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Posts tagged ‘Water’

New Weather Station Technology in Africa-3

The Trans African Hydro and Meteorological Observatory (TAHMO) project expects to put 20,000 weather stations 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.

TAHMO

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.

TAHMO

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.

TAHMO

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.

Learn how you can help TAHMO.

See weather sensor performance data for the ATMOS 41 weather station. 

Explore which weather station is right for you.

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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.

Instrument hacks

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.”

Instrument hacks

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.”

Instrument hacks

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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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.

NDVI

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.

NDVI

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.

NDVI

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?

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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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.

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.

lysimeters

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.”

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.

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Top Five Blog Posts in 2016

In case you missed them the first time around, here are the most popular Environmental Biophysics.org blog posts in 2016.

Lysimeters Determine if Human Waste Composting can be More Efficient

Top five blog posts Environmental biophysics

In Haiti, untreated human waste contaminating urban areas and water sources has led to widespread waterborne illness.  Sustainable Organic Integrated Livelihoods (SOIL) has been working to turn human waste into a resource for nutrient management by turning solid waste into compost.  Read more

Estimating Relative Humidity in Soil: How to Stop Doing it Wrong

Top five blog posts Environmental biophysics

Estimating the relative humidity in soil?  Most people do it wrong…every time.  Dr. Gaylon S. Campbell shares a lesson on how to correctly estimate soil relative humidity from his new book, Soil Physics with Python, which he recently co-authored with Dr. Marco Bittelli.  Read more.

How Many Soil Moisture Sensors Do You Need?

Top five blog posts Environmental biophysics

“How many soil moisture sensors do I need?” is a question that we get from time to time. Fortunately, this is a topic that has received substantial attention by the research community over the past several years. So, we decided to consult the recent literature for insights. Here is what we learned.

Data loggers: To Bury, or Not To Bury

Top five blog posts Environmental biophysics

Globally, the number one reason for data loggers to fail is flooding. Yet, scientists continue to try to find ways to bury their data loggers to avoid constantly removing them for cultivation, spraying, and harvest.  Chris Chambers, head of Sales and Support at Decagon Devices always advises against it. Read more

Founders of Environmental Biophysics:  Champ Tanner

Top five blog posts Environmental biophysics

Image: http://soils.wisc.edu/people/history/champ-tanner/

We interviewed Gaylon Campbell, Ph.D. about his association with one of the founders of environmental biophysics, Champ Tanner.  Read more

And our three most popular blogs of all time:

Do the Standards for Field Capacity and Permanent Wilting Point Need to Be Reexamined?

Top five blog posts Environmental biophysics

We asked scientist, Dr. Gaylon S. Campbell, which scientific idea he thinks impedes progress.  Here’s what he had to say about the standards for field capacity and permanent wilting point.  Read more

Environmental Biophysics Lectures

Top five blog posts Environmental biophysics

During a recent semester at Washington State University, a film crew recorded all of the lectures given in the Environmental Biophysics course. The videos from each Environmental Biophysics lecture are posted here for your viewing and educational pleasure.  Read more

Soil Moisture Sensors In a Tree?

Top five blog posts Environmental biophysics

Soil moisture sensors belong in the soil. Unless, of course, you are feeling creative, curious, or bored. Then maybe the crazy idea strikes you that if soil moisture sensors measure water content in the soil, why couldn’t they be used to measure water content in a tree?  Read more

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How to Measure Water Potential

In the conclusion of our 3-part water potential  series (see part 1), we discuss how to measure water potential—different methods, their strengths, and their limitations.

How to measure water potential

Vapor pressure methods work in the dry range.

How to measure water potential

Essentially, there are only two primary measurement methods for water potential—tensiometers and vapor pressure methods. Tensiometers work in the wet range—special tensiometers that retard the boiling point of water (UMS) have a range from 0 to about -0.2 MPa. Vapor pressure methods work in the dry range—from about -0.1 MPa to -300 MPa (0.1 MPa is 99.93% RH; -300 MPa is 11%).

Historically, these ranges did not overlap, but recent advances in tensiometer and temperature sensing technology have changed that. Now, a skilled user with excellent methods and the best equipment can measure the full water potential range in the lab.   

There are reasons to look at secondary measurement methods, though. Vapor pressure methods are not useful in situ, and the accuracy of the tensiometer must be paid for with constant, careful maintenance (although a self-filling version of the tensiometer is available).

Here, we briefly cover the strengths and limitations of each method.

Vapor Pressure Methods:

The WP4C Dew Point Hygrometer is one of the few commercially available instruments that currently uses this technique. Like traditional thermocouple psychrometers, the dew point hygrometer equilibrates a sample in a sealed chamber.

How to Measure Water Potential

WP4C Dew Point Hygrometer

A small mirror in the chamber is chilled until dew just starts to form on it. At the dew point, the WP4C measures both mirror and sample temperatures with 0.001◦C accuracy to determine the relative humidity of the vapor above the sample.

Advantages

The most current version of this dew point hygrometer has an accuracy of ±1% from -5 to -300 MPa and is also relatively easy to use. Many sample types can be analyzed in five to ten minutes, although wet samples take longer.

Limitations

At high water potentials, the temperature differences between saturated vapor pressure and the vapor pressure inside the sample chamber become vanishingly small.

Limitations to the resolution of the temperature measurement mean that vapor pressure methods will probably never supplant tensiometers.

The dew point hygrometer has a range of -0.1 to -300 MPa, though readings can be made beyond -0.1 MPa using special techniques. Tensiometers remain the best option for readings in the 0 to-0.1 MPa range.

Secondary Methods

Water content tends to be easier to measure than water potential, and since the two values are related, it’s possible to use a water content measurement to find water potential.

A graph showing how water potential changes as water is adsorbed into and desorbed from a specific soil matrix is called a moisture characteristic or a moisture release curve.

download

Example of a moisture release curve.

Every matrix that can hold water has a unique moisture characteristic, as unique and distinctive as a fingerprint. In soils, even small differences in composition and texture have a significant effect on the moisture characteristic.

Some researchers develop a moisture characteristic for a specific soil type and use that characteristic to determine water potential from water content readings. Matric potential sensors take a simpler approach by taking advantage of the second law of thermodynamics.

Matric Potential Sensors

Matric potential sensors use a porous material with known moisture characteristic. Because all energy systems tend toward equilibrium, the porous material will come to water potential equilibrium with the soil around it.

Using the moisture characteristic for the porous material, you can then measure the water content of the porous material and determine the water potential of both the porous material and the surrounding soil. Matric potential sensors use a variety of porous materials and several different methods for determining water content.

Accuracy Depends on Custom Calibration

At its best, matric potential sensors have good but not excellent accuracy. At its worst, the method can only tell you whether the soil is getting wetter or drier. A sensor’s accuracy depends on the quality of the moisture characteristic developed for the porous material and the uniformity of the material used. For good accuracy, the specific material used should be calibrated using a primary measurement method. The sensitivity of this method depends on how fast water content changes as water potential changes. Precision is determined by the quality of the moisture content measurement.

Accuracy can also be affected by temperature sensitivity. This method relies on isothermal conditions, which can be difficult to achieve. Differences in temperature between the sensor and the soil can cause significant errors.

Limited Range

All matric potential sensors are limited by hydraulic conductivity: as the soil gets drier, the porous material takes longer to equilibrate. The change in water content also becomes small and difficult to measure. On the wet end, the sensor’s range is limited by the air entry potential of the porous material being used.

How to Measure Water Potential

METER Tensiometer

Tensiometers and Traditional Methods

Read about the strengths and limitations of tensiometers and other traditional methods such as gypsum blocks, pressure plates, and filter paper here.

Choose the right water potential sensor

Dr. Colin Campbell’s webinar “Water Potential 201: Choosing the Right Instrument” covers water potential instrument theory, including the challenges of measuring water potential and how to choose and use various water potential instruments.

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

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Water Potential: The Science Behind the Measurement (Part 2)

In the second part of this month’s water potential  series (see part 1), we discuss the separate components of a water potential measurementThe total water potential is the sum of four components: matric potential, osmotic potential, gravitational potential, and pressure potential.  This article gives a description of each component. Read the article here…

Visualize Matric Potential

 

 

Next Week: Learn the different methods for measuring water potential and their strengths and limitations.

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

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Data Logger Dilemma: To Bury, or Not to Bury—An Update

Recently, we wrote about scientists who were burying their data loggers (read it here).  Radu Carcoana, research specialist and Dr. Aaron Daigh, assistant professor at North Dakota State University, used paint cans to completely seal their data loggers before burying them in the fall of 2015.

data logger

Paint can setup for buried data logger.

They drilled ports for the sensor cables, sealed them up, and when they needed to collect data, they dug up the cans, retrieved the instruments, and downloaded the data in a minute or less.  

Here Radu gives an update of what happened when he dug up his buried instruments in the spring.

Results of the Paint Can Experiment

In May of this year, we dug up eighteen units (one data logger and four soil moisture sensors per unit) left in the field since November 2015—over six months.

Did moisture get into the paint cans? —We found only three cans with water in them, purely due to installation techniques used for that specific unit. The other fifteen units were bone dry, although total precipitation for the month of April only amounted to 3.63 inches, plus the snow melt.

How was data recording and recovery? —For six months, every 30 minutes the soil moisture sensors took readings, the data logger recorded, and we retrieved all of the data, complete and unaltered.

data logger

Only three cans with water in them, due to installation techniques.

What about power consumption? The batteries were good —over 90% did not need replacement. The power budget provided by five AA batteries was more than enough for reading four soil moisture sensors at 30-minute intervals.

What Happens Now?

In the spring of this year, we installed 18 more units in the third farm field, right after planting soya. We now have 36 individual units (~$1,000 value each unit) buried in the ground in the middle of a field planted with corn or soybean, since the beginning of May.

On October 13-14 (after 5 months), we accessed the first twelve units (Farm A). All 30 minutes of data was read, recorded, and downloaded (since May).  The batteries and the other accessories were replaced, and then we sealed and reburied the cans. Only one unit out of twelve had an issue and was replaced: the battery exploded in the can (editor’s note: battery explosion is usually caused by a manufacturing defect and the risk can be lessened by purchasing higher quality batteries, although all types are susceptible to some degree).  Since battery leakage will often corrode everything the acid touches, the data logger had to be sent back for repair and there may be partial data loss. The other 24 units (Farm B and C) will be accessed next week, weather permitting.

data logger

Over 90% of batteries did not need replacement.

Is the Paint Can Method Worth it?

We will continue to monitor and retrieve the data from the buried data loggers (We don’t use data loggers suited for wireless communication, because several factors guided us not to). The paint can system works very well if the installation is done correctly, with great attention to detail, and it costs only $2.00/can. However, there are improvements that could be made in order to have this method become a standard in soil research. For instance, though we are still using paint cans and other common materials, advancements in the design of waterproof containers and sturdiness would be a huge step forward. This is just a well thought out concept – a prototype. It proves that burying electronics for a longer period of time can be done if properly executed.

Note:  METER’s (formerly Decagon) official position is that you should never bury your data logger.  But we couldn’t resist sharing a few stories of scientists who have figured out some innovative methods which may or may not be successful, if tried at other sites.

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Soil Sensors Help Thousand-Year-Old Levees Protect Residents of the Secchia River Valley

In Italy, on January of 2014, one of the Secchia river levees failed, causing millions of dollars in flood damage and two fatalities. Concerned with preventing similar disasters, scientists and geotechnical engineers are using soil sensors to investigate solutions in a project called, INFRASAFE (Intelligent monitoring for safe infrastructures) funded by the Emilia Romagna Region (Italy) on European Funds.  

Secchia river in Italy.

Secchia river in Italy (Image: visitsassuolo.it)

Professor Alberto Lamberti, Professor Guido Gottardi, Department of Civil, Chemical, Environmental, and Materials Engineering, University of Bologna, along with Prof. Marco Bittelli, University of Bologna professor of Soil and Environmental Physics, installed soil sensors along some transects of the Secchia river to monitor water potential and piezometric pressure.  They want to study properties of the compacted levee “soil”, during intense flooding.  Bittelli comments, “Rainfall patterns are changing due to climate change, and we are seeing more intense floods. There is a concern about monitoring levees so that we can, through studying the process, eventually create a warning system.”  

soil sensors

Trench for burying sensor cables.

What Are The Levees Made Of?

Amazingly, some of these levees are very old, built at the beginning of the second millennium to protect the Secchia valley population from floods. “These rudimentary barrages were the starting point of the huge undertakings, aiming at the regulation and stabilization of the river, which were gradually developed and expanded in the following centuries…building up a continuous chain all along the river.” (Marchii et. al., 1995)

soil sensors

Vegetation in the Secchia River floodplain.

Unlike natural soil with horizons, the soil that makes up the levees is made up of extremely compact clay and other materials, which will pose challenges to the research team in terms of sensor installation.  The team will use soil sensors to determine when the compacted material that makes up the levees gets so saturated it becomes weak.  Bittelli says, “We are looking at the mechanical properties of the levees, but mechanical properties are strongly dependent on hydraulic properties, particularly soil water potential (or soil suction).  A change in water potential changes the mechanical properties and weakens the structure.”  This can happen either when a soil dries below an optimal limit or wets above it; the result is a weakened barrier that can fail under load.

soil sensors

Here the team uses an installation tool to install water content sensors.

Soil Sensors Present Installation Challenges

To solve the installation problems, the team will use a specialized installation tool to insert their water content sensors.  Bittelli says, “Our main challenge is to install sensors deep into the levees without disturbing the soil too much.  It’s very important to have this tool because clearly, we cannot dig out a levee; we might be the instigator of a flood. So it was necessary for us to be able to install the sensors in a relatively small borehole.”  The researchers will install the sensors farther down than the current tool allows, so they are modifying it to go down to eight or ten meters.  Bittelli explains, “We used a prototype installation tool which is two meters long. We modified it in the shop and extended it to six meters to be able to install water content sensors at further depths.”

Another challenge facing the research team is how to install water potential sensors without disturbing the levee.  Marco explains, “We placed an MPS-6 (now called TEROS 21) into a cylinder of local soil prepared in the lab. A sort of a muffin made of soil with an MPS-6 inside. Then we lowered the cylinder into the borehole, installed the sensor inside, and then slid it down into the hole.  Our goal is to try and keep the structure of the soil intact. Since the cylinder is made of the same local soil, and it is in good contact with the borehole walls, hydraulic continuity will be established.”

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Researchers placed an MPS-6 into a cylinder of local soil prepared in the lab.

Unlike installing water content sensors, matric potential sensors don’t need to be installed in undisturbed soil but only require good contact between the sensor and the bulk soil so liquid water can easily equilibrate between the two. The researchers are also contemplating using a small camera with a light so they can see from above if the installation is successful.  

Find Out More

The researchers will collect data at two experimental stations, one on the Po river, and one on the Secchia River. So far, the first installation was successfully performed, and data are collected from the website. Bitteli says the first installation included water content, temperature, and electrical conductivity sensors, water potential sensors, and tensiometers connected to a wireless network that will transmit all the data to a central office for analysis.

You can read more about this project and how it’s progressing here.

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Soil Moisture: An Important Parameter in Weather Monitoring

CoCoRaHS and Weather Monitoring

Each time a rain, hail, or snow storm crosses over your area, volunteers are taking precipitation measurements that are then used to analyze situations ranging from water resource availability to severe storm warnings.  

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CoCoRaHS precipitation data is used by many high profile organizations.

CoCoRaHS (Community Collaborative Rain, Hail and Snow Network) is a non-profit community-based network of volunteers of all ages and backgrounds working together to measure and map precipitation (rain, hail, and snow).  Their data is used by the National Weather Service, meteorologists, hydrologists, emergency managers, city utilities, USDA, engineers, farmers, and more.  The organization will soon add another layer to their weather-monitoring efforts:  soil moisture measurement.

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In 1997, a localized flooding event in Fort Collins, Colorado was not well-warned due to lack of high-density precipitation observation.

Why Soil Moisture?

CoCoRaHS originated as the brain child of Nolan Doesken, the state climatologist of Colorado,  in 1997 in response to a localized flooding event in Fort Collins, CO that was not well-warned due to lack of high-density precipitation observations.  Ten years ago the Colorado Climate Center began a partnership with the National Integrated Drought Information System to establish the first regional drought early warning system. This particular system would serve the Upper Colorado River Basin and eastern Colorado.

From the beginning, Nolan was thinking about soil moisture.  He says, “When we first started this project, we identified one weakness of the current climate monitoring systems as the inability to quantitatively assess soil moisture.  Soil moisture is critical as it affects both short-term weather forecasts and long-term seasonal forecasts, which are important for drought early warning and avoiding the agricultural consequences of too much or too little soil moisture.”It wasn’t until years later in the drought of 2012, which developed rapidly in the mid and late spring across the intermountain west and central plains that Nolan began planning to use CoCoRaHS as a vehicle for improving the soil moisture aspect of drought early warning.

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The organization intends to measure soil moisture using the gravimetric method.

How Will Volunteers Measure Soil Moisture?

Historically, CoCoRaHS has had success using low-cost measurement tools, stressing training and education, and using an interactive website to provide the highest quality data, and soil moisture will be no different.  The organization intends to measure soil moisture using the gravimetric method, where the user will take samples using a soil ring, dry samples in their own oven, and measure sample weight with an electronic scale. Peter Goble, a research assistant at Colorado State, has developed the measurement protocols that volunteers will follow.  He says, “We have installed several different types of soil sensors and tried gravimetric techniques in a field next to the center, and our experience has helped us set up a protocol that gets observers as educated as they can be by the time they take their measurements. The coring device we use is something that came about through trial and error. We were trying to reconcile the fact that we really wanted deeper root zone measurements in order to satisfy drought early-warning-system users, and the need for an inexpensive set of standardized materials that we could send out to observers in a kit.”  Volunteers will take soil samples at each point in a grid pattern, both at the surface and at the 7-9 inch level near the root zone.

What will Happen to the Data?

Initially, while the program is in its test phase, the data will be put in a spreadsheet and shared. However, once CoCoRaHS has finished sending this protocol around the nation to a group of alpha testers, they’ll set up a website infrastructure enabling volunteers to enter their VWC data directly into the CoCoRaHS website.

The need for soil moisture measurement in weather monitoring will outweigh the volunteers’ ability to measure, but there is a solution.

The need for soil moisture measurement in weather monitoring will outweigh the volunteers’ ability to measure, but there is a solution.

Why the Gravimetric Method?

Nolan says the challenge of water content is that soil is highly variable across space.  And if you add issues like sensor performance, improper installation of sensors, problems with soil contact, changes in bulk density, and soil compaction, you end up with inconsistent data.  The gravimetric method will avoid inconsistencies in spatial measurements and ensure higher quality data.

An Overwhelming Task

Nolan says the need for soil moisture measurement in weather monitoring will outweigh the volunteers’ ability to measure, but there is a solution. “People who use soil moisture data in atmospheric applications need high resolution, gridded information in every square kilometer across the country, but it will happen through modeling.  The measurements we take of precipitation and soil moisture will help in the refinement of the weather modules the atmospheric scientists will use as input to their weather prediction models.”

See weather sensor performance data for the ATMOS 41 weather station.

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