Soil moisture data are useful, but they can’t tell you everything. Other strategies for growers and researchers, like plant and weather monitoring, can inform water management decisions.
In this webinar, world-renowned soil physicist, 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. Find out:
Why the Penman-Monteith equation, with the FAO 56 procedures, gives a solid, physics-based method for determining potential evapotranspiration of a crop
How the ATMOS 41 microenvironment monitor combined with the ZL6 logger and ZENTRA Cloud give easy access to crop ET data
How assimilate partitioning can be controlled by manipulating plant water potential using appropriate irrigation strategies
Why combining monitoring soil water potential with deficit irrigation based on ET estimates provide an efficient and precise method for controlled water stress management
Dr. Gaylon S. Campbell has been a research scientist and engineer at METER for over 20 years, following nearly 30 years on faculty at Washington State University. Dr. Campbell’s first experience with environmental measurement came in the lab of Sterling Taylor at Utah State University making water potential measurements to understand plant water status.
Dr. Campbell is one of the world’s foremost authorities on physical measurements in the soil-plant-atmosphere continuum. His book written with Dr. John Norman on Environmental Biophysics provides a critical foundation for anyone interested in understanding the physics of the natural world. Dr. Campbell has written three books, over 100 refereed journal articles and book chapters, and has several patents.
Measuring soil hydraulic properties like hydraulic conductivity and soil water retention curves is difficult to do correctly. Measurements are affected by spatial variability, land use, sample prep, and more.
Leo Rivera teaches soil hydraulic properties measurement best practices
Getting the right number is like building a house of cards. If one thing goes wrong—you wind up with measurements that don’t truly represent field conditions. Once your data are skewed in the wrong direction, your predictions are off, and erroneous recommendations or decisions could end up costing you a ton of time and money.
Get the right numbers—every time
For 10 years, METER research scientist, Leo Rivera, has helped thousands of customers make saturated and unsaturated hydraulic conductivity measurements and retention curves to accurately understand their unique soil hydraulic properties. In this 30-minute webinar, he’ll explain common mistakes to avoid and best practices that will save you time, increase your accuracy, and prevent problems that could reduce the quality of your data. Learn:
Sample collection best practices
Where to make your measurements
How many measurements you need
Field mapping tools
How to get more out of your instruments
How to use the LABROS suite to fully characterize soils (i.e., full retention curves and hydraulic conductivity curves)
Best practices for measuring field hydraulic conductivity using SATURO
In this chalk talk, METER Group research scientist, Dr. Colin Campbell, extends his discussion on humidity by discussing how to calculate vapor pressure from wet bulb temperature. Today’s researchers usually measure vapor pressure or relative humidity from a capacitance-based relative humidity sensor.
However, scientists still talk in terms of wet bulb and dew point temperature. Thus, it’s important to understand how to calculate vapor pressure from those variables.
Hello, my name is Dr. Colin Campbell. I’m a research scientist here at METER group, and also an adjunct professor at Washington State University where I teach a class on environmental biophysics. And today we’re going to be extending our discussion on humidity by talking about how using a couple of common terms related to humidity, we can calculate vapor pressure. The first term we’re going to talk about is dew point temperature. I’ve drawn a couple of figures below that illustrate a test I performed when I was a graduate student in a class related to biophysics.
Dew Point Temperature Test Illustration
The professor had us take a beaker of water and a thermometer and put ice in the beaker and start to stir it. The thermometers were rotating around in the glass, and our job was to look carefully and find out when a thin film of dew began to form around on the glass. So we watched the temperature go down, and at some point, we observed a thin film form onto that glass. At the point the film began to form, we looked at the temperature to get the dew point temperature, which means exactly what it says: the point at which dew begins to form.
This experiment wasn’t perfect because there is certainly a temperature difference between the inside of our glass where we’re stirring with the thermometer and the outer surface of the glass. But it was a good approximation and a great way to demonstrate what dew point temperature is. So we can say that the dew point temperature is the point at which the air is saturated and water begins to condense out. We call this Td or dew point temperature. The beautiful thing about dew point temperature is that if you know this value, you can easily calculate vapor pressure and even go on to calculate relative humidity, as I talked about in another lecture.
To calculate vapor pressure from our dew point temperature, we’ll call vapor pressure of the air, ea which is equal to the saturation vapor pressure (es) at the dew point temperature (Td) (Equation 1).
Equation 1
And as I discussed in my other lecture, the saturation vapor pressure is a function of the temperature (not multiplied by the temperature). It’s pretty simple to get the saturation vapor pressure at the dew point temperature. We simply use Tetons formula (Equation 2 discussed here), which says that the saturation vapor pressure at the dew point is equal to 0.611 kilopascals times the exponential of b Td over C plus Td (Td being the dewpoint temperature).
Equation 2
So let’s assume our dew point temperature is five degrees C. This is something you can find in many weather reports. If you look down the list of measurements carefully, it’s usually there. So the vapor pressure of the air (ea) is calculated by the formula I showed (Equation 1). Our first constant b is 17.502 and our second constant C, is just 240.97 degrees C. If we plug all the values into that equation, it ends up that our vapor pressure is 0.87 kilopascals.
Equations 3 a, b, and c
Now there might be a variety of reasons we want this value. We might want to use it to calculate the relative humidity. If so, we’d simply divide that by the saturation vapor pressure at the air temperature. Then we’d have our relative humidity. More commonly we use the ea and the saturation vapor pressure at the air temperature to calculate the vapor deficit. So possibly in some agronomic application that might be interesting to us. So that is dew point temperature.
Now we’ll talk about another common measurement, our wet bulb temperature. This was much more common in past years where there weren’t electronic means to measure things like dew point or humidity sensors. And we used to have to make a measurement of humidity by hand. And what they did was to collect a dry bulb temperature or a standard air temperature. And that dry bulb temperature (or the temperature of the air) was compared to what we call a wet bulb temperature.
Wet Bulb Temperature
Researchers made this wet bulb temperature by putting a cotton wick around the bulb of the thermometer. This was just a fabric with water dripped onto it. Once that wick is saturated with water, the water begins to evaporate, and they would use wind to enhance that evaporation. For example, some instruments had a small fan inside that would blow water across this wick, or more commonly, two temperature sensors were attached on a rotating handle, so they could spin them in the air at about one meter per second (or two miles an hour). I don’t know how you’d ever estimate that speed, but that was the goal. This would help the water evaporate at an optimum level.
You can imagine what happens during this evaporation by thinking about climbing out of the pool. You feel some cooling on your skin as water begins to evaporate when you climb out of a pool on a dry, warm summer day. That’s water as it changes from liquid into water vapor, and it actually takes energy for this to happen (44 kilojoules per mole). That’s actually quite a bit of energy used for changing liquid water into water vapor. When that happens, it decreases the temperature of this bulb. If we wait till we’ve reached that maximum temperature decrease, we can take that as our wet bulb temperature, or Tw.
This wet bulb temperature is not quite as simple as our dew point temperature to use in a calculation. Here’s the calculation we need to estimate vapor pressure from the wet bulb temperature.
Equation 4
We take the saturation vapor pressure (es) at the wet bulb temperature (Tw) and subtract, the gamma (Ɣ), which is the psychrometer constant 6.66 times 10-4 ℃-1 times the pressure of the air (Pa), multiplied by the difference between the air temperature (Ta) or that dry bulb that I mentioned earlier, and the wet bulb temperature (Tw).
Gamma is an interesting number. It’s actually the specific heat of air divided by the latent heat of vaporization, or that 44 kilojoules per mole that I mentioned before. We can simply take it as a constant for our purposes here as 6.66 times 10-4 ℃-1. So let’s actually put it into a calculation. Our example problem says find the vapor pressure of the air. If air temperature (Ta) is 20 degrees Celsius, the wet bulb temperature (Tw) is 11 degrees Celsius, and air pressure (Pa) is 100 kilopascals (basically at sea level). And just to remind us, this is the constant gamma (6.66 times 10-4 ℃-1). Air pressure is 100 kilopascals. We take this standard equation (Equation 4) and insert all these numbers.
Equation 5
So our vapor pressure is going to be this calculation from Tetons formula (Equation 2) and if you plug all those numbers into your calculator (notice our degrees C will cancel) we’re left with kilopascals. So our vapor pressure is about 0.71 kilopascals. So that is how we calculate the vapor pressure from the wet bulb temperature.
I hope this has been interesting. These are values that you may hear about. It’s less common today since we usually get our relative humidity from a capacitance-based relative humidity sensor, but still scientists talk in terms of wet bulb and dew point temperature. So it’s important to understand how we actually calculate our vapor pressure from those variables. If you’d like to know more about this, please visit our website, metergroup.com, and look at some of the instruments that are there to make measurements. Or you can email me if you want to know more at [email protected]. I hope you have a great day.
Hypoxic floods can be catastrophic for river ecosystems, often leading to widespread fish kills or other alterations in fish community composition and behavior. Hypoxia in rivers is uncommon due to the high rates of re-aeration in flowing waters, and when it does occur, it’s typically associated with human pollution (high nutrient loading). However, in the East African Mara River, hypoxic flooding events are not caused by humans, but by hippos.
Hippopotamia in East African Mara River
Over the past ten years, Dr. Christopher Dutton, aquatic ecologist at Yale University, and other researchers have documented frequent hypoxic floods and fish kills in the Mara river system. He says, “Our research shows these floods are caused by the flushing of hippopotamus pools. There are over 4000 hippopotami in the Kenyan portion of the Mara River bringing in over 3500 kg of organic carbon into the aquatic ecosystem each day. Hippo pools within the three tributaries of the Mara become anoxic under low discharge, while increases in discharge flush out the hippo pools and carry a hypoxic pulse of water through the river downstream.”
Hippopotamus Pool on the Mara River
Dutton and his team aim to understand the drivers of variability in these hypoxic floods and how these floods are propagated downstream in order to predict how the frequency and intensity of these events will be influenced by climate and land use change.
Unexpected patterns in dissolved oxygen
Dutton says they first noticed unusual patterns in aquatic health while working on another project. “When we started working in Kenya, we were trying to determine the environmental flows needed to maintain proper ecosystem function. We sampled from up in the forest down through the protected areas in the Masaai Mara and the Serengeti. We found the traditional indicators of water quality started to get much worse in the protected areas. This was surprising to us because we assumed water flowing through a protected area would be getting cleaner. But after we collected enough data, we could see that dissolved oxygen was crashing on average every 12 days for 8 to 12 hours and then rebounding. We hadn’t seen that in other rivers. This drew us to wonder if it was being caused by the flushing of hippo pools.”
Dutton says hippopotamus pools are slack water areas on the main river channel where hippos gather throughout the day because they don’t like fast moving water. He explains, “Every day they lounge in the water because their skin is sensitive to UV and gets desiccated in the sun. But at night and in the early morning, they leave the pools, go to the grassland, and eat tons and tons of grass. Afterward, they go back to the pool to rest, sleep, and defecate. They defecate so much organic matter into the river, it alters aquatic metabolism in ways that haven’t yet been fully understood.”
Hippopotamia Gathering in a Hippopotamus Pool
Dutton wants to understand how the organic matter and inorganic nutrients the hippos bring in are altering the ecosystem and what’s causing variability in the degree of hypoxia.
What’s causing the variability?
Dutton thinks there are two likely drivers of hypoxia: time since hippo pools were flushed and the size of the rainfall driving the event. He says, “Because rainfall in the Mara region is highly localized within and among catchments, the biogeochemistry that causes hypoxia can vary among pools and tributaries. Understanding these dynamics requires fine scale spatial and temporal data on precipitation patterns across the catchment.”
Dutton is using ATMOS 41 weather stations and METER data loggers in three Mara sub catchments to monitor the intensity and frequency of rainfall during these episodic floods where rains can be highly variable in space. He’s also documenting hippo pool biogeochemistry along with discharge and dissolved oxygen (DO) response in the main stem and tributaries. He’s using a water quality sonde to monitor DO and turbidity. He says, “We’re trying to quantify these events in the various catchments because they are different geologically. One of them has more sulfur containing rocks which causes sulfates in the water. In a reducing environment, sulfates turn into hydrogen sulfide which is toxic to fish. So we’re trying to parse out what’s really killing the fish in these different catchments.”
He says the data show there is such high biochemical oxygen demand from the bottom of these pools, that when the organic waste and reduced compounds are flushed, they continue to suck oxygen out of the river as the waste moves downstream. This often causes fish kills in the river. He adds, “We’ve seen thousands of fish dead after one of these events. But interestingly, the next day, it’s like it never happened. There are no fish anywhere on the bank. They’ve already been consumed by hyena, vultures, marabou, storks, and even lions.”
Data collection challenges
Dutton says collecting precipitation data in East Africa has unusual challenges. He says, “One of our sites is close to a hyena den. They occasionally go and unplug wires. And one of our weather stations was taken by an elephant. I concreted it in, but the elephant took it and dropped it 100 meters away.”
The team avoids losing data by locating their measurement stations near tourist camps, where locals can watch over the equipment. Dutton says, “We build fences around each of the stations, and we concrete them into the ground, but our biggest strategy is putting the site close to a camp. The Kenyans that run the camps are excited to have a weather station nearby. They enjoy seeing the data and sharing it with their guests.”
The team builds fences around installations to protect them from hyenas and other animals
What’s the future of the research?
Dutton says the team is still working on collecting data, which is not always easy. He says, “This year, a 100-year flood occurred in the Mara which destroyed our water quality sonde. The water got so high the compression on the sonde popped out all the sensors. We lost two months of data. So we haven’t yet been able to look closely at the relationships between the rainfall, the different catchments, and these crashes, but that’s something we’ll do as soon as we can get to the data.”
He says this research is important because the Mara River system is still a natural river system essentially untouched by humans with much of its megafauna intact, which is rare. He adds, “The hippos are a very natural part of this river, and these processes we’re documenting help us understand how rivers may have functioned prior to the removal of larger megafauna. In the last 50 years, there has been large scale deforestation in the upper catchment. Some people speculate that this is causing more erratic flows. So what happens when the flows become more (or less) than normal?”
Dutton recently published a peer-reviewed paper on the detailed biogeochemistry of the hippo pools in Ecosystems Journal. You can read it here. And you can read the team’s first paper documenting these events published on nature.com here.
If you rely on soil moisture data to make decisions, understand treatment effects, or make predictions, then you need that data to be accurate and reliable. But even one small oversight, such as poor installation, can compromise accuracy by up to +/-10%. How can you ensure your data represent what’s really happening at your site?
Chris Chambers discusses how people unknowingly compromise their soil moisture data.
Best practices you need to know
Over the past 10 years, METER soil moisture expert Chris Chambers has pretty much seen it all. In this 30-minute webinar, he’ll discuss 6 common ways people unknowingly compromise their data and important best practices for higher-quality data that won’t cause you future headaches. Learn:
Are you choosing the right type of sensor or measurement for your particular needs?
Are you sampling in the right place?
Why you must understand your soil type
How to choose the right number of sensors to deal with variability
At what depths you should install sensors
Common installation mistakes and best practices
Soil-specific calibration considerations
How cable management can make or break a study
Factors impacting soil moisture you should always record as metadata
Choosing the right data management platform for your unique application
Ever spent hours carefully installing your weather station in the field and then come back only to discover you made mistakes that compromised the installation? Or worse, find out months later that you can’t be confident in the quality of your data?
Installing ATMOS 41 Weather Stations
Our scientists have over 100 years of combined experience installing sensors in the field, and we’ve learned a lot about what to do and what not to do during an installation.
Best practices for higher accuracy
Join Dr. Doug Cobos in this 40-minute webinar as he discusses weather station installation considerations and best practices you don’t want to miss. Learn:
General siting and installation best practices
Installation recommendations from WMO and other standards organizations
Common installation mistakes
How to identify installation mistakes in your data
Microclimate variability and how to pick a representative location
He teaches the correct way to estimate the transpiration from a single leaf and how a leaf porometer can be used to obtain one of the needed variables.
Hello, my name is Colin Campbell. I’m a senior research scientist here at METER Group. And today we’ll talk about how to estimate the transpiration from a single leaf. Occasionally we get this question: Can I estimate the transpiration from a leaf by measuring its stomatal conductance? Unfortunately, you can’t. And I want to show you why that’s true and what you’ll need to do to estimate the total conductance, and therefore, the evaporation of a leaf.
Researcher Measuring Stomatal Conductance With an sc-1 Leaf Porometer
The calculation of transpiration (E) from a leaf is given by Equation 1
Equation 1
where gv is the total conductance of vapor from inside the leaf into the air, Cvs is the concentration of vapor inside the leaf and Cva is the concentration of vapor in the air.
Our scientists have decades of experience helping researchers measure the soil-plant-atmosphere continuum. Contact us for answers to questions about your unique application.
Soil moisture data analysis is often straightforward, but it can leave you scratching your head with more questions than answers. There’s no substitute for a little experience when looking at surprising soil moisture behavior.
Join Dr. Colin Campbell April 21st, 9am PDT as he looks at problematic and surprising soil moisture data.
Understand what’s happening at your site
METER soil scientist, Dr. Colin Campbell has spent nearly 20 years looking at problematic and surprising soil moisture data. In this 30-minute webinar, he discusses what to expect in different soil, environmental, and site situations and how to interpret that data effectively. Learn about:
Telltale sensor behavior in different soil types (coarse vs. fine, clay vs. sand)
Possible causes of smaller than expected changes in water content
Factors that may cause unexpected jumps and drops in the data
What happens to dielectric sensors when soil freezes and other odd phenomena
Surprising situations and how to interpret them
Undiagnosed problems that affect plant-available water or water movement
Why sensors in the same field or same profile don’t agree
Improving irrigation requires smart data gathering to help growers make better choices in the field. Measuring in situ creates high-resolution, temporal data enabling us to see clearly what’s happening over time—but only at a single point. Satellites show data across a large spatial scale but are hampered by revisit frequencies, clouds, and resolution limits.
Often we see information in a silo, looking at one type of data or another. The challenge to researchers is how to connect across these scales and combine the information to make better irrigation decisions. In this webinar, Dr. Colin Campbell explores the future of irrigation and research he’s been doing with collaborators at Brigham Young University. Learn:
How researchers are combining in situ, drone, and satellite measurements to extract key information
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.
