Posts tagged ‘Transpiration’

Dec 8

Irrigation Curves—A Novel Irrigation Scheduling Technique

This week, guest author Dr. Michael Forster, of Edaphic Scientific Pty Ltd & The University of Queensland, writes about new research using irrigation curves as a novel technique for irrigation scheduling.

Corn field with a blue cloudy sky background

Growers do not have the time or resources to investigate optimal hydration for their crop. Thus, a new, rapid assessment is needed.

Measuring the hydration level of plants is a significant challenge for growers. Hydration is directly quantified via plant water potential or indirectly inferred via soil water potential. However, there is no universal point of dehydration with species and crop varieties showing varying tolerance to dryness. What is tolerable to one plant can be detrimental to another. Therefore, growers will benefit from any simple and rapid technique that can determine the dehydration point of their crop.

New research by scientists at Edaphic Scientific, an Australian-based scientific instrumentation company, and the University of Queensland, Australia, has found a technique that can simply and rapidly determine when a plant requires irrigation. The technique builds on the strong correlation between transpiration and plant water potential that is found across all plant species. However, new research applied this knowledge into a technique that is simple, rapid, and cost-effective, for growers to implement.

Current textbook knowledge of plant dehydration

The classic textbook values of plant hydration are field capacity and permanent wilting point, defined as -33 kPa (1/3 Bar) and -1500 kPa (15 Bar) respectively. It is widely recognized that there are considerable limitations with these general values. For example, the dehydration point for many crops is significantly less than 15 Bar.

Furthermore, values are only available for a limited number of widely planted crops. New crop varieties are constantly developed, and these may have varying dehydration points. There are also many crops that have no, or limited, research into their optimal hydration level. Lastly, textbook values are generated following years of intensive scientific research. Growers do not have the time, or resources, to completely investigate optimal hydration for their crop. Therefore, a new technique that provides a rapid assessment is required.

How stomatal conductance varies with water potential

There is a strong correlation between stomatal conductance and plant water potential: as plant water potential becomes more negative, stomatal conductance decreases. Some species are sensitive and show a rapid decrease in stomatal conductance; other species exhibit a slower decrease.

Plant physiologist refer to P50 as a value that clearly defines a species’ tolerance to dehydration. One definition of P50 is the plant water potential value at which stomatal conductance is 50% of its maximum rate. P50 is also defined as the point at which hydraulic conductance is 50% of its maximum rate. Klein (2014) summarized the relationship between stomatal conductance and plant water potential for 70 plant species (Figure 1). Klein’s research found that there is not a single P50 for all species, rather there is a broad spectrum of P50 values (Figure 1).

Figure 1. The relationship between stomatal conductance and leaf water potential for 70 plant species. The dashed red lines indicate the P80 and P50 values. The irrigation refill point can be determined where the dashed red lines intersect with the data on the graph. Image has been adapted from Klein (2014), Figure 1b.

Taking advantage of P50

The strong, and universal, relationship between stomatal conductance and water potential is vital information for growers. A stomatal conductance versus water potential relationship can be quickly, and easily, established by any grower for their specific crop. However, as growers need to maintain optimum plant hydration levels for growth and yield, the P50 value should not be used as this is too dry. Rather, research has shown a more appropriate value is possibly the P80 value. That is, the water potential value at the point that stomatal conductance is 80% of its maximum.

Irrigation Curves – a rapid assessment of plant hydration

Research by Edaphic Scientific and University of Queensland has established a technique that can rapidly determine the P80 value for plants. This is called an “Irrigation Curve” which is the relationship between stomatal conductance and hydration that indicates an optimal hydration point for a specific species or variety.

Once P80 is known, this becomes the set point at which plant hydration should not go beyond. For example, a P80 for leaf water potential may be -250 kPa. Therefore, when a plant approaches, or reaches, -250 kPa, then irrigation should commence.

P80 is also strongly correlated with soil water potential and, even, soil volumetric water content. Soil water potential and/or content sensors are affordable, easy to install and maintain, and can connect to automated irrigation systems. Therefore, establishing an Irrigation Curve with soil hydration levels, rather than plant water potential, may be more practical for growers.

Example irrigation curves

Irrigation curves were created for a citrus (Citrus sinensis) and macadamia (Macadamia integrifolia). Approximately 1.5m tall saplings were grown in pots with a potting mixture substrate. Stomatal conductance was measured daily, between 11am and 12pm, with an SC-1 Leaf Porometer. Soil water potential was measured by combining data from an MPS-6 (now called TEROS 21) Matric Potential Sensor and WP4 Dewpoint Potentiometer. Soil water content was measured with a GS3 Water Content, Temperature and EC Sensor. Data from the GS3 and MPS-6 sensors were recorded continuously at 15-minute intervals on an Em50 Data Logger. When stomatal conductance was measured, soil water content and potential were noted. At the start of the measurement period, plants were watered beyond field capacity. No further irrigation was applied, and the plants were left to reach wilting point over subsequent days.

Figure 2. Irrigation Curves for citrus and macadamia based on soil water potential measurements. The dashed red line indicates P80 value for citrus (-386 kPa) and macadamia (-58 kPa).

Figure 2 displays the soil water potential Irrigation Curves, with a fitted regression line, for citrus and macadamia. The P80 values are highlighted in Figure 2 by a dashed red line. P80 was -386 kPa and -58 kPa for citrus and macadamia, respectively. Figure 3 shows the results for the soil water content Irrigation Curves where P80 was 13.2 % and 21.7 % for citrus and macadamia, respectively.

Figure 3. Irrigation Curves for citrus and macadamia based on soil volumetric water content measurements. The dashed red line indicates P80 value for citrus (13.2 %) and macadamia (21.7 %).

From these results, a grower should consider maintaining soil moisture (i.e. hydration) above these values as they can be considered the refill points for irrigation scheduling.

Further research is required

Preliminary research has shown that an Irrigation Curve can be successfully established for any plant species with soil water content and water potential sensors. Ongoing research is currently determining the variability of generating an Irrigation Curve with soil water potential or content. Other ongoing research includes determining the effect of using a P80 value on growth and yield versus other methods of establishing a refill point. At this stage, it is unclear whether there is a single P80 value for the entire growing season, or whether P80 shifts depending on growth or fruiting stage. Further research is also required to determine how P80 affects plants during extreme weather events such as heatwaves. Other ideas are also being investigated.

For more information on Irrigation Curves, or to become involved, please contact Dr. Michael Forster: [email protected]

Reference

Klein, T. (2014). The variability of stomatal sensitivity to leaf water potential across tree species indicates a continuum between isohydric and anisohydric behaviours. Functional Ecology, 28, 1313-1320. doi: 10.1111/1365-2435.12289

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Jul 3

Stem Water Content Changes Our Understanding of Tree Water Use (Part 2)

This week, we continue highlighting the second of two current research projects (see part one) which use soil moisture sensors to measure volumetric water content in tree stems and why this previously difficult to obtain measurement will change how we look at tree water use.

Tamarisk tree: an invasive species dominant in Sudan and arid parts of the United States. (Photo credit: biolib.cz)

Determining Tree Stem Water Content in Drought Tolerant Species

Tadaomi Saito and his research team were interested in using dielectric soil moisture sensors to measure the tree stem volumetric water content of mesquite trees and tamarisk, two invasive species dominant in Sudan and arid parts of the United States. Mesquite is a species that can access deep groundwater sources using their taproots which is how they compete with native species. Tamarisk, on the other hand, uses shallow, saline groundwater to survive. The team wanted to see if dielectric probes were useful for real-time measurement of plant water stress in these drought-tolerant species and if these measurements could illuminate differing tree water-use patterns. These sensors could then potentially be used for precision irrigation strategies to assist in agricultural water management.

Temperature Calibration Was Essential

After calibrating the soil moisture sensors to the wood types in a lab, the team inserted probes into the stems of both trees. They also monitored groundwater and soil moisture content to try and infer whether or not the trees were plugged into a deep source of water. Interestingly, Saito found that, unlike soil, where temperature fluctuation is buffered, tree stems are subject to large variations in temperature throughout the course of the day. This temperature fluctuation interfered with the soil moisture probes’ ability to accurately measure VWC. The team came up with a simple method for accounting for temperature variability and were then able to obtain accurate VWC measurements.

Image of a Mesquite tree on a desert mountain slope

Photo credit: desertusa.com

Water Use Depended on Landscape Position

Saito’s results were similar to Ashley Matheny’s study (see part 1), in that they found a lot of different patterns, even in trees of the same species. Water-use depended on where the trees were on the landscape. Some of them were tapped into groundwater, and the stem water storage didn’t change no matter how dry the soil became. Whereas others, depending on their position in the landscape, were very dependent on soil moisture conditions.

You can read the full study details here.

Implications

Saito’s study illustrates that we see everything about a tree that’s above ground, but we may have no sense of what’s going on below ground. We can put a soil moisture sensor in the ground and decide there’s plenty of moisture available. Or if conditions are dry, we may decide the tree is under drought stress, but we don’t know if that tree is tapped into a more permanent source of groundwater.

Other researchers have put soil moisture sensors in orchards looking at stem water storage from a practical standpoint for irrigation management. Their data didn’t work out so well because of cable sensitivity where water on the cable created false readings. However, the data they were able to obtain showed that some of the trees were plugged into water sources that were independent of the soil. Those trees were able to withstand drought and needed less irrigation, whereas other trees were much more sensitive to soil moisture.

If we had an inexpensive, easy to deploy measurement device plugged into every tree in an orchard, we could irrigate tree by tree, give them precisely what they needed, and account for their unique situation.

What Does it All Mean?

The interesting thing about using soil moisture sensors in a tree is that stem water content is a difficult-to-obtain piece of information that has now been made easier. Historically, we’ve focused on measuring sap flow, but that’s just how much water is flowing past the sensor. We’ve measured what’s in the soil: a pool of moisture that’s available to the tree. But some trees are huge in size, such as ones along the coast of California. They’re able to store vast amounts of water above-ground in their tissue. Understanding how a tree can use that water to buffer or get through periods of drought is a unique research topic that has had very little attention. With these kinds of sensors, we can start to investigate those questions.

Reference: Saito T., H. Yasuda, M. Sakurai, K. Acharya, S. Sueki, K. Inosako, K. Yoda, H. Fujimaki, M. Abd Elbasit, A. Eldoma and H. Nawata , Monitoring of stem water content of native/invasive trees in arid environments using GS3 soil moisture sensor , Vadose Zone Journal , vol.15 (0) (p.1 – 9) , 2016.03

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Jun 26

Stem Water Content Changes Our Understanding of Tree Water Use

In an update to our previous blog, “Soil Moisture Sensors in a Tree?”, we highlight two current research projects using soil moisture sensors to measure volumetric water content (VWC) in tree stems and share why this previously difficult-to-obtain measurement will change how we look at tree water usage.

Image of green leafs with sunlight streaming through them

Researchers explore the feasibility of inserting capacitance soil sensors in tree stems as a real-time measurement.

Soil Moisture Sensors in Tree Stems?

In a recent research project, Ph.D. candidate Ashley Matheny of the University of Michigan used soil sensors to measure volumetric water content in the stems of two species of hardwood trees in a northern Michigan forest: mature red oak and red maple. Though both tree types are classified as deciduous, they have different strategies for how they use water. Oak is anisohydric, meaning the species doesn’t control their stomata to reduce transpiration, even in drought conditions. Isohydric maples are more conservative. If the soil starts to dry out, maple trees will maintain their leaf water potential by closing their stomata to conserve water. Ashley and her research team wanted to understand the different ways these two types of trees use stem water in various soil moisture scenarios.

Historically, tree water storage has been measured using dendrometers and sap flow data, but Ashley’s team wanted to explore the feasibility of inserting a capacitance-type soil sensor in the tree stems as a real-time measurement. They hoped for a practical way to make this measurement to provide more accurate estimations of transpiration for use in global models.

Image of a Hardwood tree in northern Michigan in Autumn

Scientists measured volumetric water content in the stems of two species of hardwood trees in a northern Michigan forest: mature red oak and red maple.

Measurements used

Ashley and her team used meteorological, sap flux, and stem water content measurements to test the effectiveness of capacitance sensors for measuring tree water storage and water use dynamics in one red maple and one red oak tree of similar size, height, canopy position and proximity to one another (Matheny et al. 2015). They installed both long and short soil moisture probes in the top and the bottom of the maple and oak tree stems, taking continuous measurements for two months. They calibrated the sensors to the density of the maple and oak woods and then inserted the sensors into drilled pilot holes. They also measured soil moisture and temperature for reference, eventually converting soil moisture measurements to water potential values.

Results Varied According to Species

The research team found that the VWC measurements in the stems described tree storage dynamics which correlated well with average sap flux dynamics. They observed exactly what they assumed would be the anisohydric and isohydric characteristics in both trees. When soil water decreased, they saw that red oak used up everything that was stored in the stem, even though there wasn’t much available soil moisture. Whereas in maple, the water in the stem was more closely tied to the amount of soil water. After precipitation, maple trees used the water stored in their stem and replaced it with more soil water. But, when soil moisture declined, they held onto that water and used it at a slower rate.

Researchers want to figure out the appropriate level of detail for tree water-use strategy in a global model.

Trees use different strategies at the species level

The ability to make a stem water content measurement was important to these researchers because much of their work deals with global models representing forests in the broadest sense possible. They want to figure out the appropriate level of detail for tree water-use strategy in a global model. Both oak and the maple are classified as broadleaf deciduous, and in a global model, they’re lumped into the same category. But this study illustrates that if you’re interested in hydrodynamics (the way that trees use water), deciduous trees use different strategies at the species level. Thus, there is a need to treat them differently to produce accurate models.

Read the full study in Ecosphere.

Reference: Matheny, A. M., G. Bohrer, S. R. Garrity, T. H. Morin, C. J. Howard, and C. S. Vogel. 2015. Observations of stem water storage in trees of opposing hydraulic strategies. Ecosphere 6(9):165. http://dx.doi.org/10.1890/ES15-00170.1

Next week: Part 2 of this article showcases more research being done using soil moisture sensors to measure volumetric water content in tree stems.

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Mar 27

New Weather Station Technology in Africa (Part 2)

Weather data improve the lives of many people. But, there are still parts of the globe, such as Africa, where weather monitoring doesn’t exist (see part 1). John Selker and his partners intend to remedy the problem through the Trans African Hydro Meteorological Observatory (TAHMO). Below are some challenges they face.

Researcher holding an ATMOS 41 weather station in Africa

TAHMO aims to deploy 20,000 weather stations across the continent of Africa in order to fill a hole that exists in global climate data.

Big Data, Big Governments, and Big Unknowns

Going from an absence of data to the goal of 20,000 all-in-one weather stations offers hope for positive changes. However, Selker is still cautious. “Unintended consequences are richly expressed in the history of Africa, and we worry about that a lot. It’s an interesting socio-technical problem.” This is why Selker and others at TAHMO are asking how they can bring this technology to Africa in a way that fits with their cultures, independence, and the autonomy they want to maintain.

TAHMO works with the government in each country stations are deployed in; negotiating agreements and making sure the desires of each recipient country are met. Even with agreements in place, the officials in each country will do what is in the best interest of the people: a gamble in countries where corruption is a factor which must be addressed. Selker illustrates this point by recalling an instance in 1985 when he witnessed a corrupt government official take an African farmer’s land because the value had increased due to a farm-scale water development project.

Most TAHMO weather stations are hosted and maintained by a local school, making it available as an education tool for teachers to use to teach about climate and weather. Data from TAHMO are freely available to the government in the country where the weather station is hosted, researchers who directly request data, and to the school hosting and maintaining the weather station. Commercial organizations will be able to purchase the data, and the profits will be used to maintain and expand the infrastructure of TAHMO.

Selker says it’s all about collaboration.

Terrorism, Data, and Open Doors

“When I wanted to go out and put in weather stations, my wife said, ‘No, you will not go to Chad.’ … because it is Boko Haram central,” Selker says.

The Boko Haram— a terrorist organization that has pledged allegiance to ISIS— creates an uncommon hurdle. Currently, the Boko Haram is most active in Nigeria, but has made attacks in Chad, Cameroon, and Niger.

Selker also mentioned similar issues with ISIS, “When ISIS came through Mali, the first thing they did is destroy all the weather stations. So they have no weather data right now in Mali.” Acknowledging the need for security, he adds, “we’re completing the installation of eight stations [in Mali] in April.”

“We have good contacts [in Nigeria] and they’re working hard to get permission to put up stations right now in that area. We’ve shipped 15 stations which are ready to install. With these areas we can’t go visit, it’s all about collaboration. It’s about partners and people you know. We have a partnership with a tremendous group of Africans who are really the leading edge of this whole thing.”

Excited students running towards the camera

Most TAHMO weather stations are hosted and maintained by a local school.

A Hopeful Future

Despite the challenges of getting this large-scale research network off the ground, Selker and his group remain hopeful. About his weather data he says, “It’s not glamorous stuff, you won’t see it on the cover of magazines, but these are the underpinnings of a successful society.”

Selker optimistically adds, “We are in a time of incredible opportunity.”

Learn more about TAHMO

Next Week: Read an interview with Dr. John Selker on his thoughts about TAHMO.

See weather station performance data for the ATMOS 41.

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Feb 6

Get More From Your NDVI Sensor

Modern technology has made it possible to sample Normalized Difference Vegetation Index (NDVI) across a range of scales both in space and in time, from satellites sampling the entire earth’s surface to handheld small sensors that measure individual plants or even leaves.

Flat map of the earth depicting NDVI amounts covering the contents

Figure 1: NDVI is sensitive to the amount of vegetation cover that is present across the earth’s surface.

NDVI – Global

The broadest way to think of NDVI is data obtained from an earth orbiting satellite. In the figure above, you can see highly vegetated areas that have high NDVI values represented by dark green colors across the globe. Conversely, areas of low vegetation have low NDVI values, which look brown. NDVI is sensitive to the amount of vegetation cover that is present across the earth’s surface.

NDVI – Local

How might NDVI be useful at the plot level? Figure 2 below shows a successional gradient where time zero is a bare patch of soil, or a few forbs or annual grasses. If we leave that patch of ground for enough time, the vegetation will change: shrubs may take over from grasses and eventually we might see a forest. Across a large area, we may also move from grasslands to forest. In an agricultural system, there is yearly turnover of vegetation—from bare field to plant emergence, maturity, and senescence. This cycle repeats itself every year. Within these growth cycles NDVI helps to quantify the canopy growth that occurs over time as well as the spatial dynamics that occur across landscapes.

Diagram depicting seasonal growth plotted against spatiotemporal variation

Figure 2: Seasonal growth plotted against spatiotemporal variation

Spectral Reflectance Data

So where does NDVI come from? In Figure 3, the x-axis plots wavelength of light within the electromagnetic spectrum; 450 to 950 nm covers both the visible region and a portion of the near infrared. On the y-axis is percent reflectance. This is a typical reflectance spectrum from green vegetation.

Chart reflecting data and electromagnetic radiation

Figure 3: Spectral Reflectance Data. (Figure and Images: landsat.gsfc.nasa.gov)

The green hyperspectral line is what we would expect to get from a spectral radiometer. Reflectance is typically low in the blue region, higher in the green region, and lower in the red region. It shifts dramatically as we cross from the visible to the near infrared. The two vertical bars labeled NDVI give you an idea of where a typical NDVI sensor measures within the spectrum. One band is in the red region and the other is in the near-infrared region.

NDVI capitalizes on the large difference between the visible region and the near infrared portion of the spectrum. Healthy, growing plants reflect near-infrared strongly. The two images on the right of the figure above are of the same area. The top image is displayed in true color, or three bands–blue, green and red. The image below is a false color infrared image. The three bands displayed are blue, green, and in place of red, we used the near infrared. The bright red color indicates a lot of near infrared reflectance which is typical of green or healthy vegetation.

The reason NDVI is formulated with red and near infrared is because red keys in on chlorophyll absorption, and near infrared is sensitive to canopy structure and the internal cellular structure of leaves. As we add leaves to a canopy, there’s more chlorophyll and structural complexities, thus we can expect decreasing amounts of red reflectance and higher amounts of near-infrared reflectance.

How Do We Calculate the NDVI?

The Normalized Difference Vegetation Index takes into account the amount of near-infrared (NIR) reflected by plants. It is calculated by dividing the difference between the reflectances (Rho) in the near-infrared and red by the sum of the two. NDVI values typically range between negative one (surface water) and one (full, vibrant canopy). Low values (0.1 – 0.4) indicate sparse canopies, while higher values (0.7 – 0.9) suggest full, active canopies.

The way we calculate the percent reflectance is to quantify both the upwelling radiation (the radiation that’s striking the canopy and then reflected back toward our sensor) as well as the total amount of radiation that’s downwelling (from the sky) on a canopy. The ratio of those two give us percent reflectance in each of the bands.

Next Week: Learn about NDVI applications, limitations, and how to correct for those limitations.

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Jan 2

Lysimeters Determine if Human Waste Composting can be More Efficient

In Haiti, untreated human waste contaminating urban areas and water sources has led to widespread waterborne illness. Sustainable Organic Integrated Livelihoods (SOIL) has been working to turn human waste into a resource for nutrient management by turning solid waste into compost. Read more…

Estimating Relative Humidity in Soil: How to Stop Doing it Wrong

Estimating the relative humidity in soil? Most people do it wrong…every time. Dr. Gaylon S. Campbell shares a lesson on how to correctly estimate soil relative humidity from his new book, Soil Physics with Python, which he recently co-authored with Dr. Marco Bittelli. Read more.…

How Many Soil Moisture Sensors Do You Need?

“How many soil moisture sensors do I need?” is a question that we get from time to time. Fortunately, this is a topic that has received substantial attention by the research community over the past several years. So, we decided to consult the recent literature for insights. Here is what we learned.

Data loggers: To Bury, or Not To Bury

Globally, the number one reason for data loggers to fail is flooding. Yet, scientists continue to try to find ways to bury their data loggers to avoid constantly removing them for cultivation, spraying, and harvest. Chris Chambers, head of Sales and Support at Decagon Devices always advises against it. Read more…

Founders of Environmental Biophysics: Champ Tanner

Image: http://soils.wisc.edu/people/history/champ-tanner/

We interviewed Gaylon Campbell, Ph.D. about his association with one of the founders of environmental biophysics, Champ Tanner. Read more…

And our three most popular blogs of all time:

Do the Standards for Field Capacity and Permanent Wilting Point Need to Be Reexamined?

We asked scientist, Dr. Gaylon S. Campbell, which scientific idea he thinks impedes progress. Here’s what he had to say about the standards for field capacity and permanent wilting point. Read more…

Environmental Biophysics Lectures

During a recent semester at Washington State University, a film crew recorded all of the lectures given in the Environmental Biophysics course. The videos from each Environmental Biophysics lecture are posted here for your viewing and educational pleasure. Read more…

Soil Moisture Sensors In a Tree?

Soil moisture sensors belong in the soil. Unless, of course, you are feeling creative, curious, or bored. Then maybe the crazy idea strikes you that if soil moisture sensors measure water content in the soil, why couldn’t they be used to measure water content in a tree? Read more…

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May 14

1 Comment

Is Average Relative Humidity A Meaningless Measurement? (Part II)

Scientists often misunderstand average relative humidity (see part I). In fact, it’s not uncommon to encounter average relative humidity being misused in scientific literature. This week, learn which measurement should be used instead.

Humid conditions in a pine forest.

What is Wrong with Average Relative Humidity?

We often use average values to illustrate the behavior of parameters over time. One of the most common is air temperature, where we effectively graph average half-hourly temperature across a day or daily temperature across a year to show important details about the environment. But, consider what average relative humidity would look like.

As noted above, a general rule, though not consistent everywhere, is that the temperature at night cools down to the point where the air is saturated and the relative humidity is 100% (1). During the day, depending on the climate and weather, the saturated vapor pressure may increase roughly two to five times ea and relative humidity would be between 0.2 to 0.5. If we calculated an average for the day, it would most likely be between 0.6 and 0.75, no matter what environment was being measured. Of course, if it were raining or in the winter with low incoming radiation, this would be higher. Still, it is easy to see that an average relative humidity does not do much to define meteorological conditions.

The title of this chart is misleading because they were not averaging across the day, but only daily at noon. Image: Britannica.com/

What Should We Use Instead?

The measurement that should be reported is vapor pressure. Not only is it independent of temperature, but it can also be effectively averaged over time to show ecosystem behavior. However, this value will not be helpful to scientists who are identifying the pull generated by the atmosphere for water vapor in the plant or soil. This quantity is called vapor deficit and is calculated by taking the difference between the saturation vapor pressure and ea.

We sense water deficit in the atmosphere through our skin.

As humans, we intuitively sense the deficit when we feel that the atmosphere is dry through drying of our lips or our skin. The same is true for plants. The dry atmosphere will exert a higher pull on the water, pulling it out through the leaves. The higher the difference between the vapor pressure and the saturation vapor pressure, the more pull for water. Although sometimes reported in literature, the most common use for vapor pressure is as a standard input to evapotranspiration models like FAO56 or Penman-Monteith.

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May 5

1 Comment

Is Average Relative Humidity A Meaningless Measurement?

Relative humidity is one of the most widely reported weather parameters and is familiar to most people.

Scientists sometimes misunderstand relative humidity.

Still, it is not uncommon to encounter it being misused. Here are two examples:

My sister recently stated that her son was experiencing 45℃ and 100% humidity while walking around during the day in the Philippines.
In scientific literature, I often find figures displaying daily average relative humidity over a period of weeks or months.

Both of these examples show a misunderstanding of what relative humidity is and how it can be used.

What is relative humidity?

Relative humidity (h_r) is the ratio of the vapor pressure (e_a) in the air over how much vapor pressure there could be if the air were saturated at that air temperature (saturated vapor pressure, e_s(T_a)).

While vapor pressure is a reasonably conservative quantity, meaning it doesn’t change drastically with time (i.e.hours), e_s(T_a) is solely tied to temperature, shown by the empirical Tetens equation:

where T_a is air temperature, and b =17.502 and c = 240.97℃ (constants). As the equation shows, saturated vapor pressure is only a function of temperature, so relative humidity in natural conditions will simply show a sinusoidal pattern that is inverse to air temperature.

When humidity is higher, the vapor concentration difference is smaller so we lose less water, reducing our ability to cool.

Why do we estimate it poorly?

When temperatures are elevated above our comfort zone, we begin to feel hot. Our bodies, which are adept at keeping us cool, evaporate water from our skin to return us to a comfortable skin temperature. When humidity is higher, the vapor concentration difference is smaller so we lose less water, thus reducing our ability to cool. In an attempt to balance the humidity, our body moistens the skin surface with sweat, leaving us feeling damp and sticky. This makes us feel like the air is nearly saturated, but in reality, the higher humidity has simply limited our ability to cool ourselves.

It is a relatively simple thing to convince ourselves that daytime humidities are never 100% unless it’s raining. We know that daytime temperatures are almost always higher than nighttime, due to solar radiation. And, we are familiar with dew that forms on surfaces as nighttime temperatures cool to the point that they begin to condense water out of the air (dew point temperature). If we assume that the vapor pressure of the air (e_a) is the same as the saturation vapor pressure when the dew began to form (nighttime low temperature), then any air temperature throughout the day (T_a, which we assume would be higher) generates a saturation vapor pressure (e_s(T_a)) that is higher than e_a and thus, relative humidity would be less than 1.

So, what about my nephew in the Philippines? Right now, a typical low temperature is 24℃ with a high of 34℃ (when it’s not raining). Under that scenario, the relative humidity, although it would feel quite high, would only be around 56% at midday.

Next Week: Learn what’s wrong with using average relative humidity in scientific papers and what measurement should be used instead.

See weather sensor performance data for the ATMOS 41 weather station.

Explore which weather station is right for you.

Download the “Researcher’s complete guide to soil moisture”—>

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Measuring and Modeling the Environment

Posts tagged ‘Transpiration’

Current textbook knowledge of plant dehydration

How stomatal conductance varies with water potential

Taking advantage of P50

Irrigation Curves – a rapid assessment of plant hydration

Example irrigation curves

Further research is required

Reference

Determining Tree Stem Water Content in Drought Tolerant Species

Temperature Calibration Was Essential

Water Use Depended on Landscape Position

Implications

What Does it All Mean?

Big Data, Big Governments, and Big Unknowns

Terrorism, Data, and Open Doors

A Hopeful Future

NDVI – Global

NDVI – Local

Spectral Reflectance Data

How Do We Calculate the NDVI?

And our three most popular blogs of all time:

What is Wrong with Average Relative Humidity?

What Should We Use Instead?

What is relative humidity?

Why do we estimate it poorly?