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Mar 13

Best Research Instrument Hacks

We wanted to highlight innovative ways people have modified their instrumentation to fit their research needs.  Here, Georg von Unold, founder and president of UMS (now METER) illustrates ingenuity in a story that inspired the invention of the first UMS tensiometer and what could be one of the greatest scientific instrument hacks of all time.

Image of the Bavarian Alps with snow on top

The Bavarian Alps

An Early Penchant for Ingenuity

In 1986, graduating German students were required to join the military or perform civil service.  Von Unold chose to do a civil service project investigating tree mortality in the alpine region of the Bavarian Mountains.  He explains, “We were trying to understand pine tree water stress in a forest decline study related to storms in certain altitudes where trees were inexplicably falling over. The hypothesis was that changing precipitation patterns had induced water stress.”  

To investigate the problem, von Unold’s research team needed to find tensiometers that could measure the water stress of plants in the soil, which was not easy. The tensiometers von Unold found were not able to reach the required water potential without cavitating, so he decided to design a new type of tensiometer.  He says, “I showed my former boss the critical points. It must be glued perfectly, the ceramic needed defined porosity, a reliable air reference access, and water protection of the pressure transducer. I explained it with a transparent acrylic glass prototype to make it easier to understand. At a certain point, my boss said, “Okay, please stop. I don’t understand much about these things, but you can make those on your own.”

Two snorkels protecting a data logger from relative humidity

Two snorkels protected a data logger predecessor from relative humidity.

Snorkels Solve a Research Crisis

The research team used those tensiometers (along with other chemical and microbial monitoring) to investigate why trees only in the precise altitude of 800 to 1100 meters were dying. One challenge facing the team was that they didn’t have access to anything we might call a data logger today.  Von Unold says, “We did have a big process machine from Schlumberger that could record the sensors, but it wasn’t designed to be placed in alpine regions where maximum winter temperatures reached -30℃ or below. We had to figure out how to protect this extremely expensive machine, which back then cost more than my annual salary.“

Von Unold’s advisor let him use the machine, cautioning him that the humidity it was exposed to could not exceed 80%, and the temperature must not fall below 0℃.  As von Unold pondered how to do this, he had an idea. Since the forest floor often accumulated more than a meter of snow, he designed an aluminum box with two snorkels that would reach above the snow.  The snorkels were guided to a height of two meters.  Using these air vents, he sucked a small amount of cold, dry air into the box. Then, he took his mother’s hot iron, bought a terminal switch to replace the existing one (so it turned on in the range of 0-30℃), and mounted a large aluminum plate on the iron’s metal plate to better distribute the heat.

Von Unold says, “Pulling in the outside air and heating it worked well. The simple technique reduced the relative humidity and controlled the temperature inside the box. Looking back, we were fortunate there wasn’t condensing water and that we’d selected a proper fan and hot iron. We didn’t succeed entirely, as on hot summer days it was a bit moist inside the box, but luckily, the circuit boards took no damage.”

Fog in trees in a pine forest

Tree mortality factors were only found at the precise altitude where fog accumulated.

Finding Answers

Interestingly, the research team discovered there was more to the forest decline story than they thought. Fog interception in this range was extremely high, and when it condensed on the needles, the trees absorbed more than moisture.  Von Unold explains, “In those days people of the Czech Republic and former East Germany burned a lot of brown coal for heat. The high load of sulfur dioxide from the coal reduced frost resistivity and damaged the strength of the trees, producing water stress.  These combined factors were only found at the precise altitude where the fog accumulated, and the weakened trees were no match for the intense storms that are sometimes found in the Alps.”  Von Unold says once the East German countries became more industrialized, the problem resolved itself because the people stopped burning brown coal.

Share Your Hacks with Us

Do you have an instrument hack that might benefit other scientists?  Send your idea to [email protected].

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Mar 6

New Infiltrometer Helps City of Pittsburgh Limit Traditional Stormwater Infrastructure (Part 2)

To save the aesthetics of Dellrose Street, an aging, 900 ft. long, brick road, the city of Pittsburgh wanted to limit traditional stormwater infrastructure (see part 1). Jason Borne, a stormwater engineer for ms consultants and his team decided permeable pavers was a viable option, and used two different types of infiltrometers to determine soil infiltration potential.  Here’s how they compared.

Looking down the street where researchers are doing their installation

Setting up the infiltrometers.

Shortened Test Times Allow Design Changes on the Fly

Though most of the subsoil was a clay urban fill, there was a distinct transition between that clay material to a broken shale/clay mixture.  Borne says, “After excavation, it rained, and we saw that the water was disappearing through the broken shale/clay material.  When we did the infiltration tests, the broken shale/clay showed a higher infiltration potential than the clay fill material.  That led us to modify the design of the subsurface flow barriers based on specific observed infiltration rates of the subsoils. Where the tests showed higher hydraulic conductivity values, we were able to rely on infiltration entirely to remove the water from behind the check dams.”  Borne adds that in the areas where infiltration was poor, they augmented infiltration with a slow release concept. “We put some weep holes in the flow barrier and let the water trickle out down to the next barrier and so on.  Basically, the automated SATURO infiltrometer allowed us to do many tests in a short amount of time to establish a threshold of where good infiltrating soils and poor infiltrating soils were located.  This enabled us to change the design on the fly.  The double ring infiltrometer takes significantly more time to do a test, and time is of the essence when the contractor wants to backfill the area and get things moving. It was nice to have a tool that got us the information we needed more rapidly.”

Image of a SATURO double ring infiltrometer

SATURO Infiltrometer

How did the Double Ring and SATURO Compare?

Borne says the SATURO Infiltrometer was faster and reduced the possibility of human error.  He adds, “We liked the idea of it being very standardized. The automated plot of flux over time was also of great interest to us, because we could see a trend, or anomalies that might invalidate the results we were getting. The double ring infiltrometer takes a long time to achieve a state of equilibrium, and it’s hard to know when that occurs. You’re following the Pennsylvania Department of Environmental Protection suggested guidelines, but they’re very generalized.  To me it doesn’t suit all situations.  What we found with the SATURO infiltrometer is it records information at very discreet intervals, plots a curve of the flux over time, and when it levels out, you basically achieve equilibrium.  You get to that state of equilibrium faster.  There’s a water savings, but there’s also a time savings.  And there’s the satisfaction of getting standardized results rather than the possibility of each technician applying the principles in a slightly different way, as they might with the double ring infiltrometer.”

Borne and his team were ultimately able to prepare a permeable paver street design which allowed for the exclusion of traditional storm sewer infrastructure, reducing both capital costs and long-term maintenance life cycle costs. The permeable paver concept is intended to provide a template for the city of Pittsburgh to apply to the future reconstruction of other city streets.

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Feb 27

New Infiltrometer Helps City of Pittsburgh Limit Traditional Stormwater Infrastructure

Though difficult and expensive to restore, the brick-paved streets that still exist in some Pennsylvania neighborhoods are a treasure worth preserving, according to the City of Pittsburgh. Dellrose Street, an aging, 900 ft. long, brick road, was in need of repair, but the city of Pittsburgh wanted to limit traditional stormwater infrastructure, such as pipes and catch basins.

Pennsylvania brick road

Dellrose Street permeable paver system

To save the aesthetics of the neighborhood, they hired ms consultants, inc. to design a permeable paver solution for controlling stormwater runoff volumes and peak runoff rates that would traditionally be routed off-site via storm sewers.  Jason Borne, a stormwater engineer for ms consultants who worked on the project says, “What we try to do is understand the in situ infiltration potential of the subsoils to determine the most efficient natural processes for attenuating flows; either through infiltrating excess water volume back into the soil or through slow-release off-site.”  He used the SATURO Infiltrometer to get an idea of how urban fill material would infiltrate water.

Green Infrastructure Aids Natural Infiltration

As Borne and his team investigated what they could do to slow down the runoff, they decided permeable pavers would be a viable solution.  He says, “There’s not much you can do once you put in a hardened surface like a pavement.  Traditional pavement surfaces accelerate the runoff which requires catch basins and large diameter pipes to carry the runoff off-site. We were interested in investigating what some of the urban subsoils or urban fill would allow us to do from an infiltration perspective.  As we started looking at some of these subsoils, we decided a permeable paver system would be ideal for this particular street.”

Researchers install a subsurface flow barrier

Subsurface flow barrier installation

Infiltrometers Determine Natural Infiltration Potential

Once the water flowed into the aggregate, the team began to figure out ways to slow it down and promote infiltration.  Borne says, “Basically we came up with a tiered subsurface flow barrier system.  We had about 60 concrete flow barriers across the subgrade within the aggregate base of the road. We needed so many because the longitudinal slope of the road was fairly significant. Behind each of these barriers we stored a portion of the stormwater that would typically run off the site.  The ideal was to remove the stored water through infiltration—to get it down to the subgrade and away, so we used infiltrometers to help us establish where we could maximize infiltration and where we might need to rely on other management methods.”

A Need for Faster Test Times Inspires a Comparison

Borne says that USDA soil surveys are too generalized for green infrastructure applications in urban areas and only give crude approximations of the soil hydraulic conductivity. Understanding the best way to promote natural infiltration requires a very specific infiltration rate or hydraulic conductivity for the location of interest.  He says, “The goal is to excavate down to the desired elevation before construction and find out, through some kind of device what the infiltration potential of the subsoil is.  Typically we use a double ring infiltrometer, but it’s a very manual device. We’re constantly refilling water, and it requires us to be on-site and attentive to what’s happening.  We can’t really multitask, especially in areas of decently infiltrating soils where the device might run out of water in 30 minutes or less. So, in the interest of saving water and time, we used the automated SATURO infiltrometer and the manual double ring infiltrometer concurrently for comparison purposes.”

Next week:  Find out how the two infiltrometers compared.

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Feb 20

How to Get More From Your NDVI Sensor (Part 3)

In the conclusion of our three-part series on improving NDVI sensor data (see part 2), we discuss how to correct for limitations which occur in high leaf area index (LAI) conditions.

NDVI Sensor

Where there’s a large amount of vegetation, NDVI tends to saturate.

NDVI Limitations – High LAI

NDVI is useful in the midrange of LAI’s as long as you don’t have strong soil effects, but as you approach an LAI above 4, you lose sensitivity. In figure 6, loss of sensitivity is primarily due to a saturation in the red band. Measurements were taken in a wheat canopy and a maize canopy. The near-infrared reflectance is sensitive across the entire spectrum of the wheat and maize canopies, but the red saturates relatively quickly. Where the red starts to saturate is where the NDVI starts to saturate.

NDVI Sensor

Figure 6: Gitelson (2004) J. Plant Phys

Note: NDVI saturates at high LAI’s, however, if your purpose is to get at the fractional interception of light, NDVI tends not to have the saturation issue. In Figure 7, Fpar or the fractional interception of light of photosynthetically radiation is nearly complete far before NDVI saturates. This is because canopies are efficient at intercepting light, and once we get to an LAI of about 4, most of the light has been intercepted or absorbed by the canopy.  Thus, incremental increases in LAI don’t significantly affect the FPar variable.

NDVI Sensor

Figure 7: Fractional interception of light is near complete at an LAI around 4. (Gamon et al. (1995) Eco. Apps)

Solution 3- WDRVI

One solution for the NDVI saturation issue is called the Wide Dynamic Range Vegetation Index (WDRVI). Its formulation is similar to NDVI, except for a weighting coefficient that can be used to reduce the disparity between the contribution of the near infrared and red reflectance.  

NDVI Sensor

In the WDRVI, a is multiplied by the near-infrared reflectance to reduce its value and bring it closer to the red reflectance value. In doing so, it balances out the red and the near-infrared contribution to the vegetation index.

NDVI Sensor

Figure 8: (Gitelson (2004) J. Plant Phys)

a can range anywhere from 0 to 1. Figure 8 shows that as we use a smaller value of a, we get an increasing linear response of the wide dynamic vegetation index to LAI.

The only drawback of the WDRVI is that the selection of a is subjective. It’s something that you experiment on your own until you find a value for a that is optimal for your solution.  People tend to err on the side of a very low value simply because they’ll get closer and closer to a linear response to LAI as a decreases.

Solution 4 – Enhanced Vegetation Index

The enhanced vegetation index (EVI) was designed to enhance sensitivity in high biomass ecosystems, but it also attempts to reduce atmospheric influences.  This was a vegetation index created for the purposes of a satellite-based platform. There’s a lot of atmosphere to look through from a satellite to the ground, and sometimes the aerosols in the atmosphere affect the reflectances in the red and the near infrared regions causing spurious observations.  The EVI also tries to reduce sensitivity of the index to soil. Thus the EVI is a kind of solution to both extremes.

NDVI Sensor

In the EVI equation, the two major inputs are near infrared and red reflectances.  C1 , C2, and L are all parameters that can be estimated, but the blue band is something that has to be measured. Most NDVI sensors are two band sensors, so you don’t have that information in the blue.  Plus, with satellites, the blue band is relatively noisy and doesn’t always have the best quality data, thus EVI has limited value.

Solution 6: EVI2 (Enhanced Vegetation Index 2)

Those problems led a scientist named Jiang to come up with a solution.  Jiang observed quite a bit of autocorrelation between the red band and the blue band, so he decided to try and formulate EVI without the blue band in what he called the EVI2 (Enhanced Vegetation Index 2).  if you’re interested in the mathematics, we encourage you to read his paper, but here we give you the equation in case you’re interested in using it.

NDVI Sensor

Figure 9

When Jiang calculated his EVI2 and compared it to the traditional EVI (Figure 9), it was nearly a one to one relationship. For all intents and purposes EVI2 was equivalent to EVI.  Since this avoids blue band, it offers some exciting possibilities as it reduces to just using the two inputs of NIR and red bands to calculate NDVI.

NDVI Sensor Summary

NDVI measurements have considerable value, and though there are extremes where NDVI performs poorly, even in these cases there are several solutions.  These solutions all use the near infrared and the red bands, so you can take an NDVI sensor, obtain the raw values of NIR and red reflectances and reformulate them in one of these indices (there are several other indices available that we haven’t covered). So if you’re in a system with extremely high or low LAI, try to determine how near infrared and red bands can be used in some type of vegetation index to allow you to research your specific application.

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Feb 13

Get More From Your NDVI Sensor (Part 2)

Last week we discussed Normalized Difference Vegetation Index (NDVI) sampling across a range of scales both in space and in time, from satellites sampling the entire earth’s surface to handheld small sensors that measure individual plants or even leaves (see part 1).  This week, learn about NDVI applications, limitations, and how to correct for those limitations.

Field with crop seedlings starting to sprout

Limitations of the Normalized Difference Vegetation Index tend to occur at the extremes of the spectrum.

Green crops in a field

NDVI Applications

People use NDVI to infer things like leaf area index (LAI) or fractional light interception (FPAR) of a canopy.  Some scientists also associate NDVI with biomass or yield of a crop. People also use NDVI to get a sense of phenology (general temporal patterns of greenness), as well as where vegetation occurs or how much vegetation is in a particular location.

In Figure 4, you can see how the reflectance spectrum at a given canopy LAI changes with leaf area index, decreasing in the visible range while increasing in the near infrared.

Diagram depicting NDVI Sensor data

Figure 4

At very low LAI’s, the reflectance spectrum is relatively undifferentiated between red and NIR (black line), but when LAI is high, there’s a strong absorption of red light by chlorophyll with a strong reflectance in the NIR. In fact, as LAI increases, there’s an ever-increasing reflectance in the near infrared around 800 nm.

NDVI Limitations

Limitations of the Normalized Difference Vegetation Index tend to occur at the extremes of the spectrum. Any time there’s very low vegetation cover (majority of the scene is soil), NDVI will be sensitive to that soil. This can confound measurements.  On the other extreme, where there’s a large amount of vegetation, NDVI tends to saturate. Notice the negligible difference between spectra at a leaf area index (LAI) of 3 (purple) versus 6 (green). Indeed, in a tropical forest, NDVI will not be sensitive to small changes in the LAI because LAI is already very high.  However, several solutions exist.

Solution 1-Soil Adjusted Vegetation Index

Figure 5 shows the results of a study taking spectral measurements of different vegetation indices across a transect of bare soil.  Moving from dry clay loam to wet clay loam, we see a very strong response of NDVI due to the wetness of the soil; undesirable if we’re measuring vegetation.  We’re not interested in an index that’s sensitive to changes in soil or soil moisture. However, there are a few other indices plotted in figure 5 with much lower sensitivities to variations in the soil across the transect.

Diagram of Maricopa Aircraft Data

Figure 5: Qi et al. (1994) Rem. Sens. Env.

The first one of those indices is the Soil Adjusted Vegetation Index (SAVI). The equation for SAVI is similar to NDVI. It incorporates the same two bands as the NDVI—the near infrared and the red.

Image depicts two equations one is NDVI and the other is SAVI

Soil Adjusted Vegetation Index (Huete (1988) Rem. Sens. Env.)

The only thing that’s different, is the L parameter.  L is a soil adjustment factor with values that range anywhere from 0 to 1.  When vegetation cover is 100%, L is 0 because there’s no need for a soil background adjustment. However, when vegetation cover is very low, that L parameter will approach one. Because it is difficult to measure exactly how much vegetation cover you have without using NDVI, we can modify the NDVI so it’s not sensitive to soil by guessing beforehand what L should be. It’s common practice to set L to an intermediate value of 0.5. You can see in Figure 5 the Soil Adjusted Vegetation Index or SAVI has a much lower sensitivity to the soil background.

Solution 2- Modified SAVI

The next vegetation index is the modified SAVI (MSAVI). The SAVI equation contains an L parameter that we have to estimate—not an accurate way of handling things.  So a scientist named Key developed a universal optimum for L. We won’t get into the math, but he was able to simplify the SAVI equation to where there’s no longer a need for the L parameter, and the only inputs required are the reflectances in the near infrared and the red.  

Image depicts two equations SAVI is the top equation while the bottom equation is modified SAVI or MSAVI

Modified SAVI (Qi et al. (1994) Rem. Sens. Env.)

This was a pretty significant advance as it circumvented the need to estimate or independently measure L. When Key compared SAVI to MSAVI, there was virtually no difference between the two indices in terms of their sensitivity to the amount of vegetation and their response to the soil background.

Depicts a compairson of MSAVI and SAVI in terms of dynamic range and noise level

MSAVI compares well with SAVI in terms of dynamic range and noise level (Qi et al. (1994) Rem. Sens. Env.)

Next week:  Learn about solutions for high LAI.

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Feb 6

Get More From Your NDVI Sensor

Modern technology has made it possible to sample Normalized Difference Vegetation Index (NDVI) across a range of scales both in space and in time, from satellites sampling the entire earth’s surface to handheld small sensors that measure individual plants or even leaves.

Flat map of the earth depicting NDVI amounts covering the contents

Figure 1: NDVI is sensitive to the amount of vegetation cover that is present across the earth’s surface.

NDVI – Global

The broadest way to think of NDVI is data obtained from an earth orbiting satellite. In the figure above, you can see highly vegetated areas that have high NDVI values represented by dark green colors across the globe.  Conversely, areas of low vegetation have low NDVI values, which look brown.  NDVI is sensitive to the amount of vegetation cover that is present across the earth’s surface.

NDVI – Local

How might NDVI be useful at the plot level? Figure 2 below shows a successional gradient where time zero is a bare patch of soil, or a few forbs or annual grasses. If we leave that patch of ground for enough time, the vegetation will change: shrubs may take over from grasses and eventually we might see a forest. Across a large area, we may also move from grasslands to forest. In an agricultural system, there is yearly turnover of vegetation—from bare field to plant emergence, maturity, and senescence. This cycle repeats itself every year.  Within these growth cycles NDVI helps to quantify the canopy growth that occurs over time as well as the spatial dynamics that occur across landscapes.

Diagram depicting seasonal growth plotted against spatiotemporal variation

Figure 2: Seasonal growth plotted against spatiotemporal variation

Spectral Reflectance Data

So where does NDVI come from? In Figure 3, the x-axis plots wavelength of light within the electromagnetic spectrum; 450 to 950 nm covers both the visible region and a portion of the near infrared. On the y-axis is percent reflectance.  This is a typical reflectance spectrum from green vegetation.

Chart reflecting data and electromagnetic radiation

Figure 3: Spectral Reflectance Data. (Figure and Images: landsat.gsfc.nasa.gov)

The green hyperspectral line is what we would expect to get from a spectral radiometer.  Reflectance is typically low in the blue region, higher in the green region, and lower in the red region. It shifts dramatically as we cross from the visible to the near infrared. The two vertical bars labeled NDVI give you an idea of where a typical NDVI sensor measures within the spectrum.  One band is in the red region and the other is in the near-infrared region.  

NDVI capitalizes on the large difference between the visible region and the near infrared portion of the spectrum. Healthy, growing plants reflect near-infrared strongly.  The two images on the right of the figure above are of the same area.  The top image is displayed in true color, or three bands–blue, green and red. The image below is a false color infrared image.  The three bands displayed are blue, green, and in place of red, we used the near infrared. The bright red color indicates a lot of near infrared reflectance which is typical of green or healthy vegetation.

The reason NDVI is formulated with red and near infrared is because red keys in on chlorophyll absorption, and near infrared is sensitive to canopy structure and the internal cellular structure of leaves.  As we add leaves to a canopy, there’s more chlorophyll and structural complexities, thus we can expect decreasing amounts of red reflectance and higher amounts of near-infrared reflectance.

How Do We Calculate the NDVI?

Calculation equation of NDVI

The Normalized Difference Vegetation Index takes into account the amount of near-infrared (NIR) reflected by plants. It is calculated by dividing the difference between the reflectances (Rho) in the near-infrared and red by the sum of the two.  NDVI values typically range between negative one (surface water) and one (full, vibrant canopy). Low values (0.1 – 0.4) indicate sparse canopies, while higher values (0.7 – 0.9) suggest full, active canopies.  

The way we calculate the percent reflectance is to quantify both the upwelling radiation (the radiation that’s striking the canopy and then reflected back toward our sensor) as well as the total amount of radiation that’s downwelling (from the sky) on a canopy.  The ratio of those two give us percent reflectance in each of the bands.

Next Week: Learn about NDVI applications, limitations, and how to correct for those limitations.

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Jan 30

Piñon Pine: Studying the Effects of Climate Change on Drought Tolerance (part 2)

Henry Adams, a PhD student at the University of Arizona, is studying the effect of climate change and drought on Piñon Pines in the university’s Biosphere 2 lab (see part 1).  This week, find out how the researchers made comparisons at leaf level, transplanted the trees, and future implications for the Piñon Pine.

Image of a Piñon Pine growing high in the southwest

The Piñon Pine, a conifer with an extensive root system, grows at high elevations in the Southwest. (Image: naturesongs.com)

Sensitivity to Dry Conditions

Another part of the drought study involved a hydrologist who was interested in using weighing lysimeter data to parameterize some models used by hydrologists to model water loss during drought. “The lysimeters are a pain to run, but they’re pretty sensitive,” says Adams. “They can measure with a 0.1 kg precision, so that sounds like a good way to quantify water loss. It turns out that stomatal conductance from the porometer actually appears more sensitive than the weighing lysimeter data. Water loss from the scale hits zero pretty quickly, and we can’t measure any loss after a couple of weeks, but we can still see water loss with our porometer data from the morning and the evening.”

Close up on a Piñon Pine branch

The Piñon Pine’s root system makes it remarkably drought tolerant, but an extended drought in combination with a bark beetle outbreak killed 12,000 hectares of the trees in 2003. (Image: naturesongs.com)

Expanding the Experiment

At the peak of the experiment, Adams had undergraduates and lab techs running up to three porometers at a time all day long, and although he’s still buried in data from the first experiment, he’s looking forward to accumulating even more data. “One limitation of our study is that the trees had pretty small root balls when they arrived. We’ve transplanted some trees [at different elevations at a site] in northern Arizona using a full-sized tree mover to get as big a root to shoot ratio as possible in the transplant. We’ll be using the porometers to try to understand the physiology of how these trees die and to predict their temperature sensitivity in the light of global climate change, using elevation change as a surrogate for temperature. We also have trees at the site that are not transplanted to serve as a control for the transplants.”

Herds of cattle in a dry valley with hills

Some ranchers are happy to see the pines go (Image: travelforumboard.com)

Implications for the Future

Adams acknowledges that not everyone in the Southwest is worried about the Piñon Pine. “We work in a system that doesn’t have a lot of economic value. A lot of the ranchers are happy to see the pines go. They just think there will be a lot more grass for the cattle, and firewood cutters are out there cutting up the dead trees and selling them.” But if temperature alone makes trees more susceptible to drought, the implications go far beyond economics. Adams puts it succinctly, if somewhat mildly: “It’s kind of scary.”

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Jan 23

Piñon Pine: Studying the Effects of Climate Change on Drought Tolerance

In the name of science, Henry Adams has killed a lot of trees. Adams, a PhD student at the University of Arizona, is studying the effect of climate change and drought on Piñon Pines. The Piñon Pine, a conifer with an extensive root system, grows at high elevations in the Southwest. Its root system makes the Piñon Pine remarkably drought tolerant, but in 2002- 03, an extended drought in combination with a bark beetle outbreak killed 12,000 hectares of the trees. It was a 100 year drought, the driest period on record, and interestingly it coincided with temperatures 2 to 3˚C above recorded averages.

Biosphere 2 glass dome where researchers study the effects of climate change

Biosphere 2. Image: wickipedia.org.

Research in Biosphere 2

Adams and his advisors wondered if increasing temperatures due to climate change might exacerbate the effects of drought and accelerate tree die-off. The University of Arizona has an unusual opportunity to test drought conditions and temperature change in its Biosphere 2 lab. Biosphere 2, a unique 3-acre enclosed “living laboratory” in the high Arizona desert, once hosted 8 people for two years of self-contained survival living. Now it hosts research projects, and Adams was able to use space inside to induce drought in two separate treatments of transplanted Piñon pines, one at ambient temperatures and one at temperatures 4˚C above ambient.

Sobering Outlook for the Piñon Pine

“Obviously, the warmer trees should die first,” says Adams. “But we want to test whether temperature change, independent of other factors, accelerates mortality.” If that acceleration in fact occurs, a shorter drought, the kind the Piñon Pine has historically been able to wait out, might cause a significant die-off.

Image of a close up on a Piñon Pine branch

Piñon Pine. Image: Naturesongs.com

Measuring Drought Response

Naturally, Adams and his colleagues did more than just watch how fast trees would die without water. They also studied the trees physiological response to drought, measuring gas exchange, water potential, and stomatal conductance. To measure stomatal conductance, they used a leaf porometer, making almost 9,000 separate measurements in sessions that lasted from sunup to sundown on one very long day once each week.

Stomatal Conductance in Conifers

There isn’t much guidance in the porometer manual for people who want to use it on conifers, so Adams “played around with it a little bit” on non-drought stressed trees before he started his study. He found that the best way to get good readings was to cover the aperture with a single layer of needles. “Needles are this three-dimensional thing,” he explains. “They have stomata on several sides, depending on the species. If you imagine that the fingers on your hand are needles sticking up from a branch, we just took those and pushed them together to make sure that there was just a one needle thick covering over the aperture. If you spread your fingers, that’s what it would be like if you didn’t totally cover the aperture-then you underestimate the conductance. We also found that if we stuck several layers in there, we could drive the conductance number up.

Next week: Find out how the researchers made comparisons at leaf level, transplanted the trees, and future implications for the Piñon Pine.

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Jan 16

German Researchers Directly Measure Climate Change Effects Using Lysimeter Network (part 2)

In Germany, scientists are measuring the effects of tomorrow’s climate change with a vast network of 144 large lysimeters (see part 1).  This week, read about the intense precision required to move the soil-filled lysimeters, how problems are prevented, and how the data is used by scientists worldwide.

Image of truck moving the Lysimeters

Moving the lysimeters

Moving the Lysimeters is not Easy

As noted previously, one TERENO lysimeter weighs between 2.5 and 3.5 tons depending on the soil and the water saturation, so the problem of transporting it without compacting the soil or causing cracks in the soil column caused Georg many sleepless nights.   He explains, “We found a truck with an air venting system, which could prevent vibrations in a wide range. We made a wooden support structure, bought 100 car springs, and loaded the lysimeter on this frame.  After some careful preparation and design adjustments, I told the truck driver, ‘take care, I’m recording the entire drive with my acceleration sensor and data logger so I can see if you are driving faster than I allow.”  Each lysimeter soil surface level was marked to check if the lysimeter was rendered useless due to transport, and the truck was not allowed to go over a railway or a bump in the road faster than 2 km per hour to avoid the consequences of compaction and cracking.

Image of a Tensiometer sticking out of the ground

Tensiometers and soil moisture sensors monitor the hydraulic conditions inside the lysimeters.

Preventing Problems

Understanding the water potential inside the intact lysimeter core is not trivial. Georg and his team use maintenance-free tensiometers, which overcome the typical problem of cavitation in dry conditions as they don’t need to be refilled. Still, this parameter is so critical they installed 3 of them and took the median, which can be weighed in case one of the sensors is not working. Georg says, “There is a robust algorithm behind measuring the true field situation with tensiometers.”

What Happens With the Data?

Georg hopes that many researchers will take advantage of the TERENO lysimeter network data (about 4,000 parameters stored near-continuously on a web server). He says, “Researchers have free access to the data and can publish it. It’s wonderful because it’s not only the biggest project of its kind, each site is well-maintained, and all measurements are made with the same equipment, so you can compare all the data.”  (Contact Dr. Thomas Puetz for access). Right now, over 400 researchers are working with those data, which has been used in over 200 papers.

Picture depicting a Lysimeter plant in a garden with a CO2 fumigation facility located in Austria

Lysimeter plant with CO2 fumigation facility in Austria.

What’s the Future?

Georg thinks 40,000 data points arriving every minute will give scientists plenty of information to work on for years to come. Each year, more TERENO standard lysimeters are installed to enlarge the database. The ones in TERENO have a 1 m2 surface area, which is fine for smaller plants like wheat or grass, but is not a good dimension for big plants like trees and shrubs. Georg points out that you have to take into account effort versus good data. Larger lysimeters present exponentially larger challenges. He admits that, “With the TERENO project, they had to make a compromise. All the lysimeters are cut at a depth of 1.5 m. If there is a mistake, it is the same with all the lysimeters, so we can compare on climate change effects.”  He adds, “After six years, we now have a standard TERENO lysimeter design installed over 200 times around the world, where data can be compared through a database, enhancing our understanding of water in an era of climate change.”

Get more information on applied environmental research in our

Read about 12 large Ecotron weighing lysimeters measuring climate change at the University of Hasselt.

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

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

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Jan 9

German Researchers Directly Measure Climate Change Effects Using TERENO Lysimeters

In Germany, scientists are measuring the effects of tomorrow’s climate change with a vast network of 144 large lysimeters.

Image of Lysimeters in there installation site

The goal of these lysimeters is to measure energy balance, water flux and nutrition transport, emission of greenhouse gases, biodiversity, and solute leaching into the groundwater.

In 2008, the Karlsruhe Institute of Technology began to develop a climate feedback monitoring strategy at the Ammer catchment in Southern Bavaria. In 2009, the Research Centre Juelich Institute of Agrosphere, in partnership with the Helmholtz-Network TERENO (Terrestrial Environmental Observatories) began conducting experiments in an expanded approach.  

Throughout Germany, they set up a network of 144 large lysimeters with soil columns from various climatic conditions at sites where climate change may have the largest impact.  In order to directly observe the effects of simulated climate change, soil columns were taken from higher altitudes with lower temperatures to sites at a lower altitude with higher temperatures and vice versa. Extreme events such as heavy rain or intense drought were also experimentally simulated.

Image of Lysimeter locations in Germany

Lysimeter locations in Germany

Georg von Unold, whose company (formerly UMS, now METER) built and installed the lysimeters comments on why the project is so important. “From a scientific perspective, we accept changes for whatever reason they may happen, but it is our responsibility to carefully monitor and predict how these changes cause floods, droughts, and disease. We need to be prepared to react if and before they affect us.”

How Big Are the Lysimeters?

Georg says that each lysimeter holds approximately 3,000 kilograms of soil and has to be moved under compaction control with specialized truck techniques.  He adds, “The goal of these lysimeters is to measure energy balance, water flux and nutrition transport, emission of greenhouse gases, biodiversity, and solute leaching into the groundwater. Researchers measure the conditions of water balance in the natural soil surrounding the lysimeters, and then apply those same conditions inside the lysimeters with suction ceramic cups that lay across the bottom of the lysimeter.  These cups both inject and take out water to mimic natural or artificial conditions.”

Image of Lysimeters in a field and a diagram of whats inside the Lysimeters

Researchers use water content sensors and tensiometers to monitor hydraulic conditions inside the lysimeters.

Researchers monitor the new climate situation with microenvironment monitors and count the various grass species to see which types become dominant and which might disappear. They use water content sensors and tensiometers to monitor hydraulic conditions inside the lysimeters. The systems also use a newly-designed system to inject CO2 into the atmosphere around the plants and soil to study increased carbon effects.  Georg says, “We developed, in cooperation with the HBLFA Raumberg Gumpenstein, a new, fast-responding CO2 enrichment system to study CO2 from plants and soil respiration. We analyze gases like CO2, oxygen, and methane. The chambers are rotated from one lysimeter to another, working 24 hours, 7 days a week.  Each lysimeter is exposed only for a few minutes so as not to change the natural environment.”

Next week:  Read about the intense precision required to move the soil-filled lysimeters, how problems are prevented, and how the data is used by scientists worldwide.

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

Get more information on applied environmental research in our

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