At METER, we’ve installed thousands of remote weather stations and weather instruments over the years, so we’ve learned a lot about what to do and not to do during an installation. This article takes an in-depth look at seven basic steps you’ll want to think about as you set up your weather station in order to obtain the highest quality weather data.
Fig. 1: ATMOS 41 weather station in a wheat field
Before you start: Make sure you’re using the right weather station
One important thing to remember is that to obtain high quality data, you’ll need to install the right weather station for your unique application. In this 20-minute webinar, research scientist, Dr. Doug Cobos explores which types of weather stations fit different types of applications. Find out:
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
How does thenbsp;ATMOS 41 weather station compare to other methods?
Where is the sweet spot for performance divided by price in your application?
One often overlooked step to a good installation is testing your weather instruments before taking them to the field. It’s important to set your instrumentation up before you leave so you know everything is functioning and you have all the tools you’ll need. You can test your equipment at the office, in your yard, or even at a nearby park. Get all your mounting hardware together, and make sure all the weather instruments and sensors are functioning.
If you have a battery-powered system and a solar panel, check to see if they are charging. Ensure your data acquisition system is working. If using a programmable data logger like a Campbell Scientific logger, make sure you understand how to program it. Test the logger program to ensure it is functioning correctly and recording. Many data acquisition backbones now have remote data delivery, so check that data are flowing into the server and you’re able to access those data.
You’ve buried soil water content and water potential (soil suction) sensors in the ground, installed an ATMOS 41 in the field, and set up your ZL6 data logger. Your network of instruments has been collecting data for weeks or even all season.
Now what? Knowing how to extrapolate meaningful inferences from your data, forming big picture conclusions about what is happening, and identifying and troubleshooting issues can sometimes be challenging.
Learn everything you need to know about analyzing soil moisture data.
In this article, we will step through multiple data sets to understand how soil water content, soil temperature, soil water potential, and atmospheric measurements can be used to discover the meaning behind the traces. Within this article you will learn how to identify the following events in your data:
Behavior of soil moisture sensors in different soil types
In this 20-minute webinar, METER scientist Leo Rivera compares available methods and teaches how to combine the latest technology to generate full, accurate curves with hundreds of points in only a couple of days—instead of a couple of months.
Combine technology for more accurate soil moisture release curves
If you’re still spending months generating a handful of points to produce only a partial soil characteristic curve—old-school methods are holding you back. What if you could create a soil moisture release curve in just 48 hours? And not just a curve with a few points, but a detailed absorption and desorption curve composed of hundreds of points that show exactly what happens as your soil absorbs and desorbs water throughout the entire range of water potentials?
Change the way you understand your soil
Partial curves made with older methods don’t give you enough data for a complete picture of what’s happening in your soil. Hundreds of studies show that faster, high-precision modern methods are more accurate—so you can reach better conclusions that stand up to rigorous scientific scrutiny. In this 20-minute webinar, METER scientist Leo Rivera compares available methods and teaches how to combine the latest technology to generate full, accurate curves with hundreds of points in only a couple of days—instead of a couple of months. Learn:
The science behind current available methods
The pros and cons of each method
Advances in soil moisture release curve technology
Leo Rivera operates as a research scientist and Hydrology Product Manager at METER Group, the world leader in soil moisture measurement. He earned his undergraduate degree in Agriculture Systems Management at Texas A&M University, where he also got his Master’s degree in Soil Science. There he helped develop an infiltration system for measuring hydraulic conductivity used by the NRCS in Texas. Currently, Leo is the force behind application development in METER’s hydrology instrumentation including HYPROP and WP4C. He also works in R&D to explore new instrumentation for water and nutrient movement in soil.
You’ve buried soil water content and water potential sensors in the ground, installed an ATMOS 41 in the field, and set up your ZL6 data logger. Your network of instruments has been collecting data for days, weeks, or even all season. Now what? Performing soil moisture data analysis for your research location is one thing. Knowing how to extrapolate meaningful inferences and conclusions to understand what is happening and troubleshoot issues is completely different.
In this article, we will step through multiple data sets to understand how soil water content, soil temperature, soil water potential, and atmospheric measurements can be used to discover the meaning behind the traces. Within this article you will learn how to identify the following events in your data:
Behavior of soil moisture sensors in different soil types
Infiltration
Flooding
Soil cracking
Freezing
Spatial variability
Temperature effects
Diurnal patterns due to hydraulic redistribution
Broken sensors
Installation problems
Each example will be represented by a graph. It is not necessary to understand every aspect of information within these graphs. Each one is used as an illustration of common soil moisture data patterns you might run into and how to extrapolate the most useful information possible from the patterns seen. Each graph will have a box in the upper right-hand side corner with the soil type and crop type so you have a better understanding of the variables at play.
All of the data provided was collected by data loggers, such as our ZL6 series, and uploaded to ZENTRA Cloud for remote viewing at the convenience of the user. All data sets are either from METER’s own instrumentation or are supplied by the data owner and are included with their permission.
Figure 1. ZL6 Basic data logger with data collected and stored within the ZENTRA Cloud platform
Effects of soil types
Figure 2. Water content and water potential measurements for a turf grass in loamy sand in wet conditions
In Figure 2 we see the data from an engineered loamy sand with a cover crop of turf grass. Our goal when executing our experiments in this example was to improve irrigation in turf grass. This grass had a fairly shallow root zone, the middle of which was about six cm deep and the bottom at about 10 cm. Over time, this example showed first relatively wet conditions to start through June and July, a fixed drying period condition in July and August, and drying until the cessation of water uptake in August and September.
This graph shows two soil moisture data types: volumetric water content on the left y-axis and matric potential, or water potential, on the right y-axis. Time is on the x-axis ranging from early summer to the start of fall. To understand what these data clusters can tell us, we must look at each data set individually.
There’s a lot to consider when collecting soil moisture measurements.
Get your soil moisture questions answered in our Office Hours series.
Join Environment Support Manager, Chris Chambers, and Director of Science Outreach, Leo Rivera, as they discuss submitted questions all about getting the best soil moisture measurements.
In the full episode, they discuss:
How difficult is the calibration of dielectric sensors?
How does soilless media affect the operation of dielectric sensors?
How much can organic soil amendments influence soil moisture?
Is it possible to determine the soil hydraulic properties from soil water content?
Why volumetric water content instead of gravimetric water content?
What is the best way to correct for the temperature sensitivity of sensors?
What impact does direct solar radiation have on the overall radiation balance? Dr. Colin Campbell, WSU Environmental Biophysics professor and METER scientist, shows you how to do the calculations in our latest chalk talk.
Transcript
Hi, I’m Dr. Colin Campbell. And this is a METER Chalk Talk.
Have you ever been outside on a hot day walking in the full sun and then stepped into the shade? The relief is almost immediate. And I was thinking about that a lot when I was looking at this graph here, the estimated crop water loss on one of my experiments.
So this is an ET zero, meaning a reference ET. But since I was working grass, that was actually the estimated water loss from this grass crop. And what I noticed was that the shape of this curve kind of went up, and then went down. And it kind of matched right here, the solstice, the summer solstice. And in my mind, I thought, you know, what impact is direct solar radiation have on the overall radiation balance? Well, we can quickly just jump down and look at the equation that talks about how we might estimate the evapotranspiration from a crop. I’m not going to be able to have time here to get into what each of these variables mean.
But as you see, solar absorbed radiation, R abs is a strong component of that overall calculation. Now, when we talk about absorbed radiation, we need to understand that it’s not just all direct sunlight. In fact, if you assumed that, you’d be off in the weeds quite a bit, because it contains components of both longwave radiation, which is radiation that’s coming from your terrestrial surroundings, and shortwave radiation, that which is coming from predominantly the sun.
So let’s talk about that for a minute. With absorbed radiation, we have shortwave radiation. This is radiation that’s less than four micrometers. And we have longwave radiation. This is not surprisingly, from wavelengths greater than four micrometers. Now, this shortwave radiation, this comes from the sun longwave radiation comes from other sources, like trees, the sky, ground, just other objects that are around the temperature that we expect in the natural environment. Now, the truth of the matter is to get R abs, we need to combine both of these things into a single number. And it actually gets even more complex than that. So bear with us as we go on to the next equation.
R abs is a function of both shortwave radiation and long wave radiation. And when we calculate our radiation balance to get absorbed radiation, we have to actually take all of this into account. Now, you might be wondering, what are the other pieces in this equation, we’re going to spend a little time going over that. So you might understand how we can get from all of these numbers, all of these potential sources of radiation to a final number of R abs.
This portion of the equation here is shortwave radiation. And we’re going to talk about the variables in that equation. The first one we see is alpha s. It’s a number between zero and one. It signifies the percentage of shortwave radiation that the object can absorb. The other parameters in the equation include some F’s and some S’s. The F’s we call view factors, we’ll discuss view factors in more detail in another chalk talk. But suffice it to say that these essentially are parameters to estimate the amount of radiation that our object can see in its surroundings.
S stands for shortwave radiation. And this comes from several different sources. They include p: this is radiation that’s coming directly from the sun. That’s the one I mentioned earlier, that we feel if we’re standing in the direct sun, versus if we walk into the shade. But there are a couple of others. One is diffuse. This is the radiation that’s scattered as light comes into our atmosphere and it’s scattered by the atmosphere.
Finally, there’s R. This is reflected radiation, radiation that when it comes in, hits a surface, it reflects off that surface and comes and impinges on our object. Think about snow. If you’ve ever been skiing or out on the snow, you know, on a sunny day, you’re getting a lot of radiation that’s being reflected back. This portion of the equation over here is our longwave portion. Similar to our shortwave, it contains many of the same symbols, but they’re a little bit different.
The alpha L is the absorbed radiation. Now in the long wave that also goes from zero to one. The F is our view factor again, but now the view factor of longwave radiation, and L stands for that longwave radiation. This time, the subscripts A, that stands for atmosphere, and G stands for ground. If we put together all components in this equation, we’ll be able to solve for absorbed radiation. But that’s going to take a little bit of work. First, we need to understand the absorptivity of our surface both in the shortwave and the longwave.
The shortwave typically is calculated just from tables from looking out on the internet. For example, if I wanted to look at the absorptivity of a maple leaf, that’s typically around 50%. But it’s something that’s probably been calculated in literature. For our longwave radiation, almost all objects absorb long wave radiation at about 97 to 98% of the possible total.
So it’s pretty easy to estimate these absorptivities for objects that are fairly common. Calculating solar radiation and long wave radiation take a little bit more time. And especially understanding the view factors or how much of a particular surface our object sees, is going to take a whole chalk talk on its own. We’re going to leave this discussion here and leave for next time an opportunity to talk about how to calculate our shortwave radiation, or long wave radiation, and then get to the complicated discussion of view factors.
For more content like this, head over to our YouTube channel, or go to metergroup.com. Thanks for watching METER chalk talks.
Drs. Kim Novick and Jessica Guo team up to discuss the vital role water potential measurement plays in both plant and soil sciences and the work they are doing to establish the first-of-its-kind nationwide water potential network. Join their discussion to understand how a communal knowledge of these measurements could impact what we know about climate change and ecology as a whole.
A water potential measurement network could increase our understanding of climate change and ecology.
Dr. Kim Novick is a professor, Paul H. O’Neill Chair, Fischer Faculty Fellow, and director of the Ph.D. Program in Environmental Sciences at Indiana University. She earned her bachelor’s and Ph.D. in environmental science at Duke University’s Nicholas School of the Environment. Her research areas span ecology and conservation, hydrology and water resources, and sustainability and sustainable development, with specific interests in land-atmosphere interactions, terrestrial carbon cycling, plant ecophysiology, and nature-based climate solutions.
Dr. Jessica Guo is a plant ecophysiologist and data scientist who studies plant-environment interactions under extreme climate conditions. She earned her bachelor’s in environmental biology from Columbia University and her Ph.D. in biological sciences from Northern Arizona University. She is currently at the University of Arizona, where she blends her passion for reproducible workflows, interactive visualizations, and hierarchical Bayesian models with her expertise in plant water relations.
The views and opinions expressed in the podcast and on this posting are those of the individual speakers or authors and do not necessarily reflect or represent the views and opinions held by METER.
Like a silent battle cry, plants call out to signal they are under siege as a warning to other plants and to call in reinforcements to fend off the invasion.
Listen to research on pathogen infection, water stress, and how plants communicate and defend themselves.
How does this communication work? What else are plants doing to protect themselves from disease and predators alike? In our latest podcast, Natalie Aguirre, a PhD candidate and plant physiology and chemical ecology researcher at Texas A&M University, dives into her research on pathogen infection, water stress, and how plants communicate and defend themselves.
Natalie Aguirre graduated with a degree in biology from Pepperdine University, where she completed an honors thesis conducting research on the interaction of drought stress and pathogen infection in chaparral shrubs. She then spent a year as a Fulbright scholar in Spain, studying the effect of water stress on Dutch Elm Disease. Most recently, Natalie worked for the Everglades Foundation, creating educational programs and materials about the Florida Everglades.
The views and opinions expressed in the podcast and on this posting are those of the individual speakers or authors and do not necessarily reflect or represent the views and opinions held by METER.
As world water demand increases and supplies decrease, how can we turn more of the water we use for agriculture into biomass? In this webinar, Dr. Campbell dives deep into the measurement and implications of making the most of every drop of water.
Learn how to measure the amount of water a crop will need.
Crops turn sunlight, water, carbon dioxide, and nutrients into food
The availability of those resources puts limitations on the amount of food a crop can produce. A previous webinar considered the limitations of sunlight. In this 30-minute webinar, world-renown environmental biophysicist, Dr. Gaylon S. Campbell, discusses how to measure the amount of water a crop will need and how to use that value to predict the amount of biomass it will produce.
Achieve maximum biomass from every drop
Join Dr. Campbell as he discusses the measurements and calculations needed to know how much biomass a given environment can produce. Dr. Campbell will discuss:
How resource capture models work
How biomass production and water use are linked
Examples of effective uses of water resource capture models
Instrumentation needed to determine water and radiation limitations on yield
How to use soil and atmospheric measurements to quantify crop water capture
Water budgets and how they are used to get transpiration and biomass production
Dr. Campbell has been a research scientist and engineer at METER for 19 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.
Abiotic stress in plants: How to assess it the right way
As a plant researcher, you need to effectively assess crop performance, whether you’re selecting the best variety, trying to understand abiotic stress tolerance, studying disease resistance, or determining climate resilience. But if you’re only measuring weather data, you might be missing key performance indicators. Water potential is underutilized by plant researchers in abiotic stress studies even though it is the only way to assess true drought conditions when determining drought tolerance in plants. Learn what water potential is and how it can improve the quality of your plant study.
Soil directly impacts plant growth via nutrient availability, disease pressure, root growth, and water availability.
Quantitative genetics in plant breeding: why you need better data
If you’ve studied plant populations, you’re probably familiar with the simplified equation in Figure 1 that represents how we think about the impact of genetics and the environment on observable phenotypes.
Figure 1. Phenotype = Genotype + Environment
This equation breaks down the observed phenotype (plant height, yield, kernel color, etc.) into the effects from the genotype (the plants underlying genetics) and the effects of the environment (rainfall, average daily temperature, etc.). You can see from this equation that the quality of your study directly depends on the kind of environmental data you collect. Thus, if you’re not measuring the right type of data, the accuracy of your entire study can be compromised.
Water potential: the secret to understanding water stress in plants
Drought studies are notoriously difficult to replicate, quantify, or even design. That’s because there is nothing predictable about drought timing, intensity, or duration, and it’s difficult to make comparisons across sites with different soil types. We also know that looking at precipitation alone, or even volumetric water content, doesn’t adequately describe the drought conditions that are occurring in the soil.
Figure 2. The TEROS 21 is a field sensor used to measure soil water potential
Soil water potential is an essential tool for quantifying drought stress in plant research because it allows you to make quantitative assessments about drought and provides an easy way to compare those results across field sites and over time. Let’s take a closer look to see why.
