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Weather Monitoring 101: Which Weather Station is Right for You?

Choosing the right weather station can be confusing. Hundreds of options exist for weather monitoring ranging from $200,000+ aviation-grade observation systems to $25,000 WMO-grade mesonet stations with redundant rain gauges and multi-height wind and temperature observations, all the way to $300 hobbyist-level stations.

Researcher setting up an ATMOS 41 all-in-one weather station
ATMOS 41 all-in-one weather station

How do you know which system is right for you? And what is the sweet spot for price vs. maintenance vs. accuracy for your unique application?

Understand your choices

  • Why you need weather data as an ancillary measurement, even if your primary measurement needs are in the soil or plant community
  • Why you should consider data quality vs. maintenance and measurement parameter combinations in your cost analysis
  • 3-season vs. 4-season performance 
  • Which situations require low-, medium-, or high-grade solutions, and how high should you go?
  • Pros and cons of different solutions
  • Where is the sweet spot for performance divided by price in your application?

In this 40-minute webinar, METER research scientist, Dr. Doug Cobos explores the research weather station price vs. utility continuum. Find out:

Watch the webinar—>

Presenter:

Dr. Cobos is a Research Scientist and the Director of Research and Development at METER.  He also holds an adjunct appointment in the Department of Crop and Soil Sciences at Washington State University where he co-teaches Environmental Biophysics.  Doug’s Masters Degree from Texas A&M and Ph.D. from the University of Minnesota focused on field-scale fluxes of CO2 and mercury, respectively.  Doug was hired at METER to be the Lead Engineer in charge of designing the Thermal and Electrical Conductivity Probe (TECP) that flew to Mars aboard NASA’s 2008 Phoenix Scout Lander.  His current research is centered on instrumentation development for soil and plant sciences.

Watch it now

Read more about which remote weather station is right for you.

Data deep dive: When to doubt your measurements

Dr. Colin Campbell discusses why it’s important to “logic-check” your data when the measurements don’t make sense.

Image of the Wasatch Plateau

Wasatch Plateau

In the video below, he looks at weather data collected on the Wasatch Plateau at 10,000 feet (3000 meters) in the middle of the state of Utah.

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Video transcript

My name is Colin Campbell, I’m a research scientist here at METER group. Today we’re going to spend time doing a data deep dive. We’ll be looking at some data coming from my research site on the Wasatch Plateau at 10,000 feet (3000 meters) in the middle of the state of Utah. 

Right now, I’m interested in looking at the weather up on the plateau. And as you see from these graphs, I’m looking at the wind speeds out in the middle of three different meadows that are a part of our experiment. At 10,000 feet right now, things are not that great. This is a picture I collected today. If you look very closely, there’s an ATMOS 41 all-in-one weather station. It includes a rain gauge. And down here is our ZENTRA ZL6 logger. It’s obviously been snowing and blowing pretty hard because we’ve got rime ice on this post going out several centimeters, probably 30 to 40 cm. This is a stick that tells us how deep the snow is up on top. 

One of the things we run into when we analyze data is the credibility of the data and one day someone was really excited as they talked to me and said, “At my research site, the wind speed is over 30 meters per second.” Now, 30 meters per second is an extremely strong wind speed. If it were really blowing that hard there would be issues. For those of you who like English units, that’s over 60 miles an hour. So when you look at this data, you might get confused and think: Wow, the wind speed is really high up there. And from this picture, you also see the wind speed is very high. 

But the instrument that’s making those measurements is the ATMOS 41. It’s a three-season weather station, so you can’t use it in snow. It’s essentially producing an error here at 30 meters per second. So I’ll have to chop out data like this anemometer data at the summit where the weather station is often encrusted with snow and ice. This is because when snow builds up on the sonic anemometer reflection device, sometimes it simply estimates the wrong wind speed. And that’s what you’re seeing here. 

This is why it’s nice to have ZENTRA cloud. It consistently helps me see if there’s a problem with one of my sensors. In this case, it’s an issue with my wind speed sensors. One of the other things I love about ZENTRA Cloud is an update about what’s going on at my site. Clearly, battery use is important because if the batteries run low, I may need to make a site visit to replace them. However, one of the coolest things about the ZL6 data logger is that if the batteries run out, it’s not a problem because even though it stops sending data over the cellular network, it will keep saving data with the batteries it has left. It can keep going for several months. 

I have a mix of data loggers up here, some old EM60G data loggers which have a different voltage range than these four ZL6 data loggers. Three of these ZL6s are located in tree islands. In all of the tree islands, we’ve collected enough snow so the systems are buried and we’re not getting much solar charging. The one at the summit collects the most snow, and since late December, there’s been a slow decline in battery use. It’s down. This is the actual voltage on the batteries. The battery percentage is around 75%. The data loggers in the two other islands are also losing battery but not as much. The snow is just about to the solar charger. There’s some charging during the day and then a decrease at night. 

So I have the data right at my fingertips to figure out if I need to make a site visit. Are these data important enough to make sure the data loggers call in every day? If so, then I can decide whether to send someone in to change batteries or dig the weather stations out of the snow. 

I also have the option to set up target ranges on this graph to alert me whether the battery voltage is below an acceptable level. If I turn these on, it will send me an email if there’s a problem. So these are a couple of things I love about ZENTRA cloud that help me experiment better. I thought I’d share them with you today. If you have questions you want to get in contact me with me, my email is [email protected]. Happy ZENTRA clouding.

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Soil sensors help solve putting green water distribution issues

Distribution of soil water in high-sand-content putting greens is a major concern for golf course superintendents. Gravel is commonly used as a component of a sand-based root zone to increase moisture retention, but due to gravity, the contour and slope of a putting green significantly affect moisture retention. Coarse-textured soils often become too dry in higher elevations and too wet in lower elevations. This hampers performance and increases water and labor inputs. 

Image of a golfer putting on a putting green at a golf course
The contour of a putting green affects moisture retention


To fix this problem, Thomas Green, a graduate student at Michigan State University, and a team of researchers are assessing the impact of gravel layer particle size and slope on soil water content in a variable-depth, high-sand content root zone.  He says, “Due to lack of published research and the USGA’s wide-ranged specification for gravel selection based on the root zone material, determining the optimal bridging, filtering, permeability, and uniformity factors capable of increasing root zone soil moisture uniformity is critical.”

Validating previous turfgrass experiments

Green and his team set out to validate previous turf experiments done at MSU which showed that increasing the particle size difference between the gravel and root zone (sand) layers, in combination with a variable-depth root zone (shallower at the slope apex, deeper at the slope base) would improve soil moisture uniformity. 

He says, “We wanted to retain this moisture consistently throughout the whole profile over the entire green. Our experiments decreased the root zone depth in relation to our gravel layer. So at the peak, we reduced the root zone, and in the valleys, we increased the root zone to eliminate wet spots where water accumulates.”

Water potential is the key

Green says the goal was to manipulate the “head” (or water potential) in the peaks and valleys. He explains, “We tested particle size differences between a high-sand, root-zone mix and the gravel layer. Past studies show that the greater the difference between the root zone particle size and the gravel particle size, the more water is retained at the interface. Essentially in the valleys, we increased the depth of the sand layer to create (in physics terms) a large head that forced more water to drain. At the top of the green, we did the opposite and made a thin layer of sand so more water was available. Basically, it was all about manipulating the water potential or tension on the water to retain the right level of moisture.”

The diagrams below illustrate the physics of how this works:

Diagram of sand and gravel layers in a putting green
Figure 1. Diagram of sand and gravel layers in a putting green

In Figure 1, the gravel provides a textural barrier where pores must be saturated for water to move into the gravel.

Close up diagram of tall sand layers in the valley
Figure 2. Closeup of tall sand layer in the valley

Figure 2 is a closeup of the tall layer. Cohesion of water molecules together and adhesion to soil particles ties water together and exerts downward force or tension on water at the top of the profile. The larger the height from the top of the profile to the saturated surface, the more tension on the water (lower water potential).

Close up diagram of short sand layer at the peak
Figure 3. Closeup of short sand layer at the peak

Figure 3 is a closeup of the short sand layer. Shorter height above the saturation zone reduces the tension in the top layer of soil (higher water potential). Thus, the high part of the green with the thinnest sand layer will have less tension and more water than the thick layer in the lower part of the green. To visualize what soil tension is like, think of people hanging on people (Figure 4). The more people there are, the more “pull” will be exerted on the top person.

Diagram of a comparison of soil tension to people hanging on people
Figure 4. Soil tension is like people hanging on people. The more people, the more pull exerted on the top person.

Eliminating edge effects

Green used METER soil moisture and temperature sensors at three different depths along with METER data loggers to validate that the water was in the right place. He inserted the sensors into an enormous box that mimicked a putting green. “I created a 4-ft x 4-ft module to simulate a sloping green. I had to figure out how large it should be to eliminate edge effects (water preferentially moving toward the container edges). The soil moisture sensor helped me determine just how large this box had to be to get accurate measurements.”

Green says the surface measurements were the most important, “I was interested in that top depth because in a golf setting, that’s where you need to control moisture. In a putting green, turfgrass roots aren’t very deep because the grass is so short.”

USGA has adopted the new method

Green says the results turned out as expected. “We expected that if we increased the gravel particle size difference and reduced sand depth, we would see increased water retention in our root zone profile, and that’s exactly what happened. The great thing is the USGA has now somewhat adopted these new recommendations. More and more golf courses are going to this construction method. It’s good for the industry because they’re conserving water.”

In the future, Green says he’d like to explore some research done by F.W. Taylor in the early 1900s. Taylor thought about using a vertical sand or gravel strip contoured on a slope to form a barrier to water moving downhill instead of plastic or polyethylene. This idea is illustrated beautifully in the classic 1950s era film by Dr. Walter Gardner.

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Founders of Environmental Biophysics: Walter Gardner

Visualizing water flow in soil

This week, in our “Founders” series, we highlight a soil physicist.

Image of soil being held in a researchers hand

Water movement in soil defies intuition

When Dr. Walter Gardner passed away in June (2015), many viewed the film Water Movement in Soils as one of the main accomplishments of a remarkable career. Dr. Gardner and Jack Hsieh made the film in 1959 at Washington State University. The technology they used was impressive—it was years before advanced electronics would make time lapse movies routine—but Dr. Gaylon Campbell finds the ideas behind the experiments even more remarkable.

“Once you’ve seen the film, you can go back to the unsaturated flow theory and see how it would work,” Campbell says, “but the ideas aren’t really obvious. I wish I knew how he thought of doing that.”

At one point in the film, Gardner himself says that the phenomena he illustrates in the film can be seen in nature “if one observes carefully.” It’s possible that some of these careful observations were made in the fields around Washington State University, where farmers often turned the surface layer of soil over using a moldboard plow. This created a layer of surface soil with a layer of straw underneath it—exactly the conditions Gardner describes in the film as leading to erosion, reduced water in the root zone, and damage to the soil in the plow zone.

Though agriculture was the obvious target of the film, for a while it was also a big hit with the US Golf Association. Golf greens are mown short and get a lot of abuse. They need to be watered and fertilized heavily, but how do you keep enough water on the plants between irrigations without leaching nutrients out of the root zone? Water Movement in Soils provides a perfect answer. Gardner consulted for the USGA and used his film to train people who designed and constructed the greens.

Water movement in soil defies intuition

Our intuitions about how water moves in soil are often wrong. More than fifty years after it was made, this classic film still has the power to help people understand what’s really going on.

Watch the video

 

Learn more

Download “The researcher’s complete guide to soil moisture”

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Best of 2019: Environmental Biophysics

In case you missed them, here are our most popular educational webinars of 2019. Watch any or all of them at your convenience.

Lab vs. In Situ Water Characteristic Curves

Image of a researcher running hand across wheat

Researcher Running A Hand Across Wheat

Lab-produced soil water retention curves can be paired with information from in situ moisture release curves for deeper insight into real-world variability.

Watch it here—>

Hydrology 101: The Science Behind the SATURO Infiltrometer

Image of a fallen tree being supported off the ground by many other trees

A Forest With Fallen Trees

Dr. Gaylon S. Campbell teaches the basics of hydraulic conductivity and the science behind the SATURO automated dual head infiltrometer.

Watch it here—>

Publish More. Work Less. Introducing ZENTRA Cloud

Image of a researcher collecting information from a ZL6 data logger

Researcher is Collecting Data from the ZL6 Data Logger

METER research scientist Dr. Colin Campbell discusses how ZENTRA Cloud data management software simplifies the research process and why researchers can’t afford to live without it.

Watch it here—>

Soil Moisture 101: Need-to-Know Basics

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.

Watch it here—>

Soil Moisture 201: Moisture Release Curves—Revealed

Image of rolling hills of farm land

Rolling Hills of Farm Land

A soil moisture release curve is a powerful tool used to predict plant water uptake, deep drainage, runoff, and more.

Watch it here—>

Soil Moisture 301: Hydraulic Conductivity—Why You Need It. How to Measure it.

Image of a researcher measuring with the HYPROP balance

Researcher measuring with the HYPROP balance

If you want to predict how water will move within your soil system, you need to understand hydraulic conductivity because it governs water flow.

Watch it here—>

Soil Moisture 102: Water Content Methods—Demystified

Image of a researcher holding a TEROS 12 in front of a field

Modern Sensing is more than just a Sensor

Dr. Colin Campbell compares measurement theory, the pros and cons of each method, and why modern sensing is about more than just the sensor.

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Soil Moisture 202: Choosing the Right Water Potential Sensor

Image of a dirt plowed field being used for electrical conductivity

Electrical Conductivity

METER research scientist Leo Rivera discusses how to choose the right field water potential sensor for your application.

Watch it here—>

Water Management: Plant-Water Relations and Atmospheric Demand

Dr. Gaylon Campbell shares his newest insights and explores options for water management beyond soil moisture. Learn the why and how of scheduling irrigation using plant or atmospheric measurements. Understand canopy temperature and its role in detecting water stress in crops. Plus, discover when plant water information is necessary and which measurement(s) to use.

Watch it here—>

How to Improve Irrigation Scheduling Using Soil Moisture

Image of a crop field

Capacitance

Dr. Gaylon Campbell covers the different methods irrigators can use to schedule irrigation and the pros and cons of each.

Watch it here—>

Next up:

Soil Moisture 302: Hydraulic Conductivity—Which Instrument is Right for You?

Image of plants growing out of the sand

Leo Rivera, research scientist at METER teaches which situations require saturated or unsaturated hydraulic conductivity and the pros and cons of common methods.

Watch it here—>

Image of grapes growing off of a tree

Predictable Yields using Remote and Field Monitoring

New data sources offer tools for growers to optimize production in the field. But the task of implementing them is often difficult. Learn how data from soil and space can work together to make the job of irrigation scheduling easier.

Watch it here—>

Learn more

Download “The researcher’s complete guide to soil moisture”

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Chalk talk: How to calculate absolute humidity

In this video, Dr. Colin Campbell discusses how to use air temperature and relative humidity to calculate absolute humidity, a value you can use to compare different sites, calculate fluxes, or calculate how much water is actually in the air.

Depicting vapor and humidity coming off of the ground

Vapor density tells you how much water is actually in the air.

Watch the video to find out how to calculate absolute humidity and how to avoid a common error in the calculation.

 

Video transcript: Absolute humidity

Hello, I’m Dr. Colin Campbell, a senior research scientist here at METER Group, and also an adjunct faculty at Washington State University where I teach a class in environmental physics. Today we’re going to talk about absolute humidity. In a previous lecture, we discussed how relative humidity was a challenging variable to use in environmental studies. So, I’m going to show the right variable to use as we talk about humidity. 

Absolute humidity can be talked about in terms of vapor pressure, which is what I’m used to, or in terms of vapor density. Whatever we use, we usually start by calculating this from a relative humidity value and a knowledge of air temperature. In my relative humidity lecture, I said that Hr (or the relative humidity) was equal to the vapor pressure divided by the saturation vapor pressure. And in most field studies, we’d typically get a report of the air temperature and the relative humidity. So how do we take those two values and turn them into something we could use to compare different sites, calculate fluxes, or calculate how much water is actually in the air? We’ll need to work through some equations to get there. I’m going to take you through it and give you an example so that you know how to do that calculation.

Vapor pressure

First, we’ll talk about absolute humidity in terms of vapor pressure or Ea. If we rearrange this equation here (very simple math), the relative humidity times the saturation vapor pressure will give us our vapor pressure. And that vapor pressure is now an absolute humidity. How would we do this? Well, let’s first talk about an example in terms of vapor pressure. 

Let’s say a weather report said the air temperature was 25 degrees Celsius and the relative humidity was 28% or 0.28. First, we’d use Teten’s formula which I talked about in the previous lecture. We’d say the saturation vapor pressure at the air temperature is equal 0.611 kPa times the exponential of a constant times the air temperature divided by another constant plus the air temperature. So in our case, the air temperature is 25 degrees, which we’ll add here. Remember saturation vapor pressure is a function of 25. So 0.611 kPa times the exponential of 17.502. In the previous lecture, I showed you that b value times 25 degrees divided by the c value 240.97. And then we add to that 25 degrees (this is for liquid water, of course, it’s 25 degrees Celsius because nothing’s frozen). If you were working over ice, these constants would be different. So we put this into our calculator or into a spreadsheet, and we easily calculate the saturation vapor pressure at 25 degrees C is 3.17 kPa. But we’re not done yet. 

We have to go back to this equation that says the vapor pressure is equal to the relative humidity times the saturation vapor pressure. When we plug our data in, the relative humidity 0.28 times the saturation vapor pressure that we calculated right here, we get a vapor pressure of 0.89 kPa. And if we were calculating fluxes (we’ll talk about that in another lecture), this is typically the value we would use. But there are other things we can do with the absolute humidity values that might be useful.

Vapor density

So let’s talk about vapor density. If we had a certain volume of air, and we wanted to know how much water was in that volume of air (for example, if we were going to try to condense it out) we’d more typically use this vapor density value. But how do we get from a vapor pressure that we can easily calculate from a weather report to a vapor density that would allow me to know how much water was actually in the air? 

This is our equation that says the vapor pressure times the molecular weight of water divided by the universal gas constant times the kelvin temperature of the air will give us the vapor density. So I’ll take you through an example here, just continue on the one we’ve already done, just so you can see how to calculate it and to avoid a pretty common misstep. 

How to avoid a common error

Again, molecular weight of water is 18.02 g/mol. The universal gas constant R is 8.31 J/mol K. And here’s the kelvin temperature of the air. I’ve scribbled this in a little bit. That’s how I note the difference between something like this, which would be air temperature in Celsius and this air temperature in kelvin. So let’s go ahead and plug all these into our equation. There’s our vapor pressure. We’re just dragging that over here. There’s our molecular weight of water. There’s our universal gas constant. And here is the kelvin temperature of the air. So as we look at this, you immediately say, how do I cancel these units? The kilopascals and the joules are certainly not going to cancel as they are. But there are conversions we can use. A Pascal is equal to an Nm-2, and a joule is equal to an Nm.

So if we change this joule to an Nm, we change this Pascal to Nm-2, we have to pay attention here as we’re doing it that the kilo right there, don’t forget that because that can mess you up. So I’m circling that to make sure that we’ve got this. Now we cancel our N’s, and combining together we get a m-3. That’s what we’re hoping for on the bottom. The grams come out on top, they don’t cancel, but everything else does. The mols cancel mols, the kelvin cancels the kelvin there, and the N cancels the N. 

And we come out with just what we were looking for, save one thing, which is a kg/m-3. And this calculation gives us point 0065. But since we actually want to do this in grams, because that’s more typical of what you find in how much water there is in air. It’s not a kg of water, but more in terms of g/m-3 of water, we get 6.5 g/m-3

Check your calculation

One way you can check this calculation (just as a rule of thumb), is if we had a pressure of the air of 100 kPa and a temperature of 20 degrees Celsius, the multiplier to get from your vapor pressure to your vapor density is about 7.4 or so. We’ll just say around 7.0. And we’ll do a quick mental calculation, 0.89 times 7, that should give us something around 6.0. So our answer should be around 6.0, and it is. It’s certainly no orders of magnitude off. So we’ve got at least close to the right answer, by doing a mental check, and we can say this conversion works. 

If you want to learn more about instrumentation to measure all kinds of atmospheric parameters, please come to our website, www.metergroup.com or you can email me to chat more about this: colin.campbell@metergroup. com. 

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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.

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What plant hydraulics tell us about drought response

Trees are merchants; they sell water to the atmosphere in exchange for the CO2 they need to photosynthesize sugars. The exchange rate or ‘water-use efficiency’ that drives the plant carbon-water market place is a function of atmospheric CO2 concentrations. Thus, theoretically human carbon emissions, which have increased atmospheric CO2 by 40% since 1850, should increase plant water use efficiency, resulting in “CO2 fertilization” of our forests and crops. 

La Plata mountains

La Plata Mountains, where the study gradient is located

However, evidence for CO2 fertilization is extremely mixed. That’s why Leander Anderegg, postdoctoral fellow at UC Berkeley, and his research team are performing a two-step experiment to determine if increased atmospheric CO2 conditions increase plant water-use efficiency. The team is leveraging a natural elevation gradient in temperature, vapor pressure deficit, and precipitation on a southwestern Colorado mountain to understand:

  1. How much physiological variation is on a single mountain slope between two species
  2. How that variation ultimately affects the potential for CO2 fertilization and differential vegetation responses to rising CO2

For five years, the team has worked on quantifying how two tree species (Ponderosa Pine and Trembling Aspen) can shift their physiology going from low elevation (hot, high vapor pressure deficit environments) up to high elevation (wet, cooler, low vapor pressure deficit environments). They want to understand what that means for each species’ water relations, drought vulnerability, and biogeography in a drying and warming climate.

Quantifying weather parameters  

Anderegg and his team use METER all-in-one weather stations to quantify exactly how much the local environment changes from the bottom to the top of the mountain. Anderegg says he’s been surprised at how influential vapor pressure deficit changes are on the tree species. He says, “When we compare Aspens to Ponderosas, we’ve found that the difference in atmospheric demand is a big part of the story, particularly in how they respond to drought stress. There is more atmospheric demand at the bottom of the mountain. So one key objective was to quantify how much drier the air was during the peak mid-summer dry down for most of the species. This was critical then to infer how stomata were responding to that gradient and water stress. It’s really difficult in these wide field plots to actually measure transpiration. But with physiological measurements of leaf water potential, hydraulic conductivity, and the vapor pressure deficit from relative humidity sensors, we could then infer how open the stomata were.”

Image of a researcher coring a Ponderosa to measure its growth rate

Coring a ponderosa to measure its growth rate

Anderegg used a pressure chamber to measure leaf water potential and also did a lot of shotgun sampling to measure the hydraulic conductivity in twigs. He describes the process, “I went out at 3 am with a 20 gauge shotgun loaded with birdshot to shoot off branches. We pulled water through the branches by applying a vacuum to a pressure chamber and then inserting one end of the branch. To get the hydraulic conductivity of the branch, we measured how quickly the water moved into the chamber.” (Kolb et al 1996)

Vapor pressure deficit was surprising

Anderegg says vapor pressure deficit changes across the elevation gradient were much stronger than he expected. He says, “It was pretty impressive that as you drove up this elevation gradient, the vapor pressure deficit differed by approximately 1.5 kPa. In the crop realm, a vapor pressure deficit of 2 kPa is pretty intense, but we went from a bit over 1 kPa near the top of the mountain to more like 3 kPa down at the drier bottom, which translates to a remarkably different water-use efficiency. 

Two species—two adaptation strategies

When asked what they’ve learned, Anderegg says the difference between the two tree species is pretty amazing. “We’ve seen that the two species have extremely different responses to drought stress. Aspen keeps its stomata open, even at the bottom of the mountain where it’s really dry. It just alters its hydraulic system to try and keep up with it. The Ponderosa, however, does not alter its hydraulic system. It just closes its stomata until it rains in the fall.”

A dead Aspen tree next to a living Ponderosa tree

An aspen that died following a drought while it’s neighboring ponderosa lived

Anderegg adds that the two different water relations strategies line up with the type of biogeographical shifts occurring in the two species as the Southwest dries out. He says, “Aspen is sort of a ‘grin-and-bear-it’ species that toughs out drought while Ponderosa is a ‘sit it out’ sort of species. For the last 15 years, the Aspen have been creeping uphill but not gradually. Intermittent droughts are slowly trimming the driest Aspen up the hill in fits and starts. Ponderosa are better at dealing with extreme droughts because they preserve their hydraulic systems. We have not seen mortality pushing the Ponderosa uphill. However, there’s essentially no Ponderosa recruitment (new tree starts) at the bottom of the hill, and the growth rates of adults are a quarter of the rates at the top of the hill. So we think the Ponderosa will move uphill following mean climate change and not in fits and starts. They’ll gradually die off at the bottom and not be replaced by young recruits which will cause them to move uphill in a more gradual manner.”

Dying Aspen tree in the middle of a low elevation Aspen stand

Dying aspens in the middle of a low elevation aspen stand

Transitioning toward the future

In the years ahead, Anderegg hopes to move into the second phase of the experiment: testing how these two species will respond to CO2 fertilization. He says, “We need to make these measurements over multiple years and many environmental conditions to start to get at how much plasticity any individual plant can manifest (plasticity is the amount that a plant can change its physiology in response to climate change. So if this condition happened, how likely is the plant to respond in a particular way over time) and what the long term trajectories are in these hydraulic traits. We’ve gotten measurements at the height of a significant drought and then another medium year following that drought. We want to transition toward a long-term monitoring perspective that hopefully will give us the information we need to start thinking about how CO2 then plays in.” 

You can learn more about Leander Anderegg’s research here: ldlanderegg.com

Reference

Kolb KJ, Sperry JS, Lamont BB (1996) A method for measuring xylem hydraulic conductance and embolism in entire root and shoot systems. Journal of Experimental Botany. 47:304, pg 1805-1810

See ATMOS 41 weather station performance data

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Download “The researcher’s complete guide to Leaf Area Index

Chalk Talk: Why is Humidity Relative?

Dr. Colin Campbell, a senior research scientist at METER Group, as well as adjunct faculty at Washington State University teaches about relative humidity.

Image of a forest with clouds and fog everywhere
Comparing RH at different research sites can be a challenge

Watch the video to find out why we use the term relative humidity and why comparing RH at different research sites can be a challenge.

 

Video transcript

Why is humidity relative?

Hi, I’m Dr. Colin Campbell. I’m a senior research scientist here at METER Group, as well as adjunct faculty up at Washington State University. And I teach a class in environmental biophysics. And today, we’re going to be talking about relative humidity. Have you ever looked at a weather report and wondered, what do they mean by the term relative? Why aren’t we talking about absolute things? And so today I’m going to talk about what is relative humidity? Well, relative humidity we’re going to define here as just hr. And hr is equal to the partial pressure of water vapor in air divided by the saturation vapor pressure or the maximum possible partial pressure of water in air as a function of temperature. So this is relative because anytime we have a partial pressure of water vapor, we’re always dividing it by the maximum possible water vapor that could be in the air at any point.

Comparing RH at different sites is a challenge

So, why would relative humidity be such a challenge for us as scientists to use in comparing different sites? I wanted to talk about that so we can focus in here on this saturation vapor pressure. Over here we have Tetens equation. This says that the saturation vapor pressure, which is a function of air temperature is equal to 0.611 kPa times the exponential of a constant “b” times the air temperature divided by another constant “c” plus the air temperature. So at any point, depending on the air temperature, we can calculate the saturation vapor pressure, and then we can put it back into this equation and get our relative humidity. There are two situations we might think about for calculating our saturation vapor pressure. The most typical is this one: where that constant “b” is 17.502 degrees C. And the constant “c” is 240.97 degrees C (the units on this are degrees C, so these will cancel). If we’re over ice, those constants will be different: “b” would be 21.87 degrees C and “c” would be 265.5 degrees C. 

So as I mentioned, relative humidity is a challenging variable to use in research because while vapor pressure (ea) (the vapor pressure of the air) is somewhat conservative across a day, the saturation vapor pressure (with respect to air temperature), this changes slowly with temperature across the day. So if we graphed temperature on one axis and the relative humidity on the other axis, we might during a typical day have a temperature range that looks somewhat like this. And even if the actual vapor pressure “ea” wasn’t changing, we’d see a relative humidity trend that looked like this: only changing because of air temperature. And because of that, if we wondered how do I compare the water in the air at one research site, for example, with the water in the air at another research site? We might be inclined to average them. But because of this trend, the average of the relative humidity at any site tends to be around 0.60 to 0.65 and therefore will be totally irrelevant in the literature. 

So we need to speak in absolutes, and in my next lecture, I’m going to go into what we can do to calculate that absolute relative humidity. If you want to know more about making measurements in the atmosphere, go to metergroup.com, look at our atmospheric instrumentation, and you can learn more from there.

Download “The researcher’s complete guide to soil moisture”

Download “The researcher’s complete guide to water potential

The effects of environmental change on carbon cycling across the semi-arid west

Meet Christopher Beltz: G.A. Harris Fellowship winner

Increased nitrogen availability has the potential to alter many ecosystem functions—and is doing so already. This is due to the widespread response of net primary productivity (biomass) and soil respiration to increased nitrogen inputs into the biosphere.

Image of the grass lands with the sun setting

Grass Lands

Increases in nitrogen inputs are responsible for the acidification of soils, streams, and lakes and can affect forest and grassland productivity. Former G.A. Harris Fellowship winner, Christopher Beltz, a PhD student at Yale University, and his research team are examining two major drivers of carbon cycling: water and nitrogen. They want to understand the degree of limitation by both of these factors in the semi-arid ecosystems of the western United States and if that limitation changes by specific function.

Inspired by a mitigation pilot project

Beltz decided to study the effects of increased nitrogen on biomass after learning about the initiation of a major energy development in a sagebrush steppe system which caused declines in a local mule deer herd. He says, “One hypothesis was that the development significantly reduced available winter range forage and also impacted the use of it as the animals moved more quickly through the noisy environment. They wanted to see if the widespread application of fertilizers would potentially offset the loss of biomass and increase the forage quality. In the end, it was clear that the effect of nitrogen fertilization alone would have minimal to no effect. However we also noticed some variability in the results and that this variability seemed to be related to precipitation.”

Image of a scientist watering a field plot

Scientist Watering Field Plot

Beltz thought that if he could control the water in a system in addition to nitrogen, the results might be more consistent. Thus, Beltz and his research team broadcast nitrogen over the soil at three semi-arid grassland and shrubland/sagebrush sites in Colorado and Wyoming. He says, “The three sites essentially have a similar species list, annual precipitation, and annual temperature. However, temperature increases as you go south, and there are some differences in seasonality. The shrublands in the far north are the driest in the late summer which is typical of shrublands, where you see a large amount of precipitation occurring in the spring with a deficit in the summer. Larger taproots are beneficial in this system because they can access deeper water reservoirs.”

Measuring soil moisture improves understanding

The team used METER all-in-one weather stations, soil moisture sensors, and data loggers to monitor site conditions (i.e., precipitation, air temperature, soil moisture, and soil temperature) with high temporal resolution. Beltz explains, “We monitored soil moisture to understand whether our treatments were having any effect. We needed to know if the treatments actually altered the soil water conditions. With soil sensors in the ground, we could monitor that. We also monitored precipitation at the site level because of the fine scale spatial heterogeneity of precipitation in these systems. We weren’t confident we could obtain this with interpolation or modeling; we wanted site-specific values.”

Beltz uses this and other data to understand the interactive effects of nitrogen and water and also changes in water and nitrogen concentrations. He says, “We do a classic full-factorial manipulation outdoors. We perform the exact same manipulations with the same timing at each site. We measure a whole suite of variables that range from ecosystem structure to ecosystem function. This includes soil respiration, plant community, soil microbial communities (fungal and bacterial) using next-generation sequencing. We look at pools of soil carbon, and we do some fractionation so we can get at more labile and recalcitrant carbon compounds.”

METER weather station, ZL6 data logger, and soil moisture sensors

METER Weather Station, ZL6 Data Logger, and Soil Moisture Sensors to Receive and Process Data

Beltz says that monitoring soil moisture at multiple depths is important. “Our soil samples come from the same depths as the sensors so we can differentiate depth when we look at changes in bacterial or fungal composition. We then try to tie that to temperature and moisture. In 2018, we added an additional set of soil moisture sensors in our water treatment so we could start to quantify the effect in the soil depth that those water treatments were having. This helped explain a lot of what we were seeing.”

Nitrogen or water: which is the driver?

Beltz says the analyses are ongoing, but what they’ve learned so far is that an application of water equivalent to 12 millimeters precipitation penetrates to 10 centimeters of depth, and the effect of that application lasts three to seven days at all of their sites. He says, “Last year, we had an unseasonably large amount of precipitation at our northerly site. So for most of the season, the water treatments and the controls were identical in terms of water availability. That was a very helpful context for us because we started to see things that did not match the expected patterns.”

Looking at the big picture, he adds, “What’s come out of this is not what anybody expected. One major finding, at least in the initial analyses at two of our sites, is that it’s really the combined treatment of increased nitrogen and water that has the effect. This is not necessarily surprising in some ways, however it is the widespread lack of response of any other treatment combination that is extremely interesting.”

What it all means

Beltz sums up the implications of his research like this: “We know water availability and precipitation will shift globally due to climate change, as well as nitrogen deposition and availability. Our research is trying to tease apart the effects of two factors, at least within the western United States, that we know are likely to cause changes to the structure and function of dryland ecosystems. As we start to look at carbon balance or shifts in function or species competition of plant communities, we are finding out that it’s the combined effect of increased nitrogen and water that will cause a more major change as opposed to just one or the other. It’s important that we integrate that combination into models that often do not account for both of these factors.”

Beltz says in the future he’s interested in continuing his work in the carbon/nitrogen cycle world, and he wants to look at integrating nitrogen and water into carbon balance modeling efforts.

You can read more about the first study mentioned, regarding nitrogen fertilization in the sagebrush steppe, which was published in PloS ONE: https://doi.org/10.1371/journal.pone.0206563

Find out about his research here: christopherbeltz.com or via Twitter @BeltzEcology

Now accepting applications: 2019 G. A. Harris Fellowship

The Grant A. Harris Fellowship provides $60,000 worth of METER research instrumentation (six $10,000 awards) to graduate students studying any aspect of agricultural, environmental, or geotechnical science.

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See ATMOS 41 weather station performance data

Learn more about measuring soil moisture. Download “The researcher’s complete guide to soil moisture“.

To understand how soil moisture and soil water potential work together, download “The researcher’s complete guide to water potential.”

Data deep dive: why am I seeing diurnal changes in soil moisture?

In the video below, METER soil scientist Dr. Colin Campbell discusses an often-misdiagnosed water content signal that looks like typical diurnal temperature cycling but is actually due to a phenomenon called hydraulic redistribution. He shows how easily these patterns can be seen in ZENTRA Cloud data management software.

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Learn more about measuring soil moisture. Download “The researcher’s complete guide to soil moisture“.

To understand how soil moisture and soil water potential work together, download “The researcher’s complete guide to water potential.”

Video transcript

Hello, my name is Colin Campbell. I’m a research scientist here at METER Group. And today we’re going to be digging into some water content data that I collected over the last summer. This is a field that’s planted in spring wheat, it’s about 700 meters across. And we’ve set up six measurement sites. At each one of these sites, we’re making several measurements, but the ones we’re going to talk about today are just water content. And while we’ve installed water content sensors at 15, 45, and 65 centimeters, we’re just going to focus on the 65-centimeter water content sensors. These sensors are the METER TEROS 12 soil moisture sensors, so they also measure electrical conductivity and temperature, and we’re going to look at temperature as well because that figures into this discussion. 

So this field was planted in April of 2019. And not a lot interesting goes on at the 65-centimeter depth through April, May, and June. But as we get into July, the wheat is reaching maturity, and they essentially are going to cut off the irrigation water here on July 22. So up to July 22, there’s really not a lot of movement in the water content. One of the sites decreases a little bit, but each line is flat. What I noticed as I was looking at this particular graph is after this long period of very flat data, after June 22 when the irrigation was cut off, we start to see some movement in the water content at this depth Not only is there movement down, but there’s a daily movement of the actual water content signals, all but this top light green line. And it made me wonder, what’s going on? 

Image of a field of wheat

Diurnal water content fluctuations are not always due to temperature.

Initially, whenever you see a diurnal movement, you suspect that it’s caused by temperature. It’s been said that every sensor is probably a temperature sensor first, and a sensor of whatever we’re really interested in second. In this case, we can look to see what the temperature is doing at that depth. Here’s soil temperature, at 65 centimeters, and even though there’s just a little bobble in the line, the line is almost completely flat. We see the seasonal trends in temperature, but really no diurnal temperature cycling. And this scale is also fairly small. So back to our 65-centimeter water content. If it’s not temperature that’s affecting these lines, then what is it? 

I’ve seen this before in an experiment that I did years ago in a non-irrigated wheat field. We were measuring down at  150 centimeters, and when the water had been used up in the upper levels of the soil profile, the roots of the wheat plant just simply went down to 150 centimeters and started taking water up. So this is what I assume is also happening here. The wheat has extended its roots down to 65 centimeters, since its irrigated wheat. That’s not too deep, but wheat doesn’t necessarily need to get its roots down super deep. And as the wheat accesses that water, we’re seeing these daily drops in water. But then we’re seeing just a slight increase in water. Here on July 28, we’re seeing that water go up slightly. And so why is this happening? We might understand how the water is being taken out of the soil, but why do we see a slight increase in the water content (just a few tenths of a percent)? 

What I think is happening, in this case, is that it’s not temperature, but actually, roots are growing down into this area, and they’re probably growing around the sensor. As we change from day to night, we see a release in the elasticity of the water in the xylem, and maybe just a little bit more water down in the roots as they’re the transpiration pull of the day is lessened and stops overnight. The stomates are closed, and we see just a little bit of water coming back into the roots and possibly into the soil. 

Now there was a big discussion many years ago about whether this was something called hydraulic lift where trees could take up water from deep in the soil profile and essentially give it back to plants near the surface. And although it was a great debate, it was never proven that this actually happened: water being spread from deeper locations to more shallow locations by roots. But this is probably hydraulic redistribution where we just have roots filling with water, and when they are filled, we see a little bit in the water content sensor.