Whether researchers measure soil hydraulic properties in the lab or in the field, they’re only getting part of the picture. Laboratory systems are highly accurate due to controlled conditions, but lab measurements don’t take into account site variability such as roots, cracks, or wormholes that might affect soil hydrology. In addition, when researchers take a sample from the field to the lab, they often compress soil macropores during the sampling process, altering the hydraulic properties of the soil.
Roots, cracks, and wormholes all affect soil hydrology
Field experiments help researchers understand variability and real time conditions, but they have the opposite set of problems. The field is an uncontrolled system. Water moves through the soil profile by evaporation, plant uptake, capillary rise, or deep drainage, requiring many measurements at different depths and locations. Field researchers also have to deal with the unpredictability of the weather. Precipitation may cause a field drydown experiment to take an entire summer, whereas in the lab it takes only a week.
The big picture—supersized
Researchers who use both lab and field techniques while understanding each method’s strengths and limitations can exponentially increase their understanding of what’s happening in the soil profile. For example, in the laboratory, a researcher might use the PARIO soil texture analyzer to obtain accurate soil texture data, including a complete particle size distribution. They could then combine those data with a HYPROP-generated soil moisture release curve to understand the hydraulic properties of that soil type. If that researcher then adds high-quality field data in order to understand real world field conditions, then suddenly they’re seeing the larger picture.
Table 1. Lab and field instrument strengths and limitations
Below is an exploration of lab versus field instrumentation and how researchers can combine these instruments for an increased understanding of their soil profile. Click the links for more in-depth information about each topic.
Particle size distribution and why it matters
Soil type and particle size analysis are the first window into the soil and its unique characteristics. Every researcher should identify the type of soil that they’re working with in order to benchmark their data.
Particle size analysis defines the percentage of coarse to fine material that makes up a soil
If researchers don’t understand their soil type, they can’t make assumptions about the state of soil water based on water content (i.e., if they work with plants, they won’t be able to predict whether there will be plant available water). In addition, differing soil types in the soil’s horizons may influence a researcher’s measurement selection, sensor choice, and sensor placement.
Different readings in soil moisture sensors are caused by spatial variation in water content (see part 1). These readings provide researchers valuable information about soil texture, watering patterns, and water use. This week, learn two more strategies to keep in mind when trying to understand the varying patterns of soil moisture at your research or irrigation site.
In some crop studies, it may be important to account for horizontal variation.
Strategy #2: Crop Studies—Representing Variation in a Homogeneous Environment
In some research projects, it will be important to account for horizontal variation. How variable is the water content across a field? We did an experiment in which we set out a transect across a field of bare, tilled soil. Using a METER EC-5 soil moisture probe connected to a Procheck meter, we sampled water content at one meter intervals over a 58 meter distance. The individual readings are shown in Figure 1.
Figure 1. You can determine how many samples are necessary to characterize a homogeneous area in about an hour using and EC-5 soil moisture sensor and a ProCheck.
In this data set the samples are not spatially correlated. The variation is apparent. The mean water content of the data set is 0.198 m3m-3. The standard deviation is 0.023 m3m-3. The coefficient of variation is 12%. Using some simple geostatistics, we determined that three carefully placed sites would adequately represent the variation present in this very homogeneous environment. Of course, in some environments, samples will not be independent. If a semivariogram indicates that some underlying spatial factor influences soil moisture variability, you will have to consider that in your experimental design.
By taking into account the major relevant sources of soil moisture variation, you can plan enough sampling locations to draw conclusions from your data.
On a forested hillside, horizontal variation in soil moisture will obviously be significant. Determining how many sensors to use and where to place them is not at all trivial. Stratified sampling—systematically sampling from more uniform subgroups of a heterogeneous population—may be a better way to deal with this kind of variety. The researcher classifies the site into strata (eg., forested canopy, brush, hillside, valley) and evaluates the number of samples needed to statistically represent the variation present within each stratum.
Many people allow for the variation in soil moisture values that come from slope, orientation, vegetation, and canopy cover. Some fail to consider the important soil-level variations that come from soil type and density. By taking into account the major relevant sources of soil moisture variation, you can plan enough sampling locations to draw reasonable conclusions from your data. Choose too few locations, and you run the risk of missing the patterns that will lead to higher level understanding. Choose too many, and not only will you be unable to afford your experiment, you may miss the patterns altogether as your experiment overflows with random abundance.
Sometimes researchers want to compare dissimilar sites.
Comparing Data from Different Sites or Strata
Comparing absolute water content numbers can give confusing results. Both measurements are volumetric water content, but 35% here vs. 15% there actually tells us very little. Was the site in sand or clay, or something in between? If conditions at the two sites are virtually identical, the comparison may make some sense. But often, researchers want to compare dissimilar sites.
Figure 2. Changes in VWC with depth (convention: negative values indicate depths below soil surface) for the same time period at Site 1.
Water potential measurements determined by converting absolute volumetric water content to soil water potential using a moisture characteristic curve specific to each soil type can be used to compare results across sites. Comparing relative values—quantities of water used in centimeters for example—can also be both useful and valid.
Figure 3 below illustrates an experiment we performed in a dryland field where water content measurements were made over a growing season at 30, 60, 90, 120, and 150 cm below a wheat crop. The graph of soil moisture data shows how water is taken up from successively deeper layers. By subtracting one profile from another and summing over the layers where change occurs (for instance, in Figure 2 above, subtract the far left line from the far right line to see how much water was used from May 10th to August 21st), you can determine the amount of water used by the plants over a particular period. If similar data were taken at different sites or in different strata, these relative values, in terms of quantified water use, could form the basis of solid comparison studies.
Figure 3. Soil water content in winter wheat measured at 30 cm increments
Read more about accurate soil moisture: Can you sample the profile without a profile probe? Find out.
How Do you Know You’re Getting Accurate Soil Moisture?
Researchers and irrigators may wonder if their soil moisture sensors are accurate because probes at different locations in the same field have different water content readings. Different readings in soil moisture sensors are caused by spatial variation in water content. These readings provide researchers valuable information about soil texture, watering patterns, and water use. Here are some ideas and strategies to keep in mind when trying to understand the varying patterns of soil moisture at your research or irrigation site. Click the links for more in-depth information about accurate soil moisture.
One irrigator noticed a few sensors indicating low water content after a heavy rain that had uniformly wetted his vineyard.
Horizontal vs. Vertical Variation
It’s helpful to distinguish variation in the vertical from variation in the horizontal. Most people expect strong vertical variation due to wetting and drying patterns, soil horizonation, and compaction. Water content can vary drastically over distances of only a few centimeters, especially near the soil surface. Horizontal variation is typically less pronounced-in a bare or uniformly planted field, and at a given depth, it might be quite small. But surprisingly large variations can exist, indicating isolated patches of sand or clay or differences in topography. One irrigator noticed a few sensors indicating low water content after a heavy rain that had uniformly wetted his vineyard. Knowing that sand has a low field capacity water content, he surmised (correctly) that he had found the sandy areas in the vineyard.
Soil moisture sensors sometimes measure unexpected things.
Because properly installed dielectric soil moisture sensors lie in undisturbed (and therefore unanalyzed) soil, they sometimes measure unexpected things. One researcher buried a probe in what appeared to be a very dry location and was startled to measure 25 to 30% volumetric water content. Those readings made the soil appear saturated, but obviously it wasn’t. She dug down to the sensor and found a pocket of clay. As she discovered, it is impossible to get much information from an absolute water content measurement without knowing what type of soil the sensor is in.
Since we expect variation, how do we account for it? How many probes are needed to adequately characterize the water content in an application or experiment? There is no simple answer to this question. The answer will be affected by your site, your goals, and how you plan to analyze your data. Here are some things you might consider as you plan.
If a field will be irrigated as a unit, it should be monitored as a unit at one representative spot.
Strategy #1: Irrigation—Use Soil Moisture as an Indicator
What information do you have when you know a field’s volumetric water content? That number independently tells an irrigator very little. Soil moisture can be used like a gauge to show when a field is full and when it needs to be refilled, but the “full” and “empty” are only meaningful in context.
The goals of irrigation are to keep root zone water within prescribed limits and to minimize deep drainage. Understanding and monitoring the vertical variation lets you correlate a real time graph of water use data with above-ground field conditions and plant water needs. It makes sense to place probes both within and below the root zone.
By contrast, measuring horizontal variation—placing sensors at different spots in the field—is not very helpful. If a field will be irrigated as a unit, it should be monitored as a unit at one representative spot. Because there’s no way to adjust water application in specific spots, there’s no benefit to quantifying spatial variation in the horizontal. Like a float in a gas tank, a set of soil moisture sensors in the right spot will adequately represent the changing soil moisture condition of the whole field.
We recommend a single probe location in each irrigation zone with a minimum of one probe in the root zone and one probe below it. Additional probes at that site, within and below the root zone, will increase the reliability of the information for the irrigation manager, at minimal additional cost.
In two weeks: Learn two more techniques researchers use in crop studies and ecology studies to account for variability in order to obtain an accurate soil moisture picture.
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This week, guest author Dr. Michael Forster, of Edaphic Scientific Pty Ltd & The University of Queensland, writes about new research using irrigation curves as a novel technique for irrigation scheduling.
Growers do not have the time or resources to investigate optimal hydration for their crop. Thus, a new, rapid assessment is needed.
Measuring the hydration level of plants is a significant challenge for growers. Hydration is directly quantified via plant water potential or indirectly inferred via soil water potential. However, there is no universal point of dehydration with species and crop varieties showing varying tolerance to dryness. What is tolerable to one plant can be detrimental to another. Therefore, growers will benefit from any simple and rapid technique that can determine the dehydration point of their crop.
New research by scientists at Edaphic Scientific, an Australian-based scientific instrumentation company, and the University of Queensland, Australia, has found a technique that can simply and rapidly determine when a plant requires irrigation. The technique builds on the strong correlation between transpiration and plant water potential that is found across all plant species. However, new research applied this knowledge into a technique that is simple, rapid, and cost-effective, for growers to implement.
Current textbook knowledge of plant dehydration
The classic textbook values of plant hydration are field capacity and permanent wilting point, defined as -33 kPa (1/3 Bar) and -1500 kPa (15 Bar) respectively. It is widely recognized that there are considerable limitations with these general values. For example, the dehydration point for many crops is significantly less than 15 Bar.
Furthermore, values are only available for a limited number of widely planted crops. New crop varieties are constantly developed, and these may have varying dehydration points. There are also many crops that have no, or limited, research into their optimal hydration level. Lastly, textbook values are generated following years of intensive scientific research. Growers do not have the time, or resources, to completely investigate optimal hydration for their crop. Therefore, a new technique that provides a rapid assessment is required.
How transpiration varies with water potential
There is a strong correlation between transpiration and plant water potential: as plant water potential becomes more negative, transpiration decreases. Some species are sensitive and show a rapid decrease in transpiration; other species exhibit a slower decrease.
Plant physiologist refer to P50 as a value that clearly defines a species’ tolerance to dehydration. One definition of P50 is the plant water potential value at which transpiration is 50% of its maximum rate. P50 is also defined as the point at which hydraulic conductance is 50% of its maximum rate. Klein (2014) summarized the relationship between transpiration and plant water potential for 70 plant species (Figure 1). Klein’s research found that there is not a single P50 for all species, rather there is a broad spectrum of P50 values (Figure 1).
Figure 1. The relationship between transpiration (stomatal conductance) and leaf water potential for 70 plant species. The dashed red lines indicate the P80 and P50 values. The irrigation refill point can be determined where the dashed red lines intersect with the data on the graph. Image has been adapted from Klein (2014), Figure 1b.
Taking advantage of P50
The strong, and universal, relationship between transpiration and water potential is vital information for growers. A transpiration versus water potential relationship can be quickly, and easily, established by any grower for their specific crop. However, as growers need to maintain optimum plant hydration levels for growth and yield, the P50 value should not be used as this is too dry. Rather, research has shown a more appropriate value is possibly the P80 value. That is, the water potential value at the point that transpiration is 80% of its maximum.
Irrigation Curves – a rapid assessment of plant hydration
Research by Edaphic Scientific and University of Queensland has established a technique that can rapidly determine the P80 value for plants. This is called an “Irrigation Curve” which is the relationship between transpiration and hydration that indicates an optimal hydration point for a specific species or variety.
Once P80 is known, this becomes the set point at which plant hydration should not go beyond. For example, a P80 for leaf water potential may be -250 kPa. Therefore, when a plant approaches, or reaches, -250 kPa, then irrigation should commence.
P80 is also strongly correlated with soil water potential and, even, soil volumetric water content. Soil water potential and/or content sensors are affordable, easy to install and maintain, and can connect to automated irrigation systems. Therefore, establishing an Irrigation Curve with soil hydration levels, rather than plant water potential, may be more practical for growers.
Example irrigation curves
Irrigation curves were created for a citrus (Citrus sinensis) and macadamia (Macadamia integrifolia). Approximately 1.5m tall saplings were grown in pots with a potting mixture substrate. Transpiration was measured daily, between 11am and 12pm, with a SC-1 Leaf Porometer. Soil water potential was measured by combining data from an MPS-6 Matric Potential Sensor and WP4 Dewpoint Potentiometer. Soil water content was measured with a GS3 Water Content, Temperature and EC Sensor. Data from the GS3 and MPS-6 sensors were recorded continuously at 15-minute intervals on an Em50 Data Logger. When transpiration was measured, soil water content and potential were noted. At the start of the measurement period, plants were watered beyond field capacity. No further irrigation was applied, and the plants were left to reach wilting point over subsequent days.
Figure 2. Irrigation Curves for citrus and macadamia based on soil water potential measurements. The dashed red line indicates P80 value for citrus (-386 kPa) and macadamia (-58 kPa).
Figure 2 displays the soil water potential Irrigation Curves, with a fitted regression line, for citrus and macadamia. The P80 values are highlighted in Figure 2 by a dashed red line. P80 was -386 kPa and -58 kPa for citrus and macadamia, respectively. Figure 3 shows the results for the soil water content Irrigation Curves where P80 was 13.2 % and 21.7 % for citrus and macadamia, respectively.
Figure 3. Irrigation Curves for citrus and macadamia based on soil volumetric water content measurements. The dashed red line indicates P80 value for citrus (13.2 %) and macadamia (21.7 %).
From these results, a grower should consider maintaining soil moisture (i.e. hydration) above these values as they can be considered the refill points for irrigation scheduling.
Further research is required
Preliminary research has shown that an Irrigation Curve can be successfully established for any plant species with soil water content and water potential sensors. Ongoing research is currently determining the variability of generating an Irrigation Curve with soil water potential or content. Other ongoing research includes determining the effect of using a P80 value on growth and yield versus other methods of establishing a refill point. At this stage, it is unclear whether there is a single P80 value for the entire growing season, or whether P80 shifts depending on growth or fruiting stage. Further research is also required to determine how P80 affects plants during extreme weather events such as heatwaves. Other ideas are also being investigated.
For more information on Irrigation Curves, or to become involved, please contact Dr. Michael Forster: email@example.com
Klein, T. (2014). The variability of stomatal sensitivity to leaf water potential across tree species indicates a continuum between isohydric and anisohydric behaviours. Functional Ecology, 28, 1313-1320. doi: 10.1111/1365-2435.12289
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Soil moisture release curves have always had two weak areas: a span of limited data between 0 and -100 kPa and a gap around field capacity where no instrument could make accurate measurements.
Using HYPROP with the redesigned WP4C, a skilled experimenter can now make complete high resolution moisture release curves.
Between 0 and -100 kPa, soil loses half or more of its water content. If you use pressure plates to create data points for this section of a soil moisture release curve, the curve will be based on only five data points.
And then there’s the gap. The lowest tensiometer readings cut out at -0.85 MPa, while historically the highest WP4 water potential meter range barely reached -1 MPa. That left a hole in the curve right in the middle of plant-available range.
The Global Learning and Observations to Benefit the Environment (GLOBE) Program is an international science and education program that provides students and the public worldwide with the opportunity to participate in data collection and the scientific process.
GLOBE has a huge impact in schools around the world.
Its mission is to promote the teaching and learning of science, enhance community environmental literacy and stewardship, and provide research quality environmental observations. The GLOBE program works closely with agencies such as NASA to do projects like validation of SMAP data and the Urban Heat Island/Surface Temperature Student Research Campaign. The figure below shows the impact GLOBE is having in schools worldwide.
Dixon Butler, former GLOBE Chief Scientist, is excited about the recent African project GLOBE is now participating in called the TAHMO project. He says, “Right now, in Kenya and Nigeria, GLOBE schools are putting in over 100 new mini-weather stations to collect weather data, and all that usable data will flow into the GLOBE database.”
Participating in real science at a young age gets youth more ready to be logical, reasoning adults.
Why Use Kids to Collect Data?
Dixon says kids do a pretty good job taking research quality environmental measurements. Working with agencies like NASA gets them excited about science, and participating in real science at a young age gets them more ready to be logical, reasoning adults. He explains, “The 21st century requires a scientifically literate citizenry equipped to make well-reasoned choices about the complex and rapidly changing world. The path to acquiring this type of literacy goes beyond memorizing scientific facts and conducting previously documented laboratory experiments to acquiring scientific habits of mind through doing hands-on, observational science.”
Dixon says when GLOBE started, the plan was to have the kids measure temperature. But one science teacher, Barry Rock, who had third grade students using Landsat images to do ozone damage observations, called the White House and said, “Kids can do a lot more than measure temperature.” He gave a presentation at the White House where he showed a video of two third grade girls looking at Landsat imagery. They were discussing their tree data, and at one point, one said to the other, ‘That’s in the visible. Let’s look at it in the false color infrared.’ At that point, Barry became the first chief scientist of GLOBE, and he helped set up the science and the protocols that got the program started.
GLOBE uses online and in-person training and protocols to be sure the students’ data is research quality.
Can GLOBE Data be Used by Scientists?
GLOBE uses online and in-person training and protocols to be sure the students’ data is research quality. Dixon explains, “There was a concern that these data be credible, so the idea was to create an intellectual chain of custody where scientists would write the protocols in partnership with an educator so they would be written in an educationally appropriate way. Then the teachers would be trained on those protocols. The whole purpose is to be sure scientists have confidence that the data being collected by GLOBE is useable in research.”
Today GLOBE puts out a Teacher’s’ Guide and the protocols have increased from 17 to 56. The soil area went from just a temperature and moisture measurement to a full characterization. Dixon says, “We’ve been trying to improve it ever since, and I think we’re getting pretty good at it.”
GLOBE students were the only ones going around looking up at the sky doing visual categorization of clouds and counting contrails. It was just no longer being done, except by these students.
What About the Skeptics?
If you ask Dixon how he deals with skeptics of the data collected by the kids, he says, “I tell them to take a scientific approach. Check out the data, and see if they’re good. One year, a GLOBE investigator found a systematic error In U-tube maximum/minimum thermometers mounted vertically, which had been in use for over a century, that no one else found. The GLOBE data were good enough to look at and find the problem. There are things the data are good for and things they’re not good for. Initially, we wanted these data to be used by scientists in the literature, and there have been close to a dozen papers, but I would argue that GLOBE hasn’t yet gotten to the critical mass of data that would make that easier.”
GLOBE did have enough cloud data, however, to be used in an important analysis of geostationary cloud data where the scientist compared GLOBE student data with satellite data Dixon adds, “GLOBE students were the only ones going around looking up at the sky doing visual categorization of clouds and counting contrails. It was just no longer being done, except by GlOBE students. Now GLOBE has developed the GLOBE Observer app that let’s everyone take and report cloud observations.”
Young minds need to experience the scientific approach of developing hypotheses, taking careful, reproducible measurements, and reasoning with data.
What’s the Future of GLOBE?
Dixon says GLOBE’s goal is to raise the next generation of intelligent constituents in the body politic. He says, “I thought about this a lot when I worked for the US Congress. In addition to working with GLOBE, I now have a non-profit grant-making organization called YLACES with the objective of helping kids to learn science by doing science. Young minds need to experience the scientific approach of developing hypotheses, taking careful, reproducible measurements, and reasoning with data. Inquiries should begin early and grow in quality and sophistication as learners progress in literacy, numeracy, and understanding scientific concepts. In addition to fostering critical thinking skills, active engagement in scientific research at an early age also builds skills in mathematics and communications. These kids will grow up knowing how to think scientifically. They’ll ask better questions, and they’ll be harder to fool. I think that’s what the world needs, and I see the environment and science as the easiest path to get there.”
Learn more about GLOBE and its database here and about YLACES at www.ylaces.org.
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The Trans African Hydro and Meteorological Observatory (TAHMO) project expects to put 20,000 microenvironment monitors over Africa in order to understand the weather patterns which affect that continent, its water, and its agriculture. In the conclusion of our 3 part series, we interview Dr. John Selker about his thoughts on the project.
The economics of weather data value may be going up because we’re reaching a cusp in terms of humanity’s consumption of food.
In your TEDx talk you estimate that US weather stations directly bring U.S. consumers 31 billion dollars in value per year. Can Africa see that same kind of return?
Even more. The economics of weather data value may be going up because we’re reaching a cusp in terms of humanity’s consumption of food. Africa, one could argue, is the breadbasket for this coming century. Thus, the value of information about where we could grow what food could be astronomical. It’s very difficult to estimate. One application of weather data is crop insurance. Right now, crop insurance is taking off across Africa. The company we’re working with has 180,000 clients just in Kenya. When we talked about 31 billion dollars in the U.S., that is the value citizens report, but you need to add to that protection against floods, increased food production, water supply management, crop insurance and a myriad of other basic uses for weather data. In Africa, the value of this type of protection alone pays for over 1,000 times the cost of the weather stations.
Another application for weather data is that in Africa, the valuation of land itself is uncertain. So if, because of weather station data, we find that a particular microclimate is highly valuable, suddenly land goes from having essentially no value to becoming worth thousands of dollars per acre. It’s really difficult to estimate the impact the data will have, but it could very well end up being worth trillions of dollars. We have seen this pattern take place in central Chile, where land went from about $200/hectare in 1998 to over $3,000/ha now due to the understanding that it was exceptionally suited to growing pine trees, which represented a change in land value exceeding $3 billion.
Does the effect of these weather stations go beyond Africa?
There’s limited water falling on the earth, and if you can’t use weather data to invest in the right seeds, the right fertilizer, and plant at the right time in the right place, you’re not getting the benefit you should from having tilled the soil. So for Africa the opportunity to improve yields with these new data is phenomenal.
In terms of the world, the global market for calories is now here, so if we can generate more food production in Africa, that’s going to affect the price and availability of food around the world. The world is one food community at this point, so an entire continent having inefficient production and ineffective structures costs us all.
If we can generate more food production in Africa, that’s going to affect the price and availability of food around the world.
You’re collecting data from Africa. Is it time to celebrate yet?
I think this is going to be one of those projects where we are always chilling the champagne and never quite drinking it. It is such a huge scope trying to work across a continent. So I would say we’ve got some stations all over Africa, we’re learning a lot, and we’ve got collaborators who are excited. We have reason to feel optimistic. It will be another five years before I’ll believe that we have a datastream that is monumental. Right now we’re still getting the groundwork taken care of. By September of this year we expect to have five hundred of stations in place, and then two years from now, over two thousand. This will be a level of observation that will transform the understanding of African weather and climate.
This is a project of hundreds of people across the world putting their hands and hearts in to make this possible.
How do you deal with the long wait for results?
In science there is that sense you get when you want to know something, and you can see how to get there. You have a theory, and you want to prove it. It kind of captures your imagination. It’s a combination of curiosity and the potential to actually see something happen in the world: to go from a place where you didn’t know what was going on to a place where you do know what’s going on. I think about Linus Pauling, who made the early discoveries about the double helix. He had in his pocket the X-ray crystallography data to show that the protein of life was in helical form, and he said, “In my pocket, I have what’s going to change the world.” When we realized the feasibility of TAHMO, we felt much the same way.”
Sometimes in your mind, you can see that path: how you might change the world. It may never be as dramatic as what Pauling did, but even a small contribution has that same excitement of wanting to be someone who added to the conversation, who added to our ability to live more gracefully in the world. It’s that feeling that carries you along, because in most of these projects you have an idea, and then ten years later you say, “why was it that hard?”
Things are usually much harder than your original conception, and that energy and curiosity really helps you through some of the low points in your projects. So, curiosity has a huge influence on scientific progress. Changing the world is always difficult, but the excitement, curiosity, and working with people, it all fits together to help us draw through the tough slogs. In TAHMO, I cannot count the number of people who have urged us to keep the effort moving forward and given a lift just when we needed it most. This is a project of hundreds of people across the world putting their hands and hearts in to make this possible. Having these TAHMO supporters is an awesome responsibility and concrete proof of the generosity and optimism of the human spirit.
Weather data improve the lives of many people. But, there are still parts of the globe, such as Africa, where weather monitoring doesn’t exist (see part 1). John Selker and his partners intend to remedy the problem through the Trans African Hydro Meteorological Observatory (TAHMO). Below are some challenges they face.
TAHMO aims to deploy 20,000 weather stations across the continent of Africa in order to fill a hole that exists in global climate data.
Big Data, Big Governments, and Big Unknowns
Going from an absence of data to the goal of 20,000 weather stations offers hope for positive changes. However, Selker is still cautious. “Unintended consequences are richly expressed in the history of Africa, and we worry about that a lot. It’s an interesting socio-technical problem.” This is why Selker and others at TAHMO are asking how they can bring this technology to Africa in a way that fits with their cultures, independence, and the autonomy they want to maintain.
TAHMO works with the government in each country stations are deployed in; negotiating agreements and making sure the desires of each recipient country are met. Even with agreements in place, the officials in each country will do what is in the best interest of the people: a gamble in countries wherecorruption is a factor which must be addressed. Selker illustrates this point by recalling an instance in 1985 when he witnessed a corrupt government official take an African farmer’s land because the value had increased due to a farm-scale water development project.
Most TAHMO weather stations are hosted and maintained by a local school, making it available as an education tool for teachers to use to teach about climate and weather. Data from TAHMO are freely available to the government in the country where the weather station is hosted, researchers who directly request data, and to the school hosting and maintaining the weather station. Commercial organizations will be able to purchase the data, and the profits will be used to maintain and expand the infrastructure of TAHMO.
Selker says it’s all about collaboration.
Terrorism, Data, and Open Doors
“When I wanted to go out and put in weather stations, my wife said, ‘No, you will not go to Chad.’ … because it is Boko Haram central,” Selker says.
The Boko Haram— a terrorist organization that has pledged allegiance to ISIS— creates an uncommon hurdle. Currently the Boko Haram is most active in Nigeria, but has made attacks in Chad, Cameroon, and Niger.
Selker also mentioned similar issues with ISIS, “When ISIS came through Mali, the first thing they did is destroy all the weather stations. So they have no weather data right now in Mali.” Acknowledging the need for security, he adds, “we’re completing the installation of eight stations [in Mali] in April.”
“We have good contacts [in Nigeria] and they’re working hard to get permission to put up stations right now in that area. We’ve shipped 15 stations which are ready to install. With these areas we can’t go visit, it’s all about collaboration. It’s about partners and people you know. We have a partnership with a tremendous group of Africans who are really the leading edge of this whole thing.”
Most TAHMO weather stations are hosted and maintained by a local school.
A Hopeful Future
Despite the challenges of getting this large-scale research network off the ground, Selker and his group remain hopeful. About his weather data he says, “It’s not glamorous stuff, you won’t see it on the cover of magazines, but these are the underpinnings of a successful society.”
Selker optimistically adds, “We are in a time of incredible opportunity.”
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.
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 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.”
Tree mortality factors were only found at the precise altitude where fog accumulated.
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 sulphur 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.
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In Germany, scientists are measuring the effects of tomorrow’s climate change with a vast network of 144 large lysimeters.
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.
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.”
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.
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