SNOW HYDROLOGY: SNOTEL
Instructor: Randall Julander
Phone: 524-5213
SNOTEL
History
In the beginning, there was snow. Man really hated snow, women also. Snow was cold.
Man was dang near butt naked. Man moved to the desert. Desert food did not include
Swiss Chocolate, beer was served warm and made from horse milk and spit. Even soda
served warm and flat. Heat made man really crazy, made man make war on everything.
Sensible Man moved back to snow, eat hairy mammoth, invent double Swiss mocha latte
to stay warm. Crazy man stay in desert, eat bugs, get nice tan.
Fast forward many years, at least to recognizable English.
As you recall from previous notes, the relationship between snow and runoff was
recognized in the late 1800 and early 1900's and methodology invented to quantify the
snowpack in an effort to predict water supply. As time progressed, further advancements
and observations revealed that additional data were required to improve accuracy and to
provide input to the new conceptual models being developed. Statistical models were
great, but provided very limited information. In order to get a more realistic hydrograph
simulation; a finer time scale of data was needed. (A hydrograph of 1 point is not a very
impressive figure!) Flooding and daily snowmelt are correlated, and again, monthly data
do not provide sufficient data for any analysis, except for an extremely gross post
mortem.
Recall, also, that manual snow surveys take what is know as a destructive sample: once
taken, that sample can never be "resampled" - it's gone forever, scattered somewhere
OFF OF THE SNOWCOURSE (as you were properly trained to do in previous labs)!
Therefore, sampling once per day or per hour is infeasible, impossible and a whole bunch
of other I words. A technique for taking repetitive, nondestructive samples of snowpack
parameters was necessary.
Note: SNOTEL is a system that is currently replacing the original Snow Course system
because: more frequent data were required, more types of data were required - but the
original problem is STILL AT THE CORE OF THE PROGRAM - WATER
MANAGEMENT. Thus this new system has all the same site location characteristics as
the original system and therefore, all the same data application limitations! While
yielding more data, it may not be as appropriate for some analyses. (bottom line, people
will use whatever data are available - whether they are strategically placed the most
appropriate location or not: something is better than nothing principle)
In the 70's!
Enter the SNOW PILLOW! The pillow, made of rubber, steel, hypalon, etc, is filled
with an antifreeze solution (50/50 mix of water and ethanol with flouresceine dye to
make it a flourescent green). As the snowpack accumulates on the pillow, it increases the
fluid pressure (obviously as this is a quasi closed system) within the pillow. By running a
fluid line to a shelter and then up the wall, where, there is, conveniently, an engineering
tape - you have a manometer! Viola! Inches of water contained in the snowpack is easily
read, like it was off the bathroom wall, where most valuable information is posted!
3 Steel Pillows
10' Hypalon pillow
What we now have, is a replicable, non-destructive sampling device that gives us snow
water equivalent. What we have lost is: depth - this system only yields snow water
equivalent, and therefore we do not have any depth, and hence density observations.
At this point, we still have to visit the site each time we want to record a data point - we
have solved only the destructive sample problem. Enter now, the pressure transducer, a
data sampling and storage device and a transciever/reciever system. The pressure
transducer is simply an electronic device that when tickled, gives a voltage reading
dependent on the pressure put against a plate or bellows of some type. The higher the
pressure, the higher the voltage reading. This device is then calibrated and a
mathematical conversion equation utilized to convert voltage into snow water equivalent
in inches! The data logger or equivalent computerized device simply determines when to
take a reading, processes the information, makes necessary conversions, tacks a time
stamp to the data and stores it till it can be transferred to the central data collection
platform. The transceiver/receiver is simply the radio function.
In SNOTEL, it is impractical to use line of sight radio systems due to the steep
mountainous terrain where sites are located. Satellite transmission is equally impractical
due the problems associated with satellite angle and mountain interference. Using a ultra
low frequency, the master station broadcasts continuous radio signals into the atmosphere
which in turn bounce off of ionized meteoric dust randomly onto the earth's surface. If a
station happens to be hit, it responds within milliseconds, sending a return signal with all
appropriate data back to the master station. Utilizing this mechanism, stations can be
located in very radio wave impenetrable locations.
Precipitation gages work is much the same manner. These gages are storage type gages:
that is to say, everything that falls into the gage is kept and the amount of precipitation is
calculated from one time increment to another. For example the storm that happens
today yields today's reading of 31.3 and yesterday had a reading of 30.2 giving a storm
total of 1.1 inches.
Storage Rain Gage
On this gage, notice the baffles at the top. This is known as an Alter Wind Screen. This
device breaks up the wind currents and allows snow to fall vertically into the gage as
opposed to flowing horizontally across the orifice. As wind blows across a non-screened
gage, a small pillow of air mounds across the orifice and light density snow can bounce
off this higher pressure pillow to the outside. Horizontally falling snow has a
substantially smaller target than does vertically falling snow as well. Non-screened gages
display significantly lower precipitation catches than do screened gages. If, in the station
history of a site, a gage that historically has not had a screen is fitted with one, it,
essentially is a NEW SITE. The historical record will be significantly different than the
current one. Comparing same site pillow data to non-screened precipitation data will
yield conflicting results as well.
The precipitation gage is plumbed as the pillow was, to a manometer inside a shelter,
fitted with a pressure transducer and various electronics for data storage, calculation and
transmission.
This was the original SNOTEL system: pillows and precipitation gages. A lot of
chewing gum and duct tape made the system work in the early years: custom interface
boards, electronics sensitive to moisture and cold made for a very temperamental system.
There were many sensor failures and a high learning curve. Later, in the 80's,
temperature sensors were added as a standard parameter. These are thermistor type
sensors with a large range: about -40 to 160 degrees.
In recent years, other sensors are starting to be added as well: sonic depth sensors, wind
speed and direction, soil moisture, soil temperature, relative humidity to name a few.
Sonic depth sensor, solar panels, etc
Since this system is truly remote (it’s a 2 day pack trip into Lakefork Basin at the foot of
Mt Lovenia), all sites are self contained with batteries and solar power. In recent years,
sensors have become much more reliable, electronics are becoming plug and play and the
sites are much more stable. Data quality has always been good, but has increased
substantially over the years.
As a system, SNOTEL, is the biggest provider of snowpack and high elevation climatic
data in the world (and its only in the western United States!). This also indicates that
there are very few other sources of data. The SNOTEL system, as of 2000, has about 650
sites in the Western US. Utah has about 85 sites. Daily historical data are available for
SWE, PCP and TEMP.
DATA QUALITY
Oh ya Baby! We got data, we got data!, lotsa data! Check your
guns at the door, young engineers: remember there are ALWAYS
hidden gotcha's in the data! If possible, get the standards of site
maintenance and data quality control.
PILLOWS
Remember, with pillows, we are NOT measuring snow water
equivalent! (but,,, but,, but: you said just up here a page or two
that we had snow water equivalent measurements! Yeah, right! I
did and I LIED!) what we have is not snow water equivalent but
WEIGHT! And weight and snow water equivalent are
synonymous most of the time. However, just like a BYU Coed,
weight can be more than it seems! There are other ways for weight
to be transferred onto and off from the pillow system. Bridging
occurs in a snowpack when an exceptionally strong snow layer
(such as when freezing rain hits the pack and creates an ice layer
several inches thick) supports the weight of additional snowpack
on top of it. This creates an under-weigh condition on the pillow.
Snow creep and glide exerts tremendous lateral and vertical
pressure on a pillow system creating an overweigh situation.
Blowing snow can be re-deposited on a pillow system, again
creating overweigh (in this case, manual and pillow measurements
will be the same, but the data can be misleading, especially when
compared to the precipitation data). Conversely, snow can be
scoured from a pillow area, leaving less snow and a similar
comparison problem.
Pillows exhibit diurnal data fluctuations that are most likely due to
temperature changes and barometric pressure fluctuations. These
cycles are typically in the range of +0.2 to -0.2 inches per day but
on really lousy sites, may range up to 0.5 inches. Some sites are
rock solid and display very little variability. Bottom line: if you
compare similar time frames (i.e. midnight data to midnight data or
noon data to noon data) you will most likely get good relative
differences. Comparing midnight pillow data to noon pillow data
may give over or under differences depending on circumstances.
Current snowfall is very light compared to the static weight of a
seasons accumulation on a pillow. Snow has structure and
typically bonds well with the pack beneath it. It may take some
time for the full weight of new snow to be transmitted to the pillow
sensor below - thus hourly pillow data may not be entirely
reflective of the amount of new snow that actually fell during the
time frame in question.
The good news is that pillows for the most part, behave very well.
They respond reasonably quickly to new snow as well as melting
snow. They exhibit some quirks that, for the design purpose of
the system are more than acceptable. They have some
limitations that data users need to be aware of, that might limit the
extent of usability (for example modeling on a time scale finer that
1 hour or, depending, finer than 3 hours)
There are distinct differences in the way steel pillows behave as
compared to hypalon pillows. Steel pillows are far more
susceptible to data fluctuation. They typically are smaller in total
areal extent of measure and are usually installed in patterns of 2,3
and 4 to a site. Changing a pillow system from steel to hypalon
typically constitutes a 'change in sensor' and thus, a change in the
current record as opposed to the historical. Most areas are phasing
out steel in favor of the more stable hypalon pillows. The majority
of the historical data record is from steel pillows.
Other pillow problems: since they have fluid, they always have the
potential to leak. They are shot, stabbed, poked, prodded and
violated in more ways than can be discussed in open civil dialog.
They appear to be giant water beds surrounded by the loving,
gentle sounds and sights of nature, inspiring the passions of who
knows how many decadent nature lovers! In reality, they are cold,
lifeless, scientific instruments of hydrometeorological data
collection and are intended solely and singularly for that purpose
only.
Precipitation Gages
Precipitation gages have their own set of data quality problems.
They have all the plumbing problems that pillows have, they are
regularly shot with all kinds of hole making projectiles devices.
We had one gage shot so badly that it physically brought the gage
down, literally hundreds of 22 caliber bullets, shotgun blasts and
who know what else, it was more lead than aluminum. The wind
screen baffles are very attractive targets as they rock back and
forth after being shot, giving the shooter positive psychological
reinforcement.
Hit from behind: sneak attack by a tree from the rear!
What remains!
Precipitation data have some other unusual characteristics as well.
Since the gage is metal (aluminum) it is affected by temperature.
It expands in the day with warmer temperatures and contracts with
cooler temperatures. This allows the fluid inside to rise and lower
depending on temperature. Same type of diurnal fluctuation seen
in pillow data. Since the gage is open to the atmosphere, the nonfreezing solution inside must be covered by a layer of lightweight
mineral oil to prevent evaporation. This oil, at very low
temperatures, sometimes prevents low density snow from mixing
into the solution. This layer of snow, sitting on top of the oil, can
freeze in place causing a plug in the gage and thus preventing
subsequent snowfall events from registering on the transducer.
This problem can persist for many days until a warmer situation
prevails and the plug detaches from the gage wall and mixes with
the solution.
Data users feeding fine time scale models need to be aware of
these types of data quality issues that are inherent to the system
and for the most part - are not likely to be corrected.
Temperature data: Thermistors are used to gather temperature
data. These thermistors are calibrated at the factory and have no
adjustments. When they go bad, they are simply replaced. The
algorithm used to calculate average daily temperature is
significantly different than that used by most other data collectors.
SNOTEL gathers temperature data every 5 minutes and the
average is the average of ALL POINTS COLLECTED over the
24 hour time frame. National Weather Service average
temperature data are calculated using the (max+min)/2 method.
This is done, mostly because it is the only data available.
However, using these data in a degree day model may give
erroneous results for the following reason: Both the max
temperature and the min may have been very short time spikes due
to a break in clouds, a cold front, or other short term weather
phenomena. They certainly do not reflect an integrated
temperature sequence over a 24 hour time frame. The SNOTEL
system gives a far more accurate indication of total degree day
temperature than a max/min system.
Sonic Depth Sensors
Relatively new to the SNOTEL system, the sonic depth sensor uses
the same technology as any auto focus camera. A sensor, mounted
above the highest expected snow depth, sends an ultrasonic ping to
the snow surface. This ping is echoed back to the sensor and the
time from send to receive is recorded. An algorithm then
calculates the distance the ping traveled. Knowing the ground
surface, one can then calculate the depth of snow. Temperature is
a critical factor in this process as the speed of sound varies
dramatically with temperature. In weather conditions where
temperatures are changing rapidly, the temperature sensor often
does not respond quickly (heat sink) and thus the sensor may give
minutely inaccurate data till a stable temperature is attained.
Very light density snow and even heavily falling snow events can
fool the sensor as well. Light density snow tends to trap the 'ping'
and the sensor cannot hear the return. The ping can actually be
reflected off from heavy snowfall events, giving false readings.
The technology utilized by SNOTEL uses a comparative reading
algorithm. This is simply: take one reading, then take another: if
they are plus or minus 0.5 inch, accept the reading as valid. If the
readings fail to match, take to 10 more in an attempt to resolve the
discrepancy. This system cuts down really lousy data immensely.
Historical Data
The quality control of historical data, for the most part, has been
excellent. Data you obtain from the archive files have typically
been through several levels of quality control, including personal
inspection by hydrological personnel. Systematic errors are still in
the data however - that is to say: errors associated with sensor
changes, site relocations, etc. If a site changed from 3 steel pillows
to a hypalon, this constitutes a potential change in snow water
equivalent data that must be examined prior to using for model
calibration or for average development.
All sensor instability due to temperature, plugging, leaks, etc were
edited and the data sanitized for use. Which of course, brings us to
another huge data problem: how do you know what has been done
to the data? Do you just accept what someone has done as the best
possible? For the most part, as engineers, yes you will because of
the legal liability - by accepting data as quality controlled by a
federal agency, you transfer legal liability to that agency (I used
their data, they quality controlled it according to this standard, they
accept responsibility)
NEW STUFF!
Additional sensors are currently being tested for application to the
SNOTEL system.
a. Gamma scintilator counter that measures incoming solar
gamma at the ground level. This has the potential to
replace pillows with a fluidless sensor - nice from a
plumbing standpoint. It will most likely have problems
with low and very high snowpacks as it measures in a
conical fashion. The area sampled increases with snow
depth. At very low snows, a very small sample is actually
recorded and the impacts of snow surface contamination
could be huge. At very high snowpacks, there may not be
enough incoming gamma to provide accurate hourly data.
b. Wind speed and direction - in use, current technology just need $$$ to add to the system. Not a high priority.
c. Soil moisture and temperature. Have the current
technology, adds a valuable hydrologic parameter that has
thusfar not been collected. Have several installed on City
Creek and above Centerville at Parrish Creek.
d. Solar Radiation: expensive, unreliable, troublesome, needs
frequent calibration, subject to bird tirds (they find it a
very convenient resting spot - and I do mean spot!)