Skip to content

Posts from the ‘Data loggers’ Category

What’s Causing Fish Kills in the East African Mara River?

A surprising culprit

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.

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.”

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.” 

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.” 

ATMOS 41 weather station near a tourist camp

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.

Download the researcher’s complete guide to soil moisture—>

Download the researcher’s complete guide to water potential—>

Soil Moisture—6 Common Oversights That Might Be Killing Your Accuracy

Your decisions are only as good as your data

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

Watch it now—>

How to interpret soil moisture data

Surprises that leave you stumped

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
  • Problems you might see in surface installations

Watch it now

Degradation of soil-applied herbicides under limited irrigation

Soil-applied herbicides are important for controlling weeds in many crops because they offer a broadened control spectrum and chemical diversity. But if soil-applied herbicides persist in the soil too long, there is risk for damage to susceptible rotational crops in succeeding years. Since herbicide degradation in the soil is highly dependent on water, imminent needs to reduce agricultural water use in the future could lead to limited herbicide degradation and a greater risk for carryover.

Some crops don’t have a wide variety of post-emergent herbicide options, so growers are dependent on soil-applied herbicides for weed control.

Recently Daniel Adamson and a research team at the University of Wyoming, under the guidance of Dr. Gustavo Sbatella, investigated the effects of soil-applied herbicides under limited irrigation conditions. They wanted to understand how limited irrigation affects the efficacy and carryover of soil-applied herbicides in Wyoming’s irrigated crop rotations. A two-part field study was undertaken by applying four soil-applied herbicides to dry beans and four soil-applied herbicides to corn. 

Soil microbe activity matters

Describing his research site, Adamson says, “Wyoming is not a huge farming state but there’s a pocket of farm ground near the Powell/Cody area with a unique rotation. The main crop is sugar beets, and they also grow dry, edible beans, sunflowers and malt barley. Some of these crops don’t have a wide variety of post-emergent herbicide options, so growers are dependent on soil-applied herbicides for weed control. However, they need to balance weed control with timely dissipation so sensitive rotational crops won’t be injured.

Adamson says that soil-applied herbicides tend to be fairly long-lived in the soil, which is advantageous for weed control. Importantly, the herbicides dissipate through degradation by soil microbes, and soil microbes are highly influenced by how much water is in soil. When the soil is moist and warm, microbes are more active, and they degrade the herbicides faster. Thus, his team hypothesized that if future climate change effects led to limited availability of surface water for irrigation, these herbicides may not degrade as quickly and possibly injure crops planted successionally.

Assessing herbicide damage

During the first year, the research team applied three irrigation treatments to each crop: 100%, 85%, and 70% of crop evapotranspiration. Both crops and soil moisture were monitored using METER data loggers and soil moisture sensors. Adamson recalls, “The sensors were our means of tracking what was happening in the soil in terms of volumetric water content. Some of the areas were chronically dry, so the sensors enabled us to confirm that the treatments were applied correctly and should theoretically affect how the herbicides were performing. The volumetric soil water content of the three irrigation treatments averaged 24%, 18%, and 16% throughout the growing season, and crop yield decreased as irrigation was reduced.” 

Over the course of the second year, the team collected soil samples at regular intervals following herbicide application. They analyzed the samples for herbicide level and used them to perform a greenhouse bioassay to determine crop response to residual herbicide. Also during the second year, crop response was evaluated in the field when sugar beet, sunflower, and dry bean or corn was planted over the original plots and assessed for herbicide damage.

The results of the experiment were surprising.

Hurdles and challenges

Adamson said timing was the major difficulty in terms of applying irrigation treatments. He said, “There were no differences in irrigation timing for the various treatments. The way we irrigated was not representative of a typical deficit irrigation strategy because we were tied to a sprinkler with other projects on it. So we irrigated based on when the full water treatment would normally be irrigated. Other treatments had smaller nozzles so the amount of water was physically reduced.”

Adamson said they also weren’t prepared to track how some of the herbicides would behave in the soil. “Some of the herbicides degrade into metabolites that are phytotoxic in the soil, and it was hard to analyze for all molecules that were plant active. So that was challenging.”

Surprising results

Adamson said the results of the experiment were surprising. He says, “It was a good result for growers because we found there were no differences in the fields, statistically or visually, between how the herbicides carried over in the really dry soil versus the normally irrigated soil. So that was surprising, but from a practical standpoint for farmers, it was important information. They now know if they do have to start applying less water, it isn’t something to be overly concerned about.”

More research is needed

Adamson says more work is needed in this area of research. He adds, “There’s a tremendous amount of information within the weed science community about what herbicides do in the soil and things that influence that. But relatively few studies look at changing irrigation rates in a practical sense. A lot of the current studies are done in rain-fed systems where the amount of rain changes (i.e., a normal year vs. a drought year). In irrigated systems, you might reduce the amount of water, but it’s not a drastic reduction like a rain-fed system might experience. There’s not a huge amount of research looking at how different irrigation rates affect herbicide management, so I do think it would be worth exploring in the future.”

Download the researcher’s complete guide to soil moisture—>

Download the researcher’s complete guide to water potential—>


Combining in situ soil moisture with satellite data for improved irrigation recommendations

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
  • How these data can be connected across scales 

Watch it now

 

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.

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.

Watch the video

 

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.

Download the researcher’s complete guide to soil moisture—>

Download the researcher’s complete guide to water potential—>

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. 

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:

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.

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).

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.

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.

Download the researcher’s complete guide to soil moisture—>

Download the researcher’s complete guide to water potential—>

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

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

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

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

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.

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

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

Watch it here—>

Soil Moisture 202: Choosing the Right Water Potential Sensor

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

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?

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—>

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”

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.

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.”

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 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.”

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.

Apply now

Learn more

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.

Watch the video

 

 

 

Learn more

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? 

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.