# Posts by Doug Cobos

## Using The Salt Balance Approach to Measure Soil Drainage

Understanding the amount of drainage that comes out of the bottom of the root zone and infiltrates into groundwater recharge is a very difficult measurement to do well. Drain gauges do a good job of it but on a small scale. Large lysimeters do an even better job, but are extremely expensive and complex.  There is an economical alternative, however, called the salt balance approach to measuring drainage.

Soil profile underneath canola

## The Salt Balance Approach

Since the majority of non-fertilizer salts in the soil solution don’t get taken up by plants, this salt can be used in soil as a conservative tracer.  This means that whatever salt is applied to the soil through rainfall or irrigation water is either stored in the soil or leaches through the profile with the soil water, enabling us to use conservation of mass in our salt balance analysis. The electrical conductivity of water (ECw) is directly proportional to the salt concentration, so ECw can be used in place of salt concentration in this analysis.  If you measure the EC of the water that’s applied to the soil, either through irrigation or precipitation,  as well as the EC of the water that’s coming out of the bottom of your profile, then you can calculate what fraction of the applied water is being transpired by the plants, and what fraction is draining out of the bottom.  This method is useful for measuring water balance at field sites.

To illustrate this concept, let’s work through a simple example.  A particular field received 40 cm of water through precipitation and irrigation.  The average ECw of the precipitation and irrigation water is 0.5 dS/m.  Measurements of ECw draining from the soil profile below the root zone indicate an ECw of 2.0 dS/m.  The drainage or leaching fraction can be easily calculated as :

ECw(applied) / ECw(drained) = 0.5 dS/m / 2.0 dS/m = 0.25

The amount of water drained can also be easily calculated as:

Leaching fraction * applied water = 0.25 * 40 cm = 10 cm

## Measuring Pore Water EC (ECw)

One challenge to this approach is the measurement of water electrical conductivity itself.  Bulk EC is a relatively simple measurement, and several types of soil water content sensors measure it as a basic sensor output.  However, the electrical conductivity of water, called pore water EC (ECw), is more complex.  Pore water EC requires that it be either estimated from the bulk EC and soil water content or that a sample of pore water be pulled from the soil matrix and measured.  When estimated, pore water EC can contain considerable error.  In addition, removing a water sample and measuring the pore water EC is not easy.

## Should We Replace “Wind Chill Factor”?

In a continuation of our series, based on this book, which discusses scientific ideas that need to be reexamined, Dr.’s Doug Cobos and Colin Campbell make a case for standard operative temperature to replace wind chill factor:

Currently, the forecast is based on air temperature and wind chill. What the forecast leaves out is the effect of radiation.

What are we looking for when we look at a weather forecast?  We want to know how we’re going to feel and what we need to wear when we go outside. Currently, the forecast is based on air temperature and wind chill, which are a major part of the picture, but not all of it.  What the forecast leaves out is the effect of radiation.  If you go out on a cold, sunny day, you’re going to be warmer than you would be at that same temperature and wind speed on a  cloudy day.  It’s not going to feel the same.  So why not replace wind chill with the more accurate measurement of standard operative temperature?

## Where wind chill came from:

In 1969, a scientist named Landsberg created a chart showing how people feel at a certain air temperature and wind speed. His chart was based on work by Paul Siple and Charles Passel.  But, Siple and Passel’s work was done in Antarctica using a covered bottle of water under the assumption that you were wearing the thickest coat ever made.  The table was updated in 2001 to improve its accuracy, but since the coat thickness assumption remained unchanged it underestimates the chill that you feel. It also explicitly leaves out radiation, assuming the worst case scenario of a clear night sky. The controversy is detailed in this NY Times article from several years ago.

Siple and Passel’s work was done in Antarctica using a covered bottle of water under the assumption a person was wearing the thickest coat ever made.

During the winter, forecasters use air temperature and wind chill with no radiation component.  In the summertime, they use an index that takes into account the temperature and the humidity called the heat index.  But again, there is no accounting for radiation. Our families deal with this all the time when we take the kids out fishing in early spring. Before we leave, we’ll check the weather report for temperature and wind chill.  But is it going to be sunny or cloudy?  That’s key information. You can see the radiation effect in action when a cloud drifts in front of the sun.  All the kids scramble for their jackets because the perceived temperature has changed.  This is something that none of the indices actually capture.

## Understanding the concept:

Standard operative temperature combines the effects of radiation and wind speed to give a more complete understanding of how you will feel outside.  It is a simple energy balance: the amount of energy coming in from the sun and metabolism minus the amount of energy going out through heat and vapor loss. Using this relationship and adding in the heat and vapor conductances, the temperature that we might “feel” can be graphed against the solar zenith angle at a fixed air temperature. For reference, the sun is directly overhead when the zenith angle is 0 degrees and at the horizon at 90 degrees.

Figure: Wind chill and standard operative temperature with respect to sun angle for two wind speeds (1 and 10 m/s) at an air temperature of -5 degrees C.

What’s interesting is that on a clear day when the sun is around 45 degrees (typical for around noon in the winter) and the temperature is -5 degrees C, if the wind is blowing at 1 m/s, you would feel a temperature of 6 degrees C (relatively warm). The wind chill predicts the feel at -6 degrees C, a huge difference in comfort.  This difference decreases with increasing wind speed as you’d expect, but even for the same conditions and wind at 10 m/s, the 45-degree sun angle creates a temperature feel 7 degrees C higher than the wind chill.  Although not huge, this makes a considerable difference in perceived comfort.

## What do we do now?

The interesting thing is that all the tools to measure radiation are there. Most weather stations have a pyranometer that measures solar radiation, and some of them even measure longwave radiation, which can also be estimated within reasonable bounds. This means forecasters have all the tools to report the standard operative temperature, which is the actual temperature that you feel.  Why not incorporate standard operative temperature into each forecast? Using standard operative temperature we could have the right number, so we’d know exactly what to wear at any given time.   It’s an easy equation, and forecast websites could use it to report a “comfort index” or comfort operative temperature that will tell us exactly how we’ll feel when we go outside.

Which scientific ideas do you think need to be reexamined?

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

## Volumetric Water Content: Keeping your Eye on the Goal

Most scientists agree that it’s productive to attend seminars and conferences in order to talk with peers, share ideas, and learn about what other scientists are doing. However, in both academia and industry, we need to be careful that we are not so easily influenced by other scientists’ opinions that we lose sight of the end goals of our own projects. This happened to us recently at Decagon. Here’s the background: our volumetric water content (VWC) sensors actually measure the dielectric permittivity of the soil and use a transfer function to predict VWC from the measured dielectric value. Most of our sensors receive a “dielectric calibration” during the production process where they are calibrated in five dielectric standards to make sure they all measure dielectric permittivity accurately, thus leading to accurate VWC measurements with our standard transfer function.

Volumetric water content (VWC) is determined by measuring the charge storing capacity of the soil using capacitance/frequency domain technology.

We were doing a pretty good job calibrating these sensors in dielectric standards, and our default dielectric-to-VWC transfer function resulted in good VWC accuracy. Then we went to a series of meetings and talked to some of our researcher friends who work on instrumentation. They said, “Look, your water sensors aren’t reading as accurately as they should in dielectric permittivity.” Here’s where the trouble started…

Wanting to make the perfect instrument, we went back and re-evaluated the dielectric calibration standards for these water content sensors and tried to use the book values of dielectric permittivity. This was a bad idea because it fundamentally changed the sensor output. Now, despite the sensors giving a slightly more accurate value for dielectric permittivity, they gave less accurate measurements of VWC. Compounding the problem, we now had a population of sensors that didn’t read the same as earlier sensors of the same type. So when customers started replacing their old sensors they said, “Wait a minute, this sensor reads 4% higher water content than my old water content sensor.” That’s when we realized that we had a real problem.

Our underlying mistake here is that we failed to remember that 99% of the people who buy our VWC sensors don’t even care what dielectric permittivity is. They just want an accurate, repeatable measurement of soil moisture. Essentially, because we were so focused on trying to produce a theoretically perfect sensor for a vocal minority of technically savvy users, we lost sight of the practical matter. Did our sensors produce an accurate water content measurement?

I wonder how often this happens in academia and industry. Scientists are bombarded with input from so many different stakeholders, it’s sometimes difficult to maintain the original focus of their projects. We need to remember to focus on the end goal and filter out things that may distract from that goal.