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 FogLifeand 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.
As forest firesthroughout 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:
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.”
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.
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.
Between dielectric soil moisture sensors with a volume of influence measured in liters and remote sensing systems which measure soil moisture on the scale of kilometers, there is a gap—a gap Dr. Larry Murdoch of Clemson University has been working to fill. In this post, read about the DELTA (Displacement Extensometer for Lysimetric Terrain Analysis), an instrument that measures water content measurements over an area with a 25 m radius.
Dr. Murdoch became interested in how much water content was in the vadose zone (the unsaturated soil above the water table). He wondered if he could use a strain measuring technique to quantify it.
A New Idea:
Dr. Murdoch was a graduate student in structural geology and geomechanics in the mid-1980s, working on the mechanics of hydraulic fractures in soil. He developed techniques for environmental “fracking” to clean up contaminated soil, long before the recent applications by the oil industry that have caused fracking to become a household word. Fracking causes movements in soil, and Dr. Murdoch developed methods for measuring those movements in order to monitor fracture displacement. This led to work on sensitive borehole extensometers that could measure small strains in rock during well testing.
In the course of his hydrology work, Dr. Murdoch became interested in how much water content was in the vadose zone (the unsaturated soil above the water table). He wondered if he could use the strain measuring technique to quantify it. He decided to bore a hole into the vadose zone and insert a simplified extensometer device that could measure the strain as the soil expands and contracts. This would allow him to gauge the weight change of the overburden. Then, because other mass changes are relatively minor compared to the water in the soil, that weight change would enable him to determine water content.
Since soil compresses more than bedrock, Dr. Murdoch developed a method where he inserted two anchors and cylinders that are pressed up against the soil borehole. In the middle of these cylinders is a fiberglass rod held tight by the bottom anchor which is able to move inside the top anchor. The anchors move up and down from the stress on the soil, and this movement is transferred to the rod where it can be measured with a high-resolution displacement transducer.
Diagram of the DELTA (Displacement Extensometer for Lysimetric Terrain Analysis)
Dr. Murdoch’s device is so sensitive that when it is buried 6 m, it will register clear strain signals as his student walks over it. The weight of a person causes around 50 nanometers of displacement at the Clemson Field site, but the instrument itself can resolve displacement approaching 1 nanometer. And the diameter of measurement on the surface is about 4 times the depth. So if you install the system at 7m, you’d be measuring about a 25 m diameter circle on top.
Like almost all other water content techniques, the challenge is removing all other confounding factors that affect the measurement. It has been said that all sensors are temperature sensors first. Not surprisingly, one thing that causes errors in the system is temperature, though Dr. Murdoch’s team has dealt with that by getting the system deep in the soil and putting the electronics near it so the temperature change is small. Barometric pressure also produces cyclical loading of soil mass and requires correction over a range of periods. And, since the calculation of water content requires an estimate of the soil elasticity, changes in soil moisture also may affect the measurement. Considerable work has been done and significant progress has been made in dealing with these and other issues with the extensometer approach.
An advantage of the system is its ability to be buried. In order to plow, for example, all you have to do is pull the sensor up, take off the top plastic casing, and cap it, and the grower can drive a plow over the top.
Strengths:
The amazing thing is that Dr. Murdoch’s system can resolve less than a millimeter of rain water falling on the soil surface, and it can match trends over time. In addition, you can easily calibrate the system by getting your 190-pound student to walk over the top of it and then checking that the compressibility of the soil matches that weight.
Another advantage of the system is its ability to be buried. In order to plow, for example, all you have to do is pull the sensor up, take off the top plastic casing, and cap it, and the grower can drive a plow over the top. Finding the installation can be challenging, so it must be located by precision GPS or survey equipment prior to burial. But, if done correctly, the site can be monitored for long periods of time.
Though not yet a final technology, the Delta extensometer did correlate well with point measurements of water content and shows a lot of promise. The instrument was developed with funding from the National Science Foundation. Colby Thrash, a grad student at Clemson, has done much of the recent work. Dr. Murdoch’s team will publish a paper describing the technique soon in Water Resources Research.
Dr. Colin Campbell, soil physicist, shares why he thinks measuring soil water potential can be more useful than measuring soil water content.
A horsetail plant showing possible signs of guttation where the water potential in the soil overnight is high enough to force water out of the stomates in the leaves.
I know an ecologist who installed an extensive soil water content (VWC) sensor network to study the effect of slope orientation on plant available water. He collected good VWC data, but ultimately he was frustrated because he couldn’t tell how much of the water was available to plants.
He’s not alone in his frustration. Accurate, inexpensive soil moisture sensors have made soil VWC a justifiably popular measurement, but as many people have discovered, a good hammer doesn’t make every soil water problem a nail. I like to compare water potential to temperature because both are considered “intensive” variables that define the intensity of something.
People often try to quantify their own environment, because those measurements define comfort and happiness. Long ago, they discovered they could make an enclosed glass tube, put mercury inside, and infer this intensive variable called temperature from the changes in the mercury’s volume. This was an obvious way to define the comfort level of a human being.
People discovered they could make an enclosed glass tube, put mercury inside, and infer an intensive variable called temperature.
They could have measured the heat content of their surroundings. But they would have discovered that while heat content would be higher in a larger room and lower in a smaller room, you would feel the same comfort level in both rooms. The temperature measurement helps you know whether or not you’d be comfortable without any other variables entering into the equation.
Similar to heat content, water content is an amount. It’s an extensive variable. It changes with size and situation. Consider the following paradoxes:
A soil with fairly low volumetric water content can have plenty of plant-available water and a soil with high water content can have almost none.
Gravity pulls water down through the profile, but water moves up into the soil from a water table.
Two adjacent patches of soil at equilibrium can have significantly different water content.
In these and many other cases, water content data can be confusing because they don’t predict how water moves. Water potential measures the energy state of water and thus explains realities of water movement that otherwise defy intuition. Like temperature, water potential defines the comfort level of a plant. Similar to the room size analogy for temperature, if we know the water potential, we can know whether plants will grow well or be stressed in any environment.
Soil, clay, sand, potting soil, and other media, all hold water differently.
Plants don’t understand the concept of a content in terms of “comfort” because soil, clay, sand, potting soil, and other media, all hold water differently. Imagine a sand with 30% water content. Due to its low surface area, the sand will be too wet for optimal plant growth, threatening a lack of aeration to the roots, and flirting with saturation. Now consider a fine textured clay at that same 30% water content. The clay may appear only moist and be well below optimum “comfort” for a plant due to the surface of the clay binding the water and making it less available to the plant.
Water potential measurements clearly indicate plant available water, and, unlike water content, there is an easy reference scale. We know that plant optimal runs from about -2-5 kPa which is on the very wet side, to about -100 kPa, at the drier end of optimal. Below that plants will be in deficit, and past -1000 kPa they start to suffer. Depending on the plant, water potentials below -1000 to -2000 kPa cause permanent wilting.
So, why would we want to measure water potential? Water content can only tell you how much water you have. If you want to know how fast water can move, you need to measure hydraulic conductivity. If you want to know whether water will move and where it’s going to go, you need water potential.
Learn more
Soil moisture is more than just knowing the amount of water in soil. Learn basic principles you need to know before deciding how to measure it. In this 20-minute webinar, discover:
Water content: what it is, how it’s measured, and why you need it
Water potential: what it is, how it’s different from water content, and why you need it
Whether you should measure water content, water potential, or both
Which sensors measure each type of parameter
Many questions about water availability and movement are best answered by measuring water potential. To find out more, watch any of the virtual seminars below, or visit our new water potential website.
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.
Several years ago I had the chance to work at the USDA ARS Research Watershed in Riesel, Texas. The goal of my research was to look at the effects of land use and landscape position on water infiltration. Within the research watershed there is preserved and maintained native prairie, improved pasture, and conventional tilled areas, which have been in existence for 75 years. Thus we were able to use infiltrometers to study the long-term effects of those different land uses, along with the effect of landscape position within the same soil type.
Texas Infiltrometer setup
My research focused on the Houston Black Soil Series, which is a clay-rich soil with a high shrink-swell capacity. This soil type has key economic importance, as it is present in much of Texas’ USDA prime farmland. To achieve our objectives, we began by mapping soil bulk electrical conductivity using an EM38 device (electromagnetic geo-surveying instrument). The maps we created allowed us to look for areas of variability in water content, depth to parent material, clay content, and salinity. Then we randomly selected three zones within the catinas (full hill slope including summit, back slope, and front slope) and flagged them with GPS points. Our goal was to make infiltration measurements at all of the landscape positions on the slope and compare them to the same landscape positions within each land use type.
We found that the native prairie had the highest infiltration rates because the soil maintained its strong structure and macropores which allowed water to conduct well through the soil. We also found some differences by landscape position that were consistent within the different catinas. As water would run down the catina, erosion would transport soil and organic matter off the shoulder and back slope and deposit it on the foot slopes. Even though they were mapped as the same soil type, the differences in erosion and reduction of organic matter affected the ability of these different positions to transport water.
We chose to customize existing double ring infiltrometers to make these measurements because there wasn’t anything automated on the market. If I was going to conduct my research in a reasonable amount of time, I had to come up with a system where I could run a lot of measurements relatively easily. As a result, we bought three double-ring infiltrometers and modified them with pressure sensors and some larger controlled ports. The resulting setup was huge; the outer ring on each infiltrometer was 60 cm in diameter and the entire instrument was very heavy. We were constantly refilling the instrument water reservoirs. In fact, this setup required so much water that we had to pull a 1,900-liter water tank on a trailer wherever we were taking measurements.
Our goal was to save time by running all three infiltrometers concurrently, but it still took a LONG time. Even though we had automated the instruments, they required a lot of monitoring; sometimes I had to fill our 1,900-liter water tank twice in a day. One measurement at one site took anywhere from 1.5 hours to 3 hours depending on when we reached steady state. We spent so much time out in the field that we were actually caught on film in one of the Google Maps picture flyovers! Even after all this field time, the data analysis was overwhelming, despite a relatively seamless approach to handle it all.
Our huge setup caught on google maps
I often dreamed of making a tool that would be a lot easier for me and others to use. When I joined Decagon (now METER), it gave me an opportunity to do just that. Our design goals were to make an infiltrometer that required less water and simplified the data analysis. We rejected the double ring design in favor of a single ring approach because research has shown that the outer ring doesn’t buffer three-dimensional flow like it’s supposed to. (Swartzendruber D. and T.C. Olson. “Sand-model study of buffer effects in the double-ring infiltrometer” Soil Sci. Soc. Am. Proc. 25 (1961), 5-8)
We also wanted to simplify the analysis of three-dimensional flow. With a constant head control in a single ring, there are equations that you use to correct for it. But you have to guess at things like soil type and structure which leads to inaccuracies. Multi-head analysis has been around for decades. It involves establishing constant water heights (heads) at multiple levels and looking at the difference in the infiltration rates to calculate the sorptivity. Thus, parameters that are normally estimated from a table can actually be measured, and infiltration results will be independent of users.
Still, there can be problems with the multiple head approach. Increasing the water height when infiltrating into a really low conductivity soil may take 1 to 2 hours to drain back to the original height. We didn’t want to make this measurement take longer than necessary, so instead of using additional water, we used air pressure to simulate higher water levels which can be added or removed very quickly.
So, thanks to the instrument hardships I endured in my past efforts to obtain infiltration measurements, we now have an easy-to-use dual-head infiltrometer (now called the SATURO), that can do the analysis of infiltration rates and saturated hydraulic conductivity on the instrument itself (it gives sorptivity and alpha, based on the soil type and structure, and makes the correction onboard). Thus, if a scientist needs a value right away, it’s there. But, if like me, they wanted to dig deeper through the data, all the measured values can still be downloaded for more careful analysis. Together, it’s a simple tool for both scientists and consultants who need to make these measurements. And they won’t get caught on Google Maps like me, because they’ve had to spend their whole life in the field taking measurements.
Below is a video of the dual-head infiltrometer in action.
Get more information on applied environmental research in our
With the recent news coverage of the SMAP (Soil Moisture Active Passive) satellite launch, researchers may wonder: what does remote sensing mean for the future of in situ measurements? We asked two scientists, Drs. Colin Campbell and Chris Lund, for answers to this complex question. Here’s what they had to say:
Image: www.jpl.nasa.gov
What is SMAP?
SMAP is an orbiting earth observatory that estimates soil moisture content in the top 5 cm of soil over the entire earth. The mission is three years long with measurements taken every 2-3 days. This will allow seasonal changes around the world to be observed over time, improving our ability to manage water resources and better parameterize land surface models. SMAP determines the amount of water found between the minerals, rocky material, and organic particles found in soil by measuring the ability of radar to penetrate the soil. The wetter the soil is, the less the radar will penetrate. SMAP has two different sensors on the platform: an L band aperture radar with a resolution of about a kilometer when it’s looking straight down (the pixel size is about 1 km by 1 km), combined with a passive radiometer with about 40 km of resolution. This combination creates a synthetic product that takes advantage of the sensitivity of the radiometer.
What does SMAP mean for in situ soil water content measurement?
It’s all about scale: In some ways, comparing in situ to SMAP measurements is like comparing apples to…well…mountain-sized apples. The two forms of measurement use vastly different scales. In situ soil moisture sensors measure water content at the volume of several liters of soil, maximum. Even the sensor with the largest field of sensitivity, the neutron probe, can only integrate a volleyball-sized volume. On the other hand, SMAP measures at a resolution of 1 km2, which is larger than the size of a quarter section, a large field for many farmers. Global soil moisture maps will allow scientists using SMAP to look at big picture applications like weather, climate and hydrological forecasting, drought, and flooding, while more detailed in situ measurements will tell a farmer when it’s time to water, or help researchers discover exactly why plants are growing in one location versus another. The difference in spatial scale makes the two forms of measurement useful for very different research purposes and applications. However, there are applications where the two measurements can be complementary. Most notably, in situ measurements are often temporally rich while being spatially poor. But, SMAP can be used to scale in situ measurements to areas where in situ measurements are absent. In situ measurements can also be used as a source of validation data for SMAP-derived values for any location where both in situ and SMAP measurements overlap. Thus, there is opportunity for synergy when pairing SMAP and in situ measurements.
Satellite image in Winter.
What can SMAP do that in situ measurement can’t?
Scientists say they’ve seen a relationship between the top 5 cm of soil moisture and some factors related to climate change and weather. Because in situ soil sensors sample across a spatial footprint of a few meters, it can be very difficult to use their data to say anything about processes occurring across broad spatial scales; two liters of soil is not going to tell you anything about weather or flooding. SMAP can help us better understand the interaction between the land surface and atmosphere, improving our understanding of the global water cycle as well as regional and global climate. This will help with forecasting crop yield, pest pressure, and disease…that’s big picture research.
The productivity of a forest also may depend on the general soil moisture measured by SMAP. For instance, if we got an idea of the soil moisture and greenness of a forest, we could tie together the approximate water availability and the resulting biomass accumulation with incoming solar radiation. Better biomass accumulation models could lead to better validation of global carbon cycle models.
SMAP will also be able to detect dry areas across the U.S. and challenges they might present. Surface runoff that leads to flooding could also be predicted as scientists will be able to see where soils reach saturated conditions.
In other applications, people working on global water or energy budgets have to parameterize the land surface in terms of how wet or dry it is. That’s the big advantage of SMAP’s relatively new data sets. Any time you’re running a regional climate model you have to parameterize what the soil moisture is in order to partition surface heat flux into sensible and latent heat flux. If there’s a lot of available water, it’s weighted more toward evaporation and less toward sensible heat flux. In areas where there’s little available water and low evaporation, you get high surface temperatures and sensible heat flux. So SMAP will be important for model parameterization as we haven’t had a good global data set for soil moisture until now.
In situ sensors show how much water is lost from the root zone and what is still left.
What can in situ sensors do that SMAP can’t?
In irrigated agriculture, farmers need to know when and how much to irrigate. In situsensors give them this information by showing how much water was lost from the root zone and what is still left. SMAP is unable to tell you what’s down in the root zone; it only reaches to 5 cm. Additionally, 1 km resolution is larger than most irrigation blocks. These factors mean that it will be difficult to make irrigation decisions from SMAP alone.
Scientists using in situ sensors are concerned with the soil moisture available in a local area because their time resolution is excellent and they have the ability to resolve what’s happening in particular conditions related to crops or natural systems. Natural systems are often heterogeneous, meaning there may be adjacent areas with different types of vegetation including trees, shrubs, and grass. Tree roots may grow deep while grass roots are shallow. Being able to look over all these different areas without averaging them together, as SMAP does, is critical in some applications.
What about geotechnical applications? Literature suggests SMAP output can help predict landslides. It is more likely that it can only see when the soil is generally saturated and generate a warning. But in slopes that are at risk of landslides, in situ monitoring with sensors such as tensiometers to measure positive pore water pressure may be more useful for determining when a slide is imminent.
SMAP, like in situ water content measuring systems, is also limited by the fact that it measures the amount, not the availability, of water. If it measures 23% water content in a certain area, that measurement may not tell us what we want to know. A clay soil at 23% VWC will be close to wilting point while a sand would be above the plant optimal range. SMAP doesn’t measure the energy status of water (water potential), so even if SMAP tells us a field has water content, that water might not be readily available. Water availability must be determined through a pedo-transfer function or moisture release curve appropriate for a specific soil type (It is possible to overlay SMAP data on soil type data to estimate energy state, but this might not be fine enough resolution to be useful).
Complementary Technology
How do SMAP and in situ instruments work together? The key is ground truthing in situ soil moisture measurements with SMAP type satellites and vice versa. Ground-based measurements at specific locations can be matched with satellite information to extrapolate over a field and gain confidence in the small continuous scale alongside the larger infrequent scale. It’s analogous of a video camera recording one plant continuously while a single shot camera snaps whole-field pictures every day. With the SMAP “single-shot” we can say, something changed from time A to time B, but we don’t know what happened in the middle (rain event, etc.). In situ measurements will tell us the details of what happened in between each snapshot. Putting both data sets together and matching trends, we can show correlation and complete the soil moisture picture. Basically, In situ measurements provide temporally rich information about soil moisture from a postage stamp-sized area of earth’s surface (driven by highly localized conditions), whereas SMAP gives us the ability to monitor broad scale spatiotemporal patterns across all of earth’s surface (driven by synoptic conditions).
Sometimes networking with new scientists at conferences and workshops can pay dividends in terms of new ideas. Steve Garrity and I recently attended and taught practicum sessions at the PEPg (Plant Environmental Physiology group) Ecophysiology Workshop. The mission of this workshop was twofold: to invite the world experts on plant physiology measurements to come and lecture, and to invite the manufacturers to teach about instrumentation and provide hands-on training.
Workshop participants check the water potential of soil with a UMS T5 mini-tensiometer.
With three sessions per day using METER instrumentation and only two of us, neither Steve nor I could teach about leaf water potential using the WP4C chilled mirror dew point instrument. So, we asked another scientist who is an expert in plant water relations to teach it for us. Not only did he do a great job of teaching about measuring leaf water potential using a hygrometer, but he also inspired us to take another look at how to make this measurement as we learned about its importance to his research (to learn more about how to do this, watch our virtual seminar).
He’s developed a procedure where you can freeze the leaf and break all of the cells so you are left with the cell water (the symplastic water).
Later in the conference, this same scientist gave a talk about the importance of osmotic potential. He’s developed a procedure where you can freeze the leaf and break all of the cells so you are left with the cell water (the symplastic water). He was able to squeeze that sap out and test it in a thermocouple psychrometer, where he established a relationship between how tolerant plants are for drought and what their osmotic sap water potential (turgor loss point) was. We have made many of those sap measurements but had not used them in this manner. That’s really interesting to us at METER because we were unaware of this relationship, and we have now found another use for osmotic potential measurements in leaves.
We would never have realized this new idea without the help of our colleague. Meeting with other scientists at conferences and talking over ideas can be really important. Have you ever struck gold in terms of coming up with new ideas for research, funding, or inventing new research tools at a conference you’ve attended?
Though collaboration can fuel innovation and increase the relevance and complexity of the scientific questions we study, I’ve noticed it does have its ups and downs. The highs and lows we’ve run into on our research projects may help others avoid some of the pitfalls we experienced as many diverse groups tried to learn how to work together.
Researchers discussing science at the Lytle Ranch Preserve, a remarkable desert laboratory located at the convergence of the Great Basin, Colorado Plateau, and Mojave Desert biogeographical regions.
There can be bumps in the road when collaborating with companies who want to test their product. Being at the forefront of innovation means that untested sensors may require patience as you work out all the bugs together. But from my perspective, this is part of the fun. If we are late adopters of technology, we wouldn’t get to have a say in creating the sensors that will best fit our projects as scientists.
Collaborating scientists can also sometimes run into problems in terms of the stress of setting up an experiment in the time frame that is best for everyone. During our experiment on the Wasatch Plateau, we had six weeks to get together soil moisture and water potential sensors, but our new GS3water content, temperature, and EC sensors had never been outside of the lab. In addition, we planned to use an NDVI sensor concept that came out of a workshop idea my father Gaylon had. We’d made ONE, and it seemed to work, but that is a long way from the 20 we needed for a long-term experiment in a remote location at 3000 meters elevation. In the end, it all worked out, but not without several late nights and a bit of luck. I remember students holding jackets over me to protect me from the rain as I raced to get the last sensor working. Then we shut the laptop and ran down the hill, trying to beat a huge thunderstorm that started to pelt the area.
Desert-FMP Researchers at the Lytle Ranch Preserve
Other challenges of scientific collaboration present organizational hardships. One of the interesting things about the interdisciplinary science in the Desert FMP project is the complexity of the logistics, and maybe that’s a reason why some people don’t do interdisciplinary projects. We are finding in order to get good data on the effects of small mammals and plants you need to coordinate when you are sampling small mammals and when you’re sampling plants. Communicating between four different labs is complicated. Each of the rainout shelters we use cover an area of approximately 1.5 m2 . That’s not a lot of space when we have two people interested in soil processes and two people interested in plants who all need to know what’s going on underneath the shelter. Deciding who gets to take a destructive sample and who can only make measurements that don’t change the system is really hard. The interesting part of the project where we’re making connections between processes has required a lot of coordination, collaboration, and forward-thinking.
In spite of the headaches, my colleague and I continue to think of ways we can help each other in our research. Maybe we’re gluttons for punishment, but I think the benefits far outweigh the trouble we’ve had. For instance, in the above-mentioned Desert FMP project we’ve been able to discover that small mammals are influential in rangeland fire recovery (read about it here). We only discovered that piece of the puzzle because scientists from differing disciplines are working together. In our Wasatch Plateau project, my scientist colleague said it was extremely helpful for him to be working with an instrumentation expert who could help him with setup and technical issues. Also, we’ve been able to secure some significant grants in our Cook Farm Project (you can read about it in an upcoming post) and answer some important questions that wouldn’t have occurred to either one of us, if we hadn’t been working together. In addition, solving problems that have cropped up in our projects has spurred us on to a new idea for analyzing enormous streams of data in near-real time. (read about it here).
The Desert FMP project originated from a discussion between pretty divergent scientists: Rick Gill, a BYU ecologist, another scientist who works on soil microbes, a plant physiologist, and a mammalogist who researches small mammals.
Tree fire in Rush Valley
In an interview Rick said, “We started talking one day about the transformations that have occurred in the arid West over the past 100 years. One of the things we are really interested in is fire. How do ecosystems recover after fire? What’s the role of water in rangeland recovery? And the unique piece of this is: what’s the role of small mammals in this process? We may never have thought of that question, or the complexity of researching how all of our questions work together in a system, if scientists from different disciplines hadn’t decided to collaborate.”
Rush Valley research site. Five replications with four treatments: burned/unburned and small mammal/no small mammal. What’s interesting for us is that you can see that in the burned plots (the light brown) there are strong differences in the amount of the bright green plant—halogeton—that was present and it is systematically associated with the presence of small mammals. Here is the logic: In the spring, the presence of small mammals suppressed the cheatgrass and to some extent halogeton; in the absence of halogeton, cheatgrass ran wild. The cheatgrass transpired away all of the water and the halogeton that had germinated all died before it could flower.
As the experiment unfolds it is becoming clear that small mammals play a larger role in ecosystem recovery from fire than originally thought. The scientists have used their observations to hypothesize that small mammals eat the seeds and seedlings of two invasive species. This ends up setting the vegetation along a very different trajectory than when small mammals are absent following fire. Rick says, “We have discovered this complex but interesting interaction between water, fire, and small mammals. The first year after the fire, a really nasty range forb moved in called halogeton, which is toxic to livestock. Halogeton also accumulates salts in the upper soil profile that will cause failure in native plant germination. Cheatgrass has also moved in which makes the area more prone to fire as it connects the sagebrush plants with flammable material. But what’s interesting is in treatments where mammals were present, the densities of both halogeton and cheatgrass were much lower than where small mammals were absent.
Plot water potential comparison using matric potential sensors between Mammal (blue) and no mammal (red) over time. With no mammals to control cheatgrass, it depleted soil water availability below no mammal treatment and consequently halogeten was not able to grow.
“The other really important thing is that cheatgrass and halogeton have different growth patterns. Cheatgrass germinates in the Fall. It reaches peak biomass early in the growing season and then dies off leaving a blanket of dead, highly flammable vegetation. Halogeton germinates early in the growing season and remains relatively small until early Autumn when it bolts. These are things that will be really easy to pick up using NDVI sensors, which are sensitive to the amount of green vegetation within the field of view of the sensor. We are also using a system that we’ve designed to manipulate precipitation input. This will enable us to connect water availability to the success of two invasive plants that have negative impacts on rangelands. And with these same treatments we’re going to be able to tease out when in the year and to what extent small mammals are influencing the ecosystem by eating the seeds or the plant and at what stage.”
“Until I saw it in the field, the question of mammals being influential in rangeland fire recovery had never occurred to me. We only discovered that piece of the puzzle because scientists from differing disciplines are working together.”
Below are two virtual tours of the site:
Get more information on applied environmental research in our
We chose to collaborate with Brigham Young University in an experiment on the Wasatch Plateau in 2009 because a scientist friend of ours had been working in that area the previous five years, and he noticed there were big grazing responses. The plants growing in the long-term grazed areas were all drought tolerant, while ungrazed plots had plants that were often found only in wetter areas. The only difference was the fence that kept sheep on one side and not on the other. The big question was: how does water influence plants in this ecosystem that we understand relatively well? The story had always been the influence of grazers, when in fact, maybe the indirect consequence of grazing was mediated by water.
The Wasatch Plateau above Ephraim Canyon, UT, USA.
METER donated some sensors in order to set up an experiment where we changed the amount of water in various plots of land. We had rain exclusion plots, and we had treatments where we collected all incoming rainfall and reapplied it either once a week or every three weeks. This allowed us to say to what extent this system was controlled by water during the growing season. To do this, we took measurements with our prototype NDVI Spectral Reflectance Sensor to measure canopy greenness. We also used our prototype volumetric water content sensors to measure soil moisture (this was a few years ago and the sensors were prototypes at the time). Using these sensors, we found that water is critical in a system people have dismissed as being climate-controlled because it’s at the top of a mountain.
A very early prototype of a NDVI sensor measuring canopy greenness in experimental plots on the Wasatch Plateau.
It turns out the amount and timing of precipitation makes a big difference. We were able to directly connect plant survival, not just to the grazing treatment, but to the actual amount of water that was in the soil. Also, using continuous NDVI data, we were able to look closely at the role of grazing on plant canopies. When we looked at our NDVI data, we were able to see a seasonal signal, not just a single snapshot sample in time. So by having the richer data from the data loggers, we obtained a more nuanced understanding of the impact of land use on these important ecological processes.
One of the mistakes we made was failure to include redundancy in the system. We only had two replicates, so when one of them went down we ended up having just one little case study. However, that mistake gave us new ideas on how to set up a better system using the right sensors for the job, and it generated a new idea on how to get real-time analysis of data. In our new Desert FMP project, we have a much better-replicated system where more is invested in the number of sensors that we’re putting out. Each treatment combination will have five to ten water potential sensors. We are also developing a system where we can analyze data in real-time, so this time we will know when a sensor goes out if a student accidentally kicks it.
For more details on the Wasatch Plateau Experiment, watch for our published paper that we’ll link to when it comes out.
