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Posts by Colin Campbell

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.

Image of hippopotamai in the east African Mara River
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.”

Image of hippopotami in a hippopotamus pool in the east African Mara River
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.” 

Image of hipopotamia gathering in a pool outside of the water currents in the Mara River
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.” 

Image of an ATMOS 41 weather station and a METER data logger placed near the Mara River
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.”

Image of an ATMOS 41 and METER data logger enclosed in a fence to protect the weather station from animals
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.

See ATMOS 41 weather station performance data.

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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?

Image of a researcher digging an installation site for a sensor
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 install soil moisture sensors—faster, better, and for higher accuracy

Why installation is everything

If you want accurate data, correct sensor installation should be your number one priority. When measuring in soil, natural variations in density may result in accuracy loss of 2 to 3%, but poor installation can potentially cause accuracy loss of greater than 10%. 

Image of a researcher holding a TEROS-12 soil moisture sensor
TEROS 12 soil moisture sensor

Proper sensor installation is the foundation for the data you collect. If you have a poor foundation, it makes data interpretation difficult. In this article, get insider tips on how to install soil moisture sensors faster, better, and for higher accuracy.  Learn:

  • What to be aware of when installing sensors
  • What installation trouble looks like in your data
  • Installation priorities for soil moisture sensors
  • How METER is advancing the science of installation for higher quality data

Understand your sensors

To understand why poor sensor installation has an enormous impact on the quality of your data, you’ll need to understand how soil moisture sensors work. 

Soil moisture sensors (water content sensors) measure volumetric water content. Volumetric water content (VWC) is the volume of water divided by the volume of soil (Equation 1) which gives the percentage of water in a soil sample.

Image of the equation used to calculate the measure of volumetric water content
Equation 1

So, for instance, if a volume of soil (Figure 1) was made up of the following constituents: 50% soil minerals, 35% water, and 15% air, that soil would have a 35% volumetric water content.

Image of a diagram showing soil constituents in a volume of soil
Figure 1. Soil constituents

Why capacitance sensors work

All METER soil moisture sensors use an indirect method called capacitance technology to measure VWC. “Indirect” means a parameter related to VWC is measured, and a calibration is used to convert that amount to VWC. In simple terms, capacitance technology uses two metal electrodes (probes or needles) to measure the charge-storing capacity (or apparent dielectric permittivity) of whatever is between them.

Image of a diagram depicting how capacitance sensors use two probes to form an electromagnetic field
Figure 2. Capacitance sensors use two probes (one with a positive charge and one with a negative charge) to form an electromagnetic field. This allows them to measure the charge-storing capacity of the material between the probes.

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7 Weather Station Installation Mistakes to Avoid

Rookie mistakes that ruin your research

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?

Image of a researcher installing an ATMOS 41 all-in-one weather station
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
  • Troubleshooting at the site
  • Types of metadata you should always collect

Register now

More resources

Explore which weather station is right for you.

Learn more about measuring the soil-plant-atmosphere continuum.

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

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

Chalk talk: How to measure leaf transpiration

In his latest chalk talk video, Dr. Colin Campbell discusses why you can’t measure leaf transpiration with only a leaf porometer.

Image of the SC-1 Leaf Porometer which measures stomatal conductance
The SC-1 Leaf Porometer measures stomatal conducance

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.

Watch the video

 

Video transcript

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.

Image of a researcher Measuring stomatal conductance With an SC-1 Leaf Porometer
Researcher Measuring Stomatal
Conductance With an sc-1 Leaf Porometer

The calculation of transpiration (E) from a leaf is given by Equation 1 

Image of the equation used for the calculation of transpiration from a leaf
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.

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Learn more about canopy measurements

Download the researcher’s complete guide to leaf area index—>

Questions?

Our scientists have decades of experience helping researchers measure the soil-plant-atmosphere continuum. Contact us for answers to questions about your unique application.

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. 

Image of orange, yellow, and white flowers in a green house
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

Learn more

Download the “Complete guide to irrigation management”—>

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.

Image of a sunflower in a sunflower field facing the sun
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.

Crops planted in a field 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—>

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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

 

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

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

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

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

Understand your choices

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

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

Watch the webinar—>

Presenter:

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

Watch it now

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

Data deep dive: When to doubt your measurements

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

Image of the Wasatch Plateau

Wasatch Plateau

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

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—>