before the work of integrating those genes takes place, since that’s a very long and tedious process.”

Why Hasn’t This Been Done Before?

Andrew Green, as a plant breeder, thinks the problem lies in the fact that most geneticists aren’t soil scientists. He says, “In past experiments, the most sophisticated drought screening was to grow the plants up to a certain point, stop watering them, and see which ones lived the longest. There’s never been a collaborative approach where physiologists and soil scientists have been involved.  So researchers have imposed this harsh, biologically irrelevant stress where it’s basically been an attrition study.” Green says he hopes in his research to use the soil as a feedback mechanism to maintain a stress level that mimics what exists in nature.

Data acquisition a cabinet setup for green's expanded experiment

Data Acquisition Cabinet setup for Green’s expanded experiment.

The Pilot Study

Green used volumetric water content sensors, matric potential sensors, as well as column tensiometers to monitor soil moisture conditions in a greenhouse experiment using 182 cm tall polyvinyl chloride (PVC) growth tubes and homogenous growth media. Measurements were taken four times a day to determine volumetric water content, soil water potential, senescence, biomass, shoot, root ratio, rooting traits, yield components, leaf water potential, leaf relative water content, and other physiological observations between moisture limited and control treatments.  

Soil Media:  Advantages and Disadvantages

To solve the problem of differing soil types, Andrew and his team chose a homogeneous soil amendment media called Profile Greens Grade, which has been extensively studied for use in space and other applications.  Green says, “It’s a very porous material with a large particle size.  It’s a great growth media because at the end of the experiment you can separate the roots of the plant from the soil media, and those roots can be measured, imaged, and studied in conjunction with the data that is collected.”   Green adds, however, that working with soil media isn’t perfect: there have been hydraulic conductivity issues, and the media must be closely monitored.

What’s Unique About this Study?

Green believes that because the substrate was very specific and his water content and water potential sensors were co-located, it allowed him to determine if all of his moisture release curves were consistent.  He says, “We try to pack these columns to a uniform bulk density and keep an eye on things when we’re watering, hoping it’s going to stay consistent at every depth.  So far it’s been working pretty well: the water content and the water potential are repeatable in the different columns.”

Irrigation setup for the expanded study with research data cabinet

Entire Irrigation setup for the expanded study.

Plans for the Future

Green’s pilot study was completed in the spring, and he’s getting ready for the expanded version of the project:  a replicated trial with wild relatives of wheat. He’s hoping to use soil moisture sensors to make automatic irrigation decisions: i.e. the water potential of the columns will activate twelve solenoid valves which will disperse water to keep the materials in their target stress zone, or ideal water potential.

The Ultimate Goal

The ultimate goal of Green’s research is to breed wild species of wheat into productive forms that can be used as farmer-grown varieties. He is optimistic about the results of his pilot study.  He says, “Based on the very small unreplicated data that we have so far, I think it is going to be possible to develop a repeatable method to screen these materials.  With the data that we’re seeing now, and the information that we’re capturing about what’s going on below ground, I think being able to hold these things in a biologically relevant stress zone is going to be possible.”

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Six short videos teach you everything you need to know about soil water content and soil water potential—and why you should measure them together.  Plus, master the basics of soil hydraulic conductivity.

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Oct 31

Water Potential/Water Content:  When to Use Dual Measurements

In a previous post, we discussed water potential as a better indicator of plant stress than water content.  However, in most situations, it’s useful to take dual measurements and measure both water content and water potential.  In a recent email, one of our scientist colleagues explains why: “The earlier article on water potential was excellent.  But what should be added is an explanation that the intensity measurement doesn’t translate directly into the quantity of water stored or needed. That information is also required when managing water through irrigation.  This is why I really like the dual measurement approach. I am excited about the possibilities of information that can be gleaned from the combined set of water content, water potential, and spectral reflectance data.”

Field plantation with a sprinkler in the middle of it

Potato field irrigation

Managing Irrigation

The value of combined data can be illustrated by what’s been happening at the Brigham Young University Turf Farm, where we’ve been trying to optimize irrigation of turfgrass (read about it here). As we were thinking about how to control irrigation, we decided the best way was to measure water potential.  However, because we were in a sandy soil where water was freely available, we also guessed we might need water content. Figure 1 illustrates why.

Turf farm data concerning water potential diagram

Figure 1: Turf farm data: water potential only

Early water potential data looks uninteresting; it tells us there’s plenty of water most of the time, but doesn’t indicate if we’re applying too much.  In addition, if we zoom in to times when water potential begins to change, we see that it reaches a stress condition quickly.  Within a couple of days, it is into the stress region and in danger of causing our grass to go into dormancy.  Water potential data is critical to be able to understand when we absolutely need to water again, but because the data doesn’t change until it’s almost too late, we don’t have everything we need.

Turf farm data dual measurements data diagram

Figure 2: Turf farm data, volumetric water content only

Unlike water potential, the water content data (Figure 2) are much more dynamic. The sensors not only show the subtle changes due to daily water uptake but also indicate how much water needs to be applied to maintain the root zone at an optimal level. However, with water content data alone, we don’t know where that optimal level is. For example, early in the season, we observe large changes in water content over four or five days and may assume, based upon onsite observations, that it’s time to irrigate. But, in reality, we know little about the availability of water to the plant. Thus, we need to put the two graphs together (Figure 3).

Water potential and vol. water content diagram

Figure 3: Turfgrass data: both water potential and volumetric water content together.

In Figure 3, we have the total picture of what’s going on in the soil at the BYU turf farm. We see the water content going down and can tell at what percentage the plants begin to stress.  We also see when we’ve got too much water: when the water content is well above where our water potential sensors start to sense plant stress. With this information, we can tell that the turfgrass has an optimal range of 12% to 17% volumetric water content. Anything below or above that range will be too little or too much water.  

Soil water potential and volumetric water content diagram

Figure 4: Turfgrass soil moisture release curve (black). Other colors are examples of moisture release curves for different types of soil.

Dual measurements will also allow you to make in situ soil moisture release curves like the one above (Figure 4), which detail the relationship between water potential and water content.  Scientists can evaluate these curves and understand many things about the soil, such as hydraulic conductivity and total water availability.

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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.

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.”  

MPS2 Water Potential in Concrete diagram

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.

Frozen ground with horseshoe prints

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

31 Comments

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.”

Water potential changes diagram

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.”

VWC and Water potential sensors diagram

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.”

Shallow Buried Mine- Soil Moisture as a Function of Depth diagram

Disturbed and Undisturbed Soil- Soil Moisture as a Function of Depth Diagram

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 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.

Download the “Researcher’s complete guide to soil moisture”—>

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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.

Three pictures the first depicts an aerial view of an infiltration trench, the second depicts an infiltration trench site, and the third depicts a irrigated green lawn

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.”

Infiltration trench site diagram

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%.

Daily time series of precipitation and volumetric water content

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.

Download the “Researcher’s complete guide to water potential”—>

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Aug 22

Author Interview: Soil Physics with Python

The new book Soil Physics with Python: Transport in the Soil-Plant-Atmosphere System written by Dr. Marco Bitteli, Dr. Gaylon S. Campbell, and Dr. Fausto Tomei presents concepts and problems in soil physics as well as solutions using original computer programs.

Picture of the cover of the book "Soil Physics with Python" by Marco Bittelli, Gaylon S. Campbell, and Fausto Tomei

Soil Physics with Python

In contrast to the majority of the literature on soil physics, this text focuses on solving, not deriving, differential equations for transport. Numerical methods convert differential equations into algebraic equations, which can be solved using conventional methods of linear algebra.  Here, Dr. Campbell interviews about this update to his classic book Soil Physics with BASIC.

Why did you write the first book, Soil Physics with BASIC?

Soil physics classes were always frustrating for me because you would spend time writing fancy equations on the chalkboard, and in the end, you couldn’t do anything with them.  You couldn’t solve any of the problems because, even though they involved difficult mathematics, the math was still so simplified that it didn’t apply to anything that went on in nature.

When I taught my first graduate soil physics class, I determined that we were going to be able to do something by the time we finished.  Luckily, in the mid-1970s, personal computers were being developed, and I realized this was the answer to my problem.  Numerical methods could solve any problem with any geometry in it.  It wasn’t limited to problems that fit the assumptions needed to derive a complex differential equation.  I could write computer programs that simplified the mathematics for the students and teach them how to solve those problems using numerical methods.  By the end of the semester, my students would have a set of tools that they could use to solve problems in the real world.  

Did this book come from class notes or some other source?  

I wrote two textbooks and they both came the same way.  When I first started teaching, I had a textbook that was inadequate, so I began writing notes of my own and handing them out to the students.   After two years, I turned these notes into An Introduction to Environmental Biophysics.  Soil Physics with BASIC came about by the same process, but I enlisted the help of my daughter, Julia, to type it up. It was in the early days of word processing so entering equations was quite difficult.  It all went well for her until chapter eight, which was a nightmare of greek symbols. After she finished slogging for days through the material, we somehow lost the chapter.  She retyped it, and we lost it again, making her type it three times!  We didn’t have spreadsheets then either, so the figures were all hand-drawn by my daughter, Karine.

Red soil in the desert with trees and brush around

Marco [Bitteli] has added two and three-dimensional flow problems, so you can model whole landscapes and water behavior in an entire terrain.

What does Soil Physics with Python add to the conversation?

First, it updates the programming language.  BASIC was a language invented at Dartmouth and intended to be a simple teaching language.  It was never supposed to be a scientific computer language.  Python (13:26.) is a newer language, and there are many open source programs for it, making it a better language to use for science.

Secondly, the old book had one-dimensional flow problems in it for the most part, but Marco [Bitteli] has added two and three-dimensional flow problems, so you can model whole landscapes and water behavior in an entire terrain.

In addition, Dr. Bitteli describes the process and analysis of soil treated as fractals as well as soil image analysis.  There are a lot of extensions and updates that weren’t in the original book.  

Will it be accessible across all disciplines?

To some extent, different disciplines speak different languages.  A soil physicist talks about water potential, and a geotechnical engineer talks about soil suction. Thus, there may be some translation of discipline-specific terms, but it’s intended to be a book that people in the plant sciences can use along with people in the soil sciences.

Dr. Marco Bitteli earned his PhD at Washington State University and was Dr. Campbell’s former student.  This book is a product of their continued collaboration. Dr. BBitteli is now a professor at University of Bologna, the oldest university in operation in the world.  Soil Physics with Python  is available at Amazon.com.

Download the “Researcher’s complete guide to water potential”—>

Download the “Researcher’s complete guide to soil moisture”—>

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