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Dec 26

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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.

Image of a mountain with a little snow on the top

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.

Image of a researcher using a WP4C Dew Point Hygrometer to test a sample

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.

Image of an example of a moisture release curve in the form of a graph

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.

Image of a METER Tensiometer in the ground

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.

Take our Soil Moisture Master Class

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.

Watch it now—>

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

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

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Dec 19

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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 measurement. The 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.

Take our Soil Moisture Master Class

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.

Watch it now—>

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

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

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Dec 11

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Secrets of Water Potential: Learn the Science Behind the Measurement

This month in a 3 part series, we will explore water potential —the science behind it and how to measure it effectively.

Pouring water into a glass with ice around the glass

To understand water potential, compare the water in a soil sample to water in a drinking glass.

Water Potential: a Definition

Read the article here…

Next week learn about the four components of water potential—osmotic potential, gravitational potential, matric potential, and pressure potential.

Take our Soil Moisture Master Class

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.

Watch it now—>

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

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

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Nov 7

Mesh Wireless Sensor Networks: Will Their Potential Ever Be Realized? (Part 2)

Soil ecologist Dr. Kathy Szlavecz and her husband, computer scientist, Dr. Alex Szalay, both at Johns Hopkins University, are testing a wireless sensor network (WSN; Mesh Sensor Network), developed by Dr. Szalay, his colleague, computer scientist Dr. Andreas Terzis, and their graduate students (read part 1). Mesh networks generate thousands of measurements monthly from wireless sensors. The husband/wife team says that WSN’s have the potential to revolutionize soil ecology by generating a previously impossible spatial resolution.  This week, read about the results of their experiments.

Worm in the Mud

Overall, the experiments were a scientific success, exposing variations in the soil microclimate not previously observed.

Results and Challenges:

About the performance of the network, Kathy says, “Overall, our experiments were a scientific success, exposing variations in the soil microclimate not previously observed. However, we encountered a number of challenging technical problems, such as the need for low-level programming to get the data from the sensor into a usable database, calibration across space and time, and cross-reference of measurements with external sources.

The ability of mesh networks that generate so much data also presents a data management challenge. Kathy explains, “We didn’t always have the resources or personnel who could organize the data.  We needed a dedicated research assistant who could clean, handle, and organize the data. And the software wasn’t user-friendly enough.  We constantly needed computer science expertise, and that’s not sustainable.”  

The team also faced setbacks stemming from inconsistencies generated by new computer science students beginning work on the project as previous students graduated. This is why the team is wondering if a commercial manufacturer in the industrial sector would be a better option to help finish the development of the mesh network.

Mesh Wireless Sensor Network on rocks in the Atacama desert

This deployment is located in the Atacama desert in Chile. Atacama is one of the highest, driest places on Earth. These sensors are co-located with the Atacama Cosmological Telescope. The goal of this deployment is to understand how the hardware survives in an extreme environment. In addition to the cold, dry climate, the desert is exposed to high UV radiation. These boxes are collecting soil temperature, soil moisture and soil CO2 data. (Image: lifeunderyourfeet.org)

What’s Next?

Kathy and Alex say that mesh sensor network design has room for improvement.  Through their testing, the research team learned that, contrary to the promise of cheap sensor networks, sensor nodes are still expensive. They estimated the cost per mote including the main unit, sensor board, custom sensors, enclosure, and the time required to implement, debug and maintain the code to be around $1,000.  Kathy says, “The equipment cost will eventually be reduced through economies of scale, but there is clearly a need for standardized connectors for connecting external sensors and in general, a need to minimize the amount of custom hardware work necessary to deploy a sensor network.”  The team also sees a need for the development of network design and deployment tools that will instruct scientists where to place gateways and sensor relay points. These tools could replace the current labor-intensive trial and error process of manual topology adjustment that disturbs the deployment area.

Image of deployment locations in fields of the farming systems

This deployment is located in the fields of the farming system project at BARC. Soil temperature and moisture probes are placed at various locations of a corn-soybean-wheat rotation. The goal is to understand and explain soil heterogeneity and to provide background data for trace gas measurements. (Image: Lifeunderyourfeet.org)

Future Requirements:

According to Kathy, wireless sensor networks promise richer data through inexpensive, low-impact collection—an attractive alternative to larger, more expensive data collection systems. However, to be of scientific value, the system design should be driven by the experiment’s requirements rather than technological limitations. She adds that focusing on the needs of ecologists will be the key to developing a wireless network technology that will be truly useful.  “While the computer science community has focused attention on routing algorithms, self-organization, and in-network processing, environmental monitoring applications require quite a different emphasis: reliable delivery of the majority of the data and metadata to the scientists, high-quality measurements, and reliable operation over long deployment cycles. We believe that focusing on this set of problems will lead to interesting new avenues in wireless sensor network research.” And, how to package all the data collected into a usable interface will also need to be addressed in the future.

You can read about Kathy’s experiments in detail at Lifeunderyourfeet.org.

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

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Oct 31

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Mesh Wireless Sensor Networks: Will Their Potential Ever Be Realized?

Although the idea of mesh wireless sensor networks is not new, the realization of their many benefits have gone largely unrealized. The low success rate of most wireless systems makes the accomplishments of this Johns Hopkins group unique.

Image of bright orange, yellow, and red colored trees in autumn

Soil moisture and temperature are major drivers of seasonal dynamics, soil respiration, carbon cycling, biogeochemical functions, and even the types of species living in a certain area.

The ability to measure soil moisture and temperature is vital to ecologists who work in heterogeneous environments because these parameters are major drivers of seasonal dynamics, soil respiration, carbon cycling, biogeochemical functions, and even the types of species living in a certain area.  But ecologists’ scientific understanding of environmental conditions is hindered when soil moisture measurements disturb the research site, or when field measurements are not collected at biologically significant spatial or temporal granularities. Soil ecologist Dr. Kathy Szlavecz and her husband and computer scientist, Dr. Alex Szalay, both at Johns Hopkins University, are working to solve this dilemma by testing a wireless sensor network (WSN; Mesh Sensor Network), developed by Dr. Szalay, his colleague, computer scientist Dr. Andreas Terzis, and their graduate students. These generate thousands of measurements monthly from wireless sensors. The husband/wife team says that WSN’s have the potential to revolutionize soil ecology by generating a previously impossible spatial resolution.

Diagram of a mesh network data system for soil moisture

Architecture of an end-to-end mesh network data collection system. (Image: lifeunderyourfeet.org)

What is a Mesh Network?

In a mesh wireless sensor network, specially designed radio units (nodes) use proprietary or open communications protocols to self-organize and can pass measurement information back to central units called gateways. Different from star networks where each node communicates directly to the gateway, mesh networks pass data to each other, acting as repeater for other nodes when necessary.

Image of 37 sampling locations at the Smithsonian Environmental Research Center

These are the 37 sampling locations at the Smithsonian Environmental Research Center (SERC) in Edgewater, MD. Data from this deployment is aimed at understanding the effect of forest age, leaf litter input, and earthworm abundance on soil carbon cycling. (Image: lifeunderyourfeet.org)

With low power and reliability as their goal, they are deployed in dense networks to automatically measure conditions such as temperature and soil moisture. These node measurements are taken every few hours over several months. The data are then uploaded onto computers, where it can be maintained and searched. Kathy explains “Without an autonomous sensor system, experiments in need of accurate information about a multitude of environmental parameters on various spatial and temporal scales require a superhuman effort. The inexpensive nature of these sensors enable scientists to place a high-resolution grid of sensors in the field, and get frequent readouts.  This provides an extremely rich data set about the correlations and subtle differences among many parameters, allowing ecologists to design experiments that study not only the gross effects of environmental variables, but also the subtle relations between gradients and small temporal changes.”

Sunlight shining through trees in a forest

Without an autonomous sensor system, experiments in need of accurate information about a multitude of environmental parameters on various spatial and temporal scales require a superhuman effort.

Landscape Studies Benefit from Mesh Networks

Kathy and Alex have deployed mesh wireless sensor networks at several study areas around the state of Maryland.  Kathy says, “Once we record the measurements, we can combine that information with observations of soil organisms to better understand how soil organisms and the soil environment interact. This means we can make better predictions about how human activities will affect the soil environment.” In one urban landscape study, Kathy and her team deployed over 100 nodes around a CO2 flux tower looking at the two major landscape covers in an urban environment: grass and forest.  She explains, “We collected data from nodes connected to soil moisture and temperature sensors for over two years at these sites, and the system worked quite well. We collected about 180 million data points, and that’s no small feat.”

Next week: Learn the results of this research group’s mesh network testing and what Kathy thinks the future holds for this technology.

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

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

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Oct 10

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Measuring NDVI in a Greenhouse Presents Challenges (Part 2)

University of Georgia researcher, Shuyang Zhen, wanted to find out if she could optimize greenhouse irrigation with reference evapotranspiration calculated from environmental factors and a crop coefficient, using NDVI measurements to adjust for canopy size (see part 1). Learn the results of the experiment and how fast growth and flowering caused problems with the NDVI measurement.

Researchers measuring the NDVI of green plants in a greenhouse

Shuyang’s experimental setup.

Fast Growth Causes Problems

Shuyang says because the plants grew so large, the canopy filled in beyond what the sensor could see.  That meant there was additional leaf area that participated in vapor loss which wasn’t identified by the NDVI sensor.  As the canopies approached moderate-to-high canopy densities, Shuyang observed that the NDVI readings became less responsive to increases in canopy size. To work around this problem, Shuyang tried to calculate a vegetation index called the Wide Dynamic Range Vegetation index with the spectral reflectance outputs of the two wavebands measured by the NDVI sensor. Shuyang says, “This index was supposed to improve the sensitivity at higher canopy density, so I transformed all my data and was surprised that it actually improved the sensitivity when the canopy density was lower.  But at a higher canopy density it wasn’t as effective.”

Researchers measuring NDVI of petunias in a greenhouse

The red flowers reflected a lot of red light compared to the leaves, which confused the NDVI measurement.

Plant flowering also caused problems with the NDVI measurement.   Shuyang explains, “We had one cultivar of petunia with red flowers which formed on top of the canopy. The red flowers reflected a lot of red light compared to the leaves, which confused the NDVI measurement.  The NDVI value gradually decreased when the plants started to flower. There was no way I could get around that issue, so in some of the replicates, I removed the flowers, and in some I kept the flowers so I could compare the different responses and characterize why it happened.”

Poinsettia plant with red small flowers

The NDVI was very sensitive to the increase in crop size when the canopy was relatively small, but when you reach a certain canopy size and the canopy closure was nearly complete, then the sensitivity decreased.

Summary and Future Studies

During the early stages of growth, the research team saw a linear relationship between NDVI and crop coefficient. However, when the crop coefficient reached higher values, the response leveled off.  Shuyang says, “The response failed to change with further increases in the crop coefficient. The NDVI was very sensitive to the increase in crop size when the canopy was relatively small, but when you reach a certain canopy size and the canopy closure was nearly complete, then the sensitivity decreased.”  

Six poinsettia plants with small flowers arranged with one in the middle and five around the middle one in a circle

Lack of NDVI sensitivity during canopy closure and flowering translated to a problem with under-irrigation,

Shuyang adds that the lack of NDVI sensitivity during canopy closure and flowering translated to a problem with under-irrigation, so the team is thinking about developing separate models for different canopy stages.  She explains, “When the canopy reaches high canopy closure we may have to add an additional coefficient to compensate for that underestimation, but it’s difficult to evaluate what kind of coefficient we should use without more data. We need to do more studies to get an idea of what kind of adjustments will make the prediction more precise.”

Learn more about Shuyang’s work on the University of Georgia horticulture blog.

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Oct 3

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Measuring NDVI in a Greenhouse Presents Challenges

Greenhouse growers need irrigation strategies to maintain high plant quality, but it’s difficult to obtain quantitative information on exactly how much water will produce the highest-quality growth.

Picture of green house full of bright red Poinsettia plants

Greenhouse plant canopies are highly variable.

Estimating irrigation needs by using reference evapotranspiration calculated from environmental factors and a crop coefficient is standard for controlling field crop irrigation, but in a greenhouse this method can be challenging.  Greenhouse plant canopies are highly variable, and there’s limited information on the crop coefficient values for ornamental crops.  

Sensor-controlled, automated irrigation system in a greenhouse

Researchers used a sensor-controlled automated irrigation system with soil moisture sensors.

Measuring Crop Size

University of Georgia researcher, Shuyang Zhen, wanted to find out if she could solve this problem for greenhouse growers using NDVI measurements to adjust for canopy size. In a greenhouse setting, she and her team planted four types of fast growing herbaceous plants in small containers on top of greenhouse benches.  They set up a small weather station to monitor environmental parameters and used that data to calculate reference evapotranspiration.  

Green plants being monitored in a greenhouse

NDVI measurements are a non-destructive, continuous monitoring method to get information as to how big a crop is.

Using a sensor-controlled automated irrigation system with soil moisture sensors, the team determined the amount of water the plants used, which allowed them to calculate a crop coefficient on a daily basis.  They then used NDVI measurements to monitor crop size.  Shuyang says, “It’s easy to monitor environmental factors such as light, temperature, relative humidity, and wind speed, but it’s much harder to determine how big the crop is because many methods are destructive and time-consuming.  We chose NDVI measurements as a non-destructive, continuous monitoring method to get information as to how big our crop was. We were specifically interested in looking at how NDVI changes with the crop coefficient and how those two parameters correlate with each other.”

Purple flowers blooming in a greenhouse

Some species were more upward growing and some more sprawling.

Shuyang mounted multiple NDVI sensors on top of the benches, approximately four feet from the plants. Each sensor had a field of view of about .6 square meters and tracked the changes in plant size and NDVI values for over 8 weeks.  Shuyang says, “Each species had different growth habits.  Some species were more upward growing and some more sprawling. They also had different leaf chlorophyll content. Over the course of my study, three species reached reproductive stages, producing flowers. All of these factors had an effect on the NDVI measurements.”

Next week: Learn the results of the experiment and how fast growth and flowering caused problems with the measurement.

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

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Sep 5

Can Canopy Measurements Determine Soil Moisture? (Part 2)

Dr. Y. Osroosh, now a researcher at Washington State University, believes that plants are the best soil moisture sensors (see part 1).  He and his team have developed a new model for interpreting plant canopy signals to indirectly determine soil moisture in a Fuji apple orchard.  Below are the results of their efforts and what he sees as the future of this research.

Close up of flower blooming

Could plants be the best indicators of soil moisture?

The Results

Osroosh says they expected to see correlations, but such strong relationships were unexpected. The team found that soil water deficit was highly correlated with thermal-based water stress indices in drip-irrigated apple orchard in the mildly-stressed range. The relationships were time-sensitive, meaning that they were valid only at a specific time of day. The measurements taken between 10:00am and 11:00am (late morning, time of maximum transpiration) were highly correlated with soil water deficit, but the “coefficient of determination” decreased quickly and significantly beyond this time window (about half in just one hour, and reached zero in the afternoon hours).  Osroosh says this is a very important finding because researchers still think midday is the best time to measure canopy water stress index (CWSI). He adds, “The apple trees showed an interesting behavior which was nothing like what we are used to seeing in row crops. They regulate their stomata in a way that transpiration rate is intense late in the morning (maximum) and late in the afternoon. During the hot hours of afternoon, they close their stomata to minimize water loss.”

Picture of a corn field

Researchers have found good relationships between CWSI and soil water content in the root zone near the end of the season at high soil water deficits in row crops.

Other Research

Osroosh points to other efforts which have tried to correlate remotely-sensed satellite-based thermal or NIR measurements to soil water content. He says, “The closest studies to ours have been able to find good relationships between CWSI and soil water content in the root zone near the end of the season at high soil water deficits in row crops. Paul Colaizzi, a research agricultural engineer did his PhD research in part on the relationship between canopy temperature, CWSI, and soil water status in Maricopa, Arizona; also motivated by Jackson et al. (1981). Steve Evett and his team at Bushland, Texas are continuing that research as they try to develop a relationship between CWSI and soil water status that will hold up. They are using a CWSI that is integrated over the daylight hours and have found good relationships between CWSI and soil water content in the root zone near the end of the season when plots irrigated at deficits begin to develop big deficits.”

Picture of a green apple on a tree

Osroosh wants to study other apple cultivars, tree species, and perhaps even row crops, under other irrigation systems and climates.

What’s The Future?

In the future, Osroosh hopes to study the limitations of this approach and to find a better way to monitor a large volume of soil in the root zone in real-time (as reference). He says, “We would like to see how universal these equations can be. Right now, I suspect they are crop and soil-specific, but by how much we don’t know. We want to study other apple cultivars, tree species, and perhaps even row crops, under other irrigation systems and climates. We need to monitor crops for health, as well, to make sure what we are measuring is purely a water stress signal. One of our major goals is to develop a sensor-based setup which, after calibration, can be used for “precise non-contact sensing of soil water content” and “stem water potential” in real-time by measuring canopy temperature and micrometeorological parameters.”

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

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

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Aug 29

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Can Canopy Measurements Determine Soil Moisture?

As a young university student, Dr. Y. Osroosh, now a researcher at Washington State University, wanted to design the most accurate soil moisture sensor.  Over the years, however, he began to realize the complexity and difficulty of the task.  Inspired by the work of Jackson et al. (1981) and researchers in Bushland, TX, he now believes that plants are the best soil moisture sensors.  He and his team developed a new model for interpreting plant canopy signals to indirectly determine soil moisture.

Apples on an open air tree

The team measured microclimatic data in an apple orchard.

How Can Plants Indicate Water in Soil?

Osroosh and his team wanted to use plant stress instead of soil sensors to make irrigation decisions in a drip-irrigated Fuji apple tree orchard. But, the current practice of using the crop water stress index (CWSI) for detecting water stress presented some problems, Osroosh comments, “Currently, scientists use either an empirical CWSI or a theoretical one developed using equations from FAO-56, but the basis for FAO-56 equations is alfalfa or grass, which isn’t similar to apple trees.”  One of the main differences between grass and apple trees is that apple tree leaves are highly linked to atmospheric conditions. They control their stomata to avoid water loss.  

Apple tree canopy in an open air field

There is high degree of coupling between apple leaves and the humidity of the surrounding air.

So Osroosh borrowed a leaf porometer to measure the stomatal conductance of apple trees, and he developed his own crop water stress index, based on what he found.  He explains, “We developed a new theoretical crop water stress index specifically for apple trees. It accounts for stomatal regulations in apple trees using a canopy conductance sub-model. It also estimates average actual and potential transpiration rates for the canopy area which is viewed by a thermal infrared sensor (IRT).”

Fuji open air apple orchard (Roza Farm, Prosser, WA).

Fuji apple orchard (Roza Farm, Prosser, WA) where Osroosh performed his research.

What Data Was Used?

Osroosh says they established their new “Apple Tree” CWSI based on the energy budget of a single apple leaf, so “soil heat flux” was not a component in their modeling. He and his team measured soil water deficit using a neutron probe in the top 60 cm of the profile, and they collected canopy surface temperature data using thermal infrared sensors. The team also measured microclimatic data in the orchard.  

Close up of an apple on a tree

Neutron probes were problematic, as they did not allow collection of data in real time.

Osroosh comments, “The accuracy of this approach greatly depends on the accuracy of reference soil moisture measurement methods.  To establish a relationship between CWSI and soil water, we needed to measure soil water content in the root zone precisely. We used a neutron probe, which provides enough precision and volume of influence to meet our requirements.  However, it was a labor and time intensive method which did not allow for real-time measurements, posing a serious limitation.”

Next week: Learn the results of Dr. Osroosh’s experiments, the future of this research, and about other researchers who are trying to achieve similar goals.

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

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

Download the “Researcher’s complete guide to leaf area index (LAI)”—>

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Apr 18

Climate Change, Genetics, and the Future World

Climate change scientists face a particular challenge— how to simulate climate change without contributing to it. Paul Heinrich, a Research Informatics Officer associated with the Southwest Experimental Garden Array (SEGA) remembers looking at the numbers for a DOE project that would have used fossil fuel to measure forests’ response to temperature change. “It would have been very, very expensive in fossils fuels to heat a hectare of forest,” he says.

The alternative is, “to use elevation change as a surrogate for climate change so we could do climate change manipulations without the large energy costs.”

SEGA Vegetation Zones diagrams

An overview of the SEGA sites using elevation change as a surrogate for climate change. For more information on these sites, visit http://www.sega.nau.edu/. Photo credit Paul Heinrich

By monitoring organisms across a temperature gradient it is possible to identify genetic variation and traits within a species that could contribute to a species survival under projected future climates.

Control and Monitoring Infrastructure

SEGA is an infrastructure project started in 2012 after researchers at Northern Arizona University’s Merriam-Powell Center for Environmental Research were awarded a $2.8 million dollar NSF grant with a $1 million match from NAU. Consisting of ten fenced garden sites for genetics-based climate change research, SEGA is set on an elevation gradient from 4000 to 9000 feet in the Southwestern United States. Each SEGA site has an elaborate data collection and control system with meteorological stations and site-specific weather information. Custom-engineered Wireless Sensing Actuating and Relay Nodes (WiSARDs) send data packets to a hub which then send the data back to a centralized server.

Because there is inherent moisture content variability from site to site, volumetric water content and soil water potential sensors have been installed to monitor and maintain moisture levels. If there is a change in soil moisture at one site, soil sensors will detect the difference. Software on the server notes the difference and sends a signal to the other sites, turning on irrigation until the soil moisture matches across sites.

SEGA Cyberinfrastructure Major Components diagram

An illustration of SEGA’s cyberinfrastructure and data management system. Photo credit Paul Heinrich.

Having such an elaborate infrastructure creates an opportunity for researchers looking to conduct climate change research. By offering access to the pre-permitted SEGA sites, the hope is that research will generate much-needed data for climate projections and land management decisions.

When asked if the data stream was overwhelming to manage Heinrich said, “Well, not yet. We are just getting started. The system is designed for what SEGA is expected to look like in ten years, where we expect to have 50 billion data points.”

Research Considerations

Climate change projections show temperatures increasing rapidly over the next 50 to 100 years, bringing drought with it. The impact of these changes will be dramatic. Temperature and drought tolerant species will survive, those that are not will die, drastically changing the landscape in areas that are currently water stressed. Pests like the pine beetle and invasive species like cheatgrass will do well in a drier environment where water-stressed natural species will not be able to compete.

Red canyon called Soap Creek AZ from an Aerial view

Soap Creek, AZ from above. With climate change projections it is likely that more land will become marginal. Photo credit Paul Heinrich.

“Foundational species,” or species that have a disproportionate impact on the ecosystem, are the primary focus of the research efforts at SEGA sites. These are the species that drive productivity, herbivore habitat, and carbon fixation in the ecosystem. Unlike forests in other parts of the United States, forests in the Southwest can be dominated by one or two species, which makes potential research subjects easier to identify.

Genetic Variance

Amy Whipple, an Assistant Professor in Biology and the Director of the Merriam-Powell Research Station who oversees the day-to-day activities at SEGA, has been conducting some of her own research at the garden sites. Whipple has studied Piñon Pine, Southwestern White Pine, and has a proposal to study Cottonwood in process.

Whipple says that models currently suggest that Piñon Pine will be gone from Arizona within the next 50 years, adding that the models do not take into account possibilities for evolution or genetic variance that might help the Piñon survive. Her research is largely asking, will trees from hotter, drier locations have a better chance of surviving climate change? “We’re trying to do that with a number of different species to look for ways to mitigate the effects of climate change in the Southwest.”

Researchers documenting a Piñon Pine

Researchers documenting a Piñon Pine. Photo credit Paul Heinrich.

In some of her research on Piñon Pine, it was discovered that four different species were grouped morphologically and geographically from southern Arizona to Central Mexico. While this suggests that the divergence of species has occurred, it also suggests a low migration rate for these tree species. Migration rates of drought and temperature tolerant species is an important consideration when modeling for a future climate. If the migration of genetically adapted species cannot keep up with climate, the land could become marginal as a foundational species dies off.

Climate Change Predictions and Considerations

In the Southwest, there are entire forests that could become grassland in 50 years because the genetic characteristics of the foundational species currently in those regions will not adapt to higher temperatures and drought stress. But what does this mean from a land management perspective?

Ponderosa pine tree hanging off the side of a rocky cliff in the desert

Ponderosa pine trees, a foundational species in some area of the Southwestern United States.

Environmental conservationists maintain that we should protect the unique species that are in a place and that introducing other organisms or genetic material would be an ethical violation. Environmental interventionists make the argument that climate change has been caused by humans, so we have lost the option of remaining bystanders.

Research, Land Management and Policy

Paul Heinrich says that the route we take to manage the land will depend on our end goals. “Places that have trees now, if you want them to have trees 50 years from now, you are going to have to do something about it. The trees that are on the landscape right now are locally adapted to the past climate. They are not necessarily adapted to the future climate. They are probably maladapted to the future climate.”

To be clear, SEGA’s goal is not to promote or implement assisted migration. Instead, Amy Whipple says, SEGA can test what the effects of assisted migration might be. “In a smaller experimental context, we’re asking: how will these plants do if we move them around? What will happen to them if we don’t move them around?’” The goal is to provide decision makers with the data they need to make informed decisions about how to manage the land.

Image of a Meadow with trees in the distance and a set of mountains

The Arboretum Meadow in Flagstaff, AZ. Home of one of the SEGA research sites. Photo credit Paul Heinrich.

Whipple’s own view is that we may no longer have the option of doing nothing. “Unless major changes are made for the carbon balance of the planet, keeping things the same is not a viable option. Managing for a static past condition is not viable anymore.”

Remaining Questions

Both Heinrich and Whipple acknowledge that these are inherently difficult questions. Ultimately the public and land managers must make these decisions. In the meantime, data from SEGA research may help ensure better predictions, better decisions, and better outcomes.

To find out more about conducting your own climate change research using SEGA go to: http://www.sega.nau.edu/use-sega. 

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