Sep 4

Examining Plant Stress using Water Potential and Hydraulic Conductivity

Many scientists rely on water potential alone to measure plant water stress. Leo Rivera, a METER soil scientist, shows how a two-pronged approach, using hydraulic conductivity as well as water potential, can make those measurements more powerful.

Green tomato plant with three bright red tomatoes

Measuring hydraulic conductivity in nursery plants shows why plants are stressed.

Soil moisture release curves can give you incredible detail about water movement, allowing you to understand not only that plants are stressed, but WHY they are not getting the water they need.

Recently, we ran into a mystery where this method was useful. Growers at a Georgia nursery noticed that plants growing in a particular soilless substrate were beginning to show signs of stress at about -10 kPa water potential, which is still really wet. They wanted to know why.

We decided to create the unsaturated hydraulic conductivity and soil moisture release curves for the substrate (using the Wind Schindler technique [HYPROP lab instrument]) and found that it had a dual porosity curve: essentially, a curve with a “stair step” in it. The source of the “stair step” can be explained by considering the substrate, which was made up of bark mixed with some other fine organic materials. In the bark material there were a lot of large and small pores, but no medium-sized pores (this is called a “gap-graded” pore size distribution). This gap in the pore size distribution reduced the unsaturated hydraulic conductivity and caused the stress. Even though there was available water in the soil, it couldn’t flow to the plant roots.

Nursery seedlings

That would have been pretty hard to understand without detailed hydraulic conductivity and soil moisture release curves—curves with more detail than most traditional techniques can provide. Our measurements showed that unsaturated hydraulic conductivity can have a major effect on how available water is to plants. Our theory about the soilless substrate was that as the roots were taking up water, they dried the soil around them pretty quickly. In a typical mineral soil, the continuous pore size distribution would allow water to flow along a water potential gradient from the surrounding area to the soil adjacent to the roots. In the bark, the roots dried the area around them in the same way, but the gap in pore size distribution created low hydraulic conductivity and prevented water from moving into the soil adjacent to the roots. This caused plants to start stressing even though the substrate was still quite wet.

We were pretty excited about this discovery. It shows that water potential, though critical, may not always tell the whole story. Using technology to measure the full soil moisture release curve and the hydraulic conductivity in one continuous test, we discovered the real reason plants were wilting even when surrounded by water. In the past, it took three or four different instruments and several months to take these measurements. We can now do it in a week. For more information about creating these kinds of curves, check out the app guide: “Tools and Tips for Measuring the Full Soil Moisture Release Curve.”

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Aug 28

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Low Impact Design: Sensors Validate California Groundwater Resource Management

Michelle Newcomer, a PhD candidate at UC Berkeley, (previously at San Francisco State University), recently published research using rain gauges, soil moisture, and water potential sensors to determine if low impact design (LID) structures such as rain gardens and infiltration trenches are an effective means of infiltrating and storing rainwater in dry climates instead of letting it run off into the ocean.

Body of water with rain droplets hitting the surface

Can Low Impact Design Structures store rainwater?

Low Impact Design Structures

Global groundwater resources in urban, coastal environments are highly vulnerable to increased human pressures and climate variability. Impervious surfaces, such as buildings, roads, and parking lots prevent infiltration, reduce recharge to underlying aquifers, and increase contaminants in surface runoff that often overflow sewage systems. To mitigate these effects, cities worldwide are adopting low impact design (LID) approaches to direct runoff into natural vegetated systems such as rain gardens that reduce, filter, and slow stormwater runoff. LID hypothetically increases infiltration and recharge rates to aquifers.

Infiltration and Recharge

Michelle and the team at San Francisco State University, advised by Dr. Jason Gurdak, realized that the effects of LID on recharge rates and quality were unknown, particularly during intense precipitation events for cities along the Pacific coast in response to inter-annual variability of the El Niño Southern Oscillation (ENSO). Using water potential and water content sensors she was able to quantify the current and projected rates of infiltration and recharge to the California Coastal Westside Basin aquifer system. The team compared a LID infiltration trench surrounded by a rain garden with a traditional turf-lawn setting in San Francisco. She says, “Cities like San Francisco are implementing these LID structures, and we wanted to test the quantity of water that was going through them. We were interested specifically in different climate scenarios, like El Niño versus La Niña, because rain events are much more intense during El Niño years and could cause flash flooding or surface pollutant overflow problems.”

Sensors Tell the Story

The research team looked at the differences in the quantity of water that LID structures could allow to pass through. Michelle says. ”The sensors yielded data proving LID areas were effective at capturing the water, infiltrating it more slowly, and essentially storing it in the aquifer.” The team tested how a low-impact development-style infiltration trench compared to an irrigated lawn and found that the recharge efficiency of the infiltration trench, at 58% to 79%, was much higher than that of the lawn, at 8% to 33%.

Rain Gauges Yield Surprises

Though it wasn’t part of the researchers’ original plan, they used rain gauges to measure precipitation, which yielded some surprising data. Michelle comments, “We were just going to use the San Francisco database, but it became necessary to use the rain gauges because of all the fog. The fog brought a lot of precipitation with it that didn’t come in the form of raindrops. As it condensed on the leaves, it provided a substantial portion of the water in the budget, and that was surprising to me. The rain gauge captured the condensate on the funnel of the instrument, so we were able to see that a certain quantity of water was coming in that is typically neglected in many studies.”

Future El Niño Precipitation

Michelle also found some really interesting results regarding El Niño and La Niña. She says, “I did a historical analysis to establish baselines for frequency, intensity, and duration of precipitation events during El Niño and La Niña years. I then ran projected climate data through a Hydrus-2D model, and the models showed that with future El Niño intensities, recharge rates were effectively higher for a given precipitation event. During these events, in typical urban settings, water runs off so fast that only these rain gardens and trenches would be able to capture the rain that would otherwise be lost to the ocean. This contrasts with a La Niña climate scenario where there’s typically less rain that is more diffuse. Most of that rain may eventually be lost to evaporation during dry years. So using sensors and 2D modeling we have validated the hypothesis that LID structures provide a service for storing water, particularly during El Niño years where there are more intense rainstorms.”

Michelle’s research received some press online and also was featured in the AGU EOS Editor’s spotlight. Her results are published in the journal Water Resources Research.

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Aug 22

Author Interview: Soil Physics with Python

The new book Soil Physics with Python: Transport in the Soil-Plant-Atmosphere System written by Dr. Marco Bitteli, Dr. Gaylon S. Campbell, and Dr. Fausto Tomei presents concepts and problems in soil physics as well as solutions using original computer programs.

Soil Physics with Python

In contrast to the majority of the literature on soil physics, this text focuses on solving, not deriving, differential equations for transport. Numerical methods convert differential equations into algebraic equations, which can be solved using conventional methods of linear algebra. Here, Dr. Campbell interviews about this update to his classic book Soil Physics with BASIC.

Why did you write the first book, Soil Physics with BASIC?

Soil physics classes were always frustrating for me because you would spend time writing fancy equations on the chalkboard, and in the end, you couldn’t do anything with them. You couldn’t solve any of the problems because, even though they involved difficult mathematics, the math was still so simplified that it didn’t apply to anything that went on in nature.

When I taught my first graduate soil physics class, I determined that we were going to be able to do something by the time we finished. Luckily, in the mid-1970s, personal computers were being developed, and I realized this was the answer to my problem. Numerical methods could solve any problem with any geometry in it. It wasn’t limited to problems that fit the assumptions needed to derive a complex differential equation. I could write computer programs that simplified the mathematics for the students and teach them how to solve those problems using numerical methods. By the end of the semester, my students would have a set of tools that they could use to solve problems in the real world.

Did this book come from class notes or some other source?

I wrote two textbooks and they both came the same way. When I first started teaching, I had a textbook that was inadequate, so I began writing notes of my own and handing them out to the students. After two years, I turned these notes into An Introduction to Environmental Biophysics. Soil Physics with BASIC came about by the same process, but I enlisted the help of my daughter, Julia, to type it up. It was in the early days of word processing so entering equations was quite difficult. It all went well for her until chapter eight, which was a nightmare of greek symbols. After she finished slogging for days through the material, we somehow lost the chapter. She retyped it, and we lost it again, making her type it three times! We didn’t have spreadsheets then either, so the figures were all hand-drawn by my daughter, Karine.

Red soil in the desert with trees and brush around

Marco [Bitteli] has added two and three-dimensional flow problems, so you can model whole landscapes and water behavior in an entire terrain.

What does Soil Physics with Python add to the conversation?

First, it updates the programming language. BASIC was a language invented at Dartmouth and intended to be a simple teaching language. It was never supposed to be a scientific computer language. Python (13:26.) is a newer language, and there are many open source programs for it, making it a better language to use for science.

Secondly, the old book had one-dimensional flow problems in it for the most part, but Marco [Bitteli] has added two and three-dimensional flow problems, so you can model whole landscapes and water behavior in an entire terrain.

In addition, Dr. Bitteli describes the process and analysis of soil treated as fractals as well as soil image analysis. There are a lot of extensions and updates that weren’t in the original book.

Will it be accessible across all disciplines?

To some extent, different disciplines speak different languages. A soil physicist talks about water potential, and a geotechnical engineer talks about soil suction. Thus, there may be some translation of discipline-specific terms, but it’s intended to be a book that people in the plant sciences can use along with people in the soil sciences.

Dr. Marco Bitteli earned his PhD at Washington State University and was Dr. Campbell’s former student. This book is a product of their continued collaboration. Dr. BBitteli is now a professor at University of Bologna, the oldest university in operation in the world. Soil Physics with Python is available at Amazon.com.

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Aug 14

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The Potential of Drones in Research

Someday soon, multi-rotors will execute pre-programmed flight paths over several hundred research plots collecting daily data and sending it back to a computer while researchers sip their morning coffee. Researchers and growers won’t need to know anything about flying: the drones will fly themselves. This is the dream.

One UAV (unmanned air vehicle) industry leader at the above drone demonstration commented, “The truth is that this is where agriculture (and research) is going, and I don’t mean ‘Tomorrowland’ going–I mean it’s pretty much there. The only thing that’s holding us back is a permit from the FAA for autonomy, and that’s because the FAA is slowly backing into this UAV piece because we have the busiest general aviation sky in the world. But really, what you should have in your mind is multiple units operating with a single operator in a control vehicle.” The above UAV was extensively tested in California’s NAPA valley with results soon to be published online.

In this blog, a METER scientist and an instrumentation engineer give their perspectives on what needs to happen before drones reach their full research potential.

Drone Hexacopter

What are the advantages of drones for researchers?

Dr. Colin Campbell, research scientist-

One of the biggest challenges of work in the field is variability: low spots, high spots, sandy soil, clay soil, hard pans beneath the surface in some areas and not in others. This results in highly variable performance in crops. In addition to that, even when you have good homogeneity in a field, you might have differences due to irrigation or rainfall. If we want to improve agriculture, one thing that we have to do is be able to come out with better tools to be able to visualize the field in more than a single dimension. In order to do this right now, students go out and take plant measurements all day, every day, all summer long. The advantage of a drone is that you could do flyovers of a field, monitoring the traits that you’re interested in using reflectance indices that would normally take days of work.

What are the obstacles to progress?

Greg Kelley, mechanical engineer, and drone hobbyist-

Recently, the FAA has come out with a set of guidelines for the industrial use of drones: flying machines have to stay under a certain ceiling (500 ft; 150 m), and they have to be flown in the line of sight of the operator. The naive thing about those policies is: how much control does the operator have over the drone anyway? It used to be that with your remote control, you were moving the control surfaces (flaps, rudder, etc) on the aircraft, but this is changing. The onboard computer performs things like holding a stable altitude, maintaining a GPS location, or auto-stabilization (it keeps the aircraft level, even when a gust of wind comes). Those are degrees of control that have been taken away from the operator. Thus, according to the level of automation that the operator has built into the system, he may not be in direct control at all times. In fact, these machines are being developed so that they can fly themselves. From my perspective, the FAA regulations are going to have to evolve along with the automation of drones in order to allow the development of this technology in an appropriate way.

Drone with eight rotors.

What needs to happen before drones reach their full potential?

Dr. Colin Campbell–

Even if we get the flexibility required with drones, we’ve got to get the right sensor on the drone. On the surface, this seems relatively simple. Sensors to measure spectral reflectance are available in a package size that should easily mount on a drone platform. But, there are still many challenges. First, current spectral reflectance sensors make a passive reflectance measurement, meaning we’re at the mercy of the reflected sunlight. Clouds, sun angle, and leaf orientation, among other things, will all affect the measurement. There are several groups working on this (just search “drone NDVI” on the internet), but it’s a difficult problem to solve. Second, drones create a spectral reflectance “map” of a field that needs to be geo-referenced to features on the ground to match measurements with position. Once data are collected, the behavior of “plot A” can only be determined by matching the location and spectral reflectance of “plot A.” Different from the first challenge, this is more related to programming than science but is still a major hurdle.

Despite these challenges, drones promise incredible benefits as an agricultural and environmental measurement tool. As one industry leader at the drone demonstration put it, “the complexity of the problems that agriculture faces and the opportunities for efficiencies are vast. It will require ongoing engagement, next year and the year after that. There are a lot of questions to be answered and the efficacy is yet to be determined, but it’s exciting to watch the UAV helicopter and where it’s going.” Both Campbell and Kelley agree that significant advances will be made within the next few years.

Read about an ROI calculator that’s been created to help growers quantify whether the benefits of using a drone will exceed their costs.

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Aug 7

New Medium Scale Soil Moisture Measurement Technique

Between dielectric soil moisture sensors with a volume of influence measured in liters and remote sensing systems which measure soil moisture on the scale of kilometers, there is a gap—a gap Dr. Larry Murdoch of Clemson University has been working to fill. In this post, read about the DELTA (Displacement Extensometer for Lysimetric Terrain Analysis), an instrument that measures water content measurements over an area with a 25 m radius.

Close up picture of cracked and dried soil

Dr. Murdoch became interested in how much water content was in the vadose zone (the unsaturated soil above the water table). He wondered if he could use a strain measuring technique to quantify it.

A New Idea:

Dr. Murdoch was a graduate student in structural geology and geomechanics in the mid-1980s, working on the mechanics of hydraulic fractures in soil. He developed techniques for environmental “fracking” to clean up contaminated soil, long before the recent applications by the oil industry that have caused fracking to become a household word. Fracking causes movements in soil, and Dr. Murdoch developed methods for measuring those movements in order to monitor fracture displacement. This led to work on sensitive borehole extensometers that could measure small strains in rock during well testing.

In the course of his hydrology work, Dr. Murdoch became interested in how much water content was in the vadose zone (the unsaturated soil above the water table). He wondered if he could use the strain measuring technique to quantify it. He decided to bore a hole into the vadose zone and insert a simplified extensometer device that could measure the strain as the soil expands and contracts. This would allow him to gauge the weight change of the overburden. Then, because other mass changes are relatively minor compared to the water in the soil, that weight change would enable him to determine water content.

Since soil compresses more than bedrock, Dr. Murdoch developed a method where he inserted two anchors and cylinders that are pressed up against the soil borehole. In the middle of these cylinders is a fiberglass rod held tight by the bottom anchor which is able to move inside the top anchor. The anchors move up and down from the stress on the soil, and this movement is transferred to the rod where it can be measured with a high-resolution displacement transducer.

Diagram of the DELTA (Displacement Extensometer for Lysimetric Terrain Analysis)

Dr. Murdoch’s device is so sensitive that when it is buried 6 m, it will register clear strain signals as his student walks over it. The weight of a person causes around 50 nanometers of displacement at the Clemson Field site, but the instrument itself can resolve displacement approaching 1 nanometer. And the diameter of measurement on the surface is about 4 times the depth. So if you install the system at 7m, you’d be measuring about a 25 m diameter circle on top.

Like almost all other water content techniques, the challenge is removing all other confounding factors that affect the measurement. It has been said that all sensors are temperature sensors first. Not surprisingly, one thing that causes errors in the system is temperature, though Dr. Murdoch’s team has dealt with that by getting the system deep in the soil and putting the electronics near it so the temperature change is small. Barometric pressure also produces cyclical loading of soil mass and requires correction over a range of periods. And, since the calculation of water content requires an estimate of the soil elasticity, changes in soil moisture also may affect the measurement. Considerable work has been done and significant progress has been made in dealing with these and other issues with the extensometer approach.

picture of a field with a barn in the distance and the ski orange and grey

An advantage of the system is its ability to be buried. In order to plow, for example, all you have to do is pull the sensor up, take off the top plastic casing, and cap it, and the grower can drive a plow over the top.

Strengths:

The amazing thing is that Dr. Murdoch’s system can resolve less than a millimeter of rain water falling on the soil surface, and it can match trends over time. In addition, you can easily calibrate the system by getting your 190-pound student to walk over the top of it and then checking that the compressibility of the soil matches that weight.

Another advantage of the system is its ability to be buried. In order to plow, for example, all you have to do is pull the sensor up, take off the top plastic casing, and cap it, and the grower can drive a plow over the top. Finding the installation can be challenging, so it must be located by precision GPS or survey equipment prior to burial. But, if done correctly, the site can be monitored for long periods of time.

Though not yet a final technology, the Delta extensometer did correlate well with point measurements of water content and shows a lot of promise. The instrument was developed with funding from the National Science Foundation. Colby Thrash, a grad student at Clemson, has done much of the recent work. Dr. Murdoch’s team will publish a paper describing the technique soon in Water Resources Research.

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Jul 31

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Water Potential Versus Water Content

Dr. Colin Campbell, soil physicist, shares why he thinks measuring soil water potential can be more useful than measuring soil water content.

A horsetail plant showing possible signs of guttation where the water potential in the soil overnight is high enough to force water out of the stomates in the leaves.

I know an ecologist who installed an extensive soil water content (VWC) sensor network to study the effect of slope orientation on plant available water. He collected good VWC data, but ultimately he was frustrated because he couldn’t tell how much of the water was available to plants.

He’s not alone in his frustration. Accurate, inexpensive soil moisture sensors have made soil VWC a justifiably popular measurement, but as many people have discovered, a good hammer doesn’t make every soil water problem a nail. I like to compare water potential to temperature because both are considered “intensive” variables that define the intensity of something.

People often try to quantify their own environment, because those measurements define comfort and happiness. Long ago, they discovered they could make an enclosed glass tube, put mercury inside, and infer this intensive variable called temperature from the changes in the mercury’s volume. This was an obvious way to define the comfort level of a human being.

People discovered they could make an enclosed glass tube, put mercury inside, and infer an intensive variable called temperature.

They could have measured the heat content of their surroundings. But they would have discovered that while heat content would be higher in a larger room and lower in a smaller room, you would feel the same comfort level in both rooms. The temperature measurement helps you know whether or not you’d be comfortable without any other variables entering into the equation.

Similar to heat content, water content is an amount. It’s an extensive variable. It changes with size and situation. Consider the following paradoxes:

A soil with fairly low volumetric water content can have plenty of plant-available water and a soil with high water content can have almost none.

Gravity pulls water down through the profile, but water moves up into the soil from a water table.

Two adjacent patches of soil at equilibrium can have significantly different water content.

In these and many other cases, water content data can be confusing because they don’t predict how water moves. Water potential measures the energy state of water and thus explains realities of water movement that otherwise defy intuition. Like temperature, water potential defines the comfort level of a plant. Similar to the room size analogy for temperature, if we know the water potential, we can know whether plants will grow well or be stressed in any environment.

sand with plants poking out and a blue sky in the background

Soil, clay, sand, potting soil, and other media, all hold water differently.

Plants don’t understand the concept of a content in terms of “comfort” because soil, clay, sand, potting soil, and other media, all hold water differently. Imagine a sand with 30% water content. Due to its low surface area, the sand will be too wet for optimal plant growth, threatening a lack of aeration to the roots, and flirting with saturation. Now consider a fine textured clay at that same 30% water content. The clay may appear only moist and be well below optimum “comfort” for a plant due to the surface of the clay binding the water and making it less available to the plant.

Water potential measurements clearly indicate plant available water, and, unlike water content, there is an easy reference scale. We know that plant optimal runs from about -2-5 kPa which is on the very wet side, to about -100 kPa, at the drier end of optimal. Below that plants will be in deficit, and past -1000 kPa they start to suffer. Depending on the plant, water potentials below -1000 to -2000 kPa cause permanent wilting.

So, why would we want to measure water potential? Water content can only tell you how much water you have. If you want to know how fast water can move, you need to measure hydraulic conductivity. If you want to know whether water will move and where it’s going to go, you need water potential.

Learn more

Soil moisture is more than just knowing the amount of water in soil. Learn basic principles you need to know before deciding how to measure it. In this 20-minute webinar, discover:

Water content: what it is, how it’s measured, and why you need it
Water potential: what it is, how it’s different from water content, and why you need it
Whether you should measure water content, water potential, or both
Which sensors measure each type of parameter

Many questions about water availability and movement are best answered by measuring water potential. To find out more, watch any of the virtual seminars below, or visit our new water potential website.

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

Water Potential 101: Making Use of an Important Tool

Water Potential 201: Choosing the Right Instrument

Water Potential 301: How to Push Your Instruments Past their Specifications

Water Potential 401: Advances in Field Water Potential

Find out when you should measure both water potential and water content.

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—>

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Jul 24

Lessons from the Field – Sensor Installation Considerations

In the Midwest, government incentives are sometimes provided to convert marginal lands to switchgrass, a leading choice for bio-energy feedstock production. Michael Wine, a researcher at New Mexico Tech, wanted to investigate whether switchgrass’s deeper root systems would affect the water cycle both during and after crop establishment. In the first stages of his investigation, he learned that many factors need to be considered when determining the optimal location for sensor installation.

Image of Switchgrass in a field with a gray sky in the background

Switchgrass (Image: Plant and Soil Sciences E-library)

Aquifer Recharge

Wine used Gee passive capillary lysimeters to determine deep drainage under natural vegetation, wheat, and switchgrass in order to improve our understanding of both the baseline water cycle and the water budget associated with a switchgrass monoculture in Woodward, Oklahoma. He put the lysimeters and some soil moisture (capacitance) sensors into the Beaver-North Canadian River Alluvial Aquifer to look at recharge, but ran into challenges with sensor installation from the start.

Climate Considerations

One thing Wine learned was that biofuels aren’t very successful in his research location– there wasn’t enough water to support switchgrass. He says, “Most places here may have no precipitation recharge for a great many years. But there are sites, such as sub-humid environments, where you could get a whole lot of infiltration in a very short time.” In hindsight, Wine says he “would have increased his use of preliminary data to more efficiently determine the frequency of recharge events.”

Using Preliminary Data to help Site Instrumentation

Wine learned that it’s important to think about the time constant of your system when siting instrumentation and that preliminary data are crucial. He says, “Before sensor installation, I did a chloride mass balance which helped me determine where I should install the lysimeters.” He had been planning to put them at watersheds at the USDA-ARS Southern Plains Range Research Station, but the chloride mass balance showed there hadn’t been a recharge event at that site in the past 200 years. So he chose to install the lysimeters at the USDA-ARS Southern Plains Experimental Range, located in the Beaver-North Canadian River Alluvial Aquifer, a site with coarser soil and higher permeability.

Wine also thinks numerical modeling could have been useful in determining placement. “In siting the instruments, numerical modeling would’ve been a big help because we could have predicted the likelihood and frequency of recharge events. So I think preliminary data, numerical modeling, and environmental tracers can all help in terms of where to place these research devices.”

a baby calf walking towards the photographer with other cows, who are collectively walking through a field

After long absences, Wine often had to repair damage caused by cattle.

Proximity to Research Site

Another challenge was that the researchers were located in Stillwater, Oklahoma, far from their research site. The experiment was protected by fences, but after long absences, Wine often had to repair damage caused by cattle. “I really need to hand it to these instruments that can be trampled numerous times by cows and the battery compartment filled up with water,” Wine says. “They just needed to be dusted off, dried out, new batteries inserted, and they worked great.” Wine adds that researchers need to consider the distance between their office and their research site because in his case, the cows would have been less of an issue if it had been a fifteen-minute drive instead of three hours each way. He adds, “Selecting a nearby research site would have allowed us additional flexibility in our experimental methods; for example, with a nearby site we could have more easily conducted artificial rainfall simulations if a particular year turned out to be too dry for natural recharge events to occur.”

Proper Siting of Equipment Makes a Difference

Once Wine determined the correct placement of his instruments, he was finally able to get some interesting data. He says, “There are large pulses of focused recharge that do occur in certain places, and we quantified one of those pulses following a storm with one of the lysimeters. We’ve got about a year’s worth of data. Since we installed lysimeters at adjacent upland (diffuse recharge) and lowland (concentrated recharge) sites, we succeeded in observing large differences between the recharge fluxes at these nearby sites.” Wine’s plan is to see if he can replicate the results of the lysimeter experiment using numerical modeling, because he says, “the data look reasonable, but I’d like to confirm the measurements due to the cows playing havoc with our site.” Wine is excited as these lysimeters are yielding the first direct physical measurements of diffuse and concentrated groundwater recharge in the Beaver-North Canadian River Alluvial Aquifer, and he’s optimistic that his numerical modeling will match this unique time series of direct physical measurements of groundwater recharge.

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Jul 18

Should We Replace “Wind Chill Factor”?

In a continuation of our series, based on this book, which discusses scientific ideas that need to be reexamined, Dr.’s Doug Cobos and Colin Campbell make a case for standard operative temperature to replace wind chill factor:

Currently, the forecast is based on air temperature and wind chill. What the forecast leaves out is the effect of radiation.

What are we looking for when we look at a weather forecast? We want to know how we’re going to feel and what we need to wear when we go outside. Currently, the forecast is based on air temperature and wind chill, which are a major part of the picture, but not all of it. What the forecast leaves out is the effect of radiation. If you go out on a cold, sunny day, you’re going to be warmer than you would be at that same temperature and wind speed on a cloudy day. It’s not going to feel the same. So why not replace wind chill with the more accurate measurement of standard operative temperature?

Where wind chill came from:

In 1969, a scientist named Landsberg created a chart showing how people feel at a certain air temperature and wind speed. His chart was based on work by Paul Siple and Charles Passel. But, Siple and Passel’s work was done in Antarctica using a covered bottle of water under the assumption that you were wearing the thickest coat ever made. The table was updated in 2001 to improve its accuracy, but since the coat thickness assumption remained unchanged it underestimates the chill that you feel. It also explicitly leaves out radiation, assuming the worst case scenario of a clear night sky. The controversy is detailed in this NY Times article from several years ago.

Ice covered lake with the sun reflecting off the surface, a bench in front of the lake in the snow with a person walking next to it

Siple and Passel’s work was done in Antarctica using a covered bottle of water under the assumption a person was wearing the thickest coat ever made.

During the winter, forecasters use air temperature and wind chill with no radiation component. In the summertime, they use an index that takes into account the temperature and the humidity called the heat index. But again, there is no accounting for radiation. Our families deal with this all the time when we take the kids out fishing in early spring. Before we leave, we’ll check the weather report for temperature and wind chill. But is it going to be sunny or cloudy? That’s key information. You can see the radiation effect in action when a cloud drifts in front of the sun. All the kids scramble for their jackets because the perceived temperature has changed. This is something that none of the indices actually capture.

Understanding the concept:

Standard operative temperature combines the effects of radiation and wind speed to give a more complete understanding of how you will feel outside. It is a simple energy balance: the amount of energy coming in from the sun and metabolism minus the amount of energy going out through heat and vapor loss. Using this relationship and adding in the heat and vapor conductances, the temperature that we might “feel” can be graphed against the solar zenith angle at a fixed air temperature. For reference, the sun is directly overhead when the zenith angle is 0 degrees and at the horizon at 90 degrees.

Wind Chill and standard Operative temperature chart

Figure: Wind chill and standard operative temperature with respect to sun angle for two wind speeds (1 and 10 m/s) at an air temperature of -5 degrees C.

What’s interesting is that on a clear day when the sun is around 45 degrees (typical for around noon in the winter) and the temperature is -5 degrees C, if the wind is blowing at 1 m/s, you would feel a temperature of 6 degrees C (relatively warm). The wind chill predicts the feel at -6 degrees C, a huge difference in comfort. This difference decreases with increasing wind speed as you’d expect, but even for the same conditions and wind at 10 m/s, the 45-degree sun angle creates a temperature feel 7 degrees C higher than the wind chill. Although not huge, this makes a considerable difference in perceived comfort.

What do we do now?

The interesting thing is that all the tools to measure radiation are there. Most weather stations have a pyranometer that measures solar radiation, and some of them even measure longwave radiation, which can also be estimated within reasonable bounds. This means forecasters have all the tools to report the standard operative temperature, which is the actual temperature that you feel. Why not incorporate standard operative temperature into each forecast? Using standard operative temperature we could have the right number, so we’d know exactly what to wear at any given time. It’s an easy equation, and forecast websites could use it to report a “comfort index” or comfort operative temperature that will tell us exactly how we’ll feel when we go outside.

Which scientific ideas do you think need to be reexamined?

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

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Jul 11

Great Science Reads: What our Scientists are Reading

We asked our scientists to share the great science reads they’ve perused recently. Here’s what they’ve been reading:

Letters to a Young Scientist by E.O. Wilson

Steve Garrity: E.O. Wilson is a leader in the science of biology. This book is a simple read. What I like most about it is that it very effectively conveys Dr. Wilson’s passion for science. His thoughts on what it takes to be a successful scientist resonated with me the most. In describing what it takes to be a successful scientist, E.O. Wilson says that being a genius, having a high IQ, and possessing mathematical fluency are all not enough. Instead, he says that success comes from hard work and finding joy in the processes of discovery. Dr. Wilson gets specific and says that the real key to success is the ability to rapidly perform numerous experiments. “Disturb nature,” he says, “and see if she reveals a secret.” Often she doesn’t, but performing rapid, and often sloppy, experiments increases the odds of discovering something new.

Out of the Scientist’s Garden by Richard Stirzaker

Lauren Crawford: “Richard Stirzaker is a scientist out of Australia committed to finding tools to make farming easier and more productive in third world countries. I love how he talks about what happens when he uses water from his washing machine on his garden and the unanticipated effects: what does the detergent do to the fertilizers and the soil properties? It’s a scientific view of how a garden works.”

Introduction to Water in California by David Carle

Chris Lund: “This is a great introduction to California’s water resources, from where the water comes from to how it is used….particularly relevant today given California’s ongoing drought and the hard choices California faces as a result.”

The Drunkard’s Walk: How Randomness Rules our Lives, by Leonard Mlodinow

Paolo Castiglione: “The Drunkard’s Walk’s beginning quote perfectly reflects the author’s thesis: “In God we trust. All others bring data!”. I enjoyed the author’s discussion on how the past century was strongly influenced by ideologies, in contrast to the present one, where data seems to shape people’s actions and beliefs.”

**Chapter 13 of An Introduction to Environmental Biophysics, by Gaylon Campbell**

Colin Campbell: “Because of teaching Environmental Biophysics class, all my focus has been on reading An Introduction to Environmental Biophysics. And, although I’ve read it too many times to count, I finally had a chance to study the human energy balance chapter (13) in depth, which was amazing. The way humans interact with our environment is something we deal with at every moment of every day; often not giving it much thought. In this chapter, we are reminded of the people of Tierra del Fuego (Fuegians) who were able to survive in an environment where temperatures approached 0 C daily, wearing no more than a loincloth. Using the principles of environmental biophysics and the equations developed in the chapter, we concluded that the Fuegian metabolic rate had to continuously run near the maximum of a typical human today. The food requirements to maintain that metabolic rate would be somewhere between the equivalent of 17 and 30 hamburgers per day (their diet was high in seal fat). You can read more about the Fuegians here.”

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Jul 5

A History of Thermocouple Psychrometry

Dr. Gaylon S. Campbell gives a short history on his involvement in the development of thermocouple psychometry:

A psychrometer measures wet and dry bulb temperatures of air in order to determine the relative humidity or vapor pressure.

The Original Psychrometers:

I started working with psychrometers in Sterling Taylor’s lab when I was a sophomore at Utah State University in 1960. A psychrometer measures wet and dry bulb temperatures of air in order to determine the relative humidity or vapor pressure. In a conventional psychrometer, a thermometer bulb is covered with a wet wick and measured to find the wet bulb temperature. A thermocouple psychrometer is used to measure the wet bulb temperature of air equilibrated with soil or plant samples. When a plant is at permanent wilting point, its relative humidity is close to 99%, so the whole range of interest for soil and plant measurements is between 99 and 100% RH. The measurements need to be very precise. To make a wet bulb we couldn’t use a wick. We made thermocouples from 0.001” chromel and constantan wires. We cooled the measuring junction of the wires by running a current through it (cooling using the Peltier effect), condensed dew on the wires through the cooling, and then read the wet bulb temperature by measuring the thermocouple output as the water evaporated. We needed to measure temperature with a precision of about 0.001 C.

Diagram of isopiestic psychrometer used to measure the water potential of plant tissue. Image: 6e.plantphys.net

A New Idea:

The original psychrometers we used in Dr. Taylor’s lab were single junctions mounted in rubber stoppers and placed in test tubes in a constant temperature bath. They were calibrated with salt solutions. Typically, before we could finish a calibration, we would break the thermocouple, so we never got data on soils. I found that frustrating, so had the idea of putting the thermocouple in a sample changer which would hold 6 samples. The sample changer went in the constant temperature bath. When it was equilibrated, we could make 6 readings without taking it out or opening it up. Calibration was done in one try, and we could start running soil or plant samples right away. This was a huge improvement. Our lab was one of a very few who could even make those measurements, and we could make them six at a time. That was about 1964.

Two New Businesses Born:

Later, when I was a graduate student at WSU, I started building soil psychrometers for my own research. Other researchers wanted them, so I taught Marv Sherman, a vet student friend to do the manufacturing, and we sold the psychrometers to whoever wanted them for the cost of his time plus materials. There was a sizable and growing demand when he and I graduated, and no one to carry on. My brother Eric came for my graduation. We asked him if he would like to take over the psychrometer business, and he said yes. We sent him home with some instructions and the materials we had left from Marv’s work. Eric built the business himself and then sold it to Wescor, where he and my brother, Evan became employees. I contributed ideas and helped Wescor grow for a few years, but Eric and Evan were not satisfied there and wanted to start a business of their own. We came up with the idea of them building a laser anemometer, and that was the start of Campbell Scientific.

Decagon’s retired SC10/NT3 thermocouple psychrometer

More Improvements:

When we were on sabbatical in England in 1977-78 I had access to a small machine shop and a machinist who was willing to make things for me. The sample changer psychrometers up to this time all had to be used in carefully controlled constant temperature water baths. However, the soil psychrometers that my brother, Eric, sold at Wescor worked fine with no temperature control. I suspected it would be possible to make a sample changer that didn’t need a constant temperature bath. I made some sketches and the machinist made it for me. It had places for 10 samples, had a large aluminum block to hold the rotor with the samples and the thermocouple, and stood on 3 legs. It worked fine without any temperature control.

I showed the new sample changer to my brothers at Campbell Scientific, and they set up and machined a couple of them. CSI didn’t have much interest in selling psychrometers, though, so Decagon began as a way for my children to earn money for college by selling the thermocouple psychrometer sample changer. The name Decagon came both from the 10 people in our family when we started and the 10 samples in the sample changer.

Thermocouple Psychrometry Fades into History:

Decagon (now METER) began selling the thermocouple psychrometer system in 1982 and updated the user-intensive calibration and measurement system to a much simpler one in the mid-1990s. Automation, speed, simplicity, and accuracy soon tipped the scales in favor of a dewpoint technique for measuring water potential, and the system was retired and replaced by a chilled mirror hygrometer (WP4C) in 2000. However, Dr. Campbell believes that thermocouple psychrometers may still have a role to play in measuring water potential. If you’re interested in water potential, check out our water potential pages. It puts many of our best water potential resources in one place and contains sections on theory, measurement methods, and history.

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Measuring and Modeling the Environment

Low Impact Design Structures

Infiltration and Recharge

Sensors Tell the Story

Rain Gauges Yield Surprises

Future El Niño Precipitation

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Did this book come from class notes or some other source?

What does Soil Physics with Python add to the conversation?

Will it be accessible across all disciplines?

What are the advantages of drones for researchers?

What are the obstacles to progress?

What needs to happen before drones reach their full potential?

A New Idea:

Strengths:

Learn more

Take our Soil Moisture Master Class

Aquifer Recharge

Climate Considerations

Using Preliminary Data to help Site Instrumentation

Proximity to Research Site

Proper Siting of Equipment Makes a Difference

Where wind chill came from:

Understanding the concept:

What do we do now?

Letters to a Young Scientist by E.O. Wilson

Out of the Scientist’s Garden by Richard Stirzaker

Introduction to Water in California by David Carle

The Drunkard’s Walk: How Randomness Rules our Lives, by Leonard Mlodinow

Chapter 13 of An Introduction to Environmental Biophysics, by Gaylon Campbell

The Original Psychrometers:

A New Idea:

Two New Businesses Born:

More Improvements:

Thermocouple Psychrometry Fades into History:

**Chapter 13 of An Introduction to Environmental Biophysics, by Gaylon Campbell**