swsihyd - Civil & Environmental Engineering at the University of

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Surface Water Supply Index
Water resources managers across the west benefit by
knowing how much water to expect from snowmelt
runoff.
Forecasts are generally made by correlating
snowpack, precipitation, antecedent moisture
conditions, climatic variables such as SOI,
(Southern Oscillation Index) etc with streamflow.
Other models are also employed to forecast
streamflow ranging from a seasonal to a daily
basis.
In a management sense, it is often very useful to
know if the current year is similar to preceeding
years for which you have a record - not only of the
hydroclimatic conditions (snowpack, precipitation,
runoff characteristics, temperatures, etc) but of
the past management actions for that year as well.
This gives one the information on what was done for
a given set of circumstances, why it was done and
the ultimate outcome of those actions both positive
and negative.
This is typcally called an ‘analog year’ type
analysis. It is most valuable when the current
year closely mimics the selected analog in all
characteristics or when certain
areas/characteristics are not close but the
differences are objectively quantifiable and the
differences in the outcome are predictable and
quantifiable.
This also
different
outcomes,
critique:
gives a manager the opportunity to assess
management strategies and their potential
by having one or more analogs to
did the outcomes meet the objective and
if not, what other options exist that could be
employed given the current circumstances.
Surface water supply indexes (SWSI’s) are
calculated to provide an indicator of how much
surface water will be available for use in any give
season.
As the name implies, it is only surface water
categorized here. Ground water may be an important
component in the total water supply picture, but in
general, groundwater information is more difficult
to obtain, especially timely information for
processing, it is generally more constant water
source with pumping costs being the variable of
interest. Given the data, total water supply
indexes could be generated (including ground water
sources) however, for convenience, jurisdictional
considerations and timely product generation - and
consistency from area to area, surface water is
generally what is categorized. Cost also enters in
this equation - ground water is expensive relative
to surface water and given an option - all other
factors being equal, managers use the cheapest
source first and most.
SWSI’s came about in the mid 80’s as a way to
quantify in objective terms the total resource
available to managers. The question generally was
“how are we doing water wise this year?” and the
answer was generally some variation of ‘runoff will
be very poor - however reservoir storage is really
high’ so all is ok, or runoff will be poor and
reservoir storage is equally bad - bad drought,
runoff is great and reservoir storage is
overflowing - floods, or runoff will be great but
reservoir storage is poor -still ok. All these
combinations needed some kind of objective analysis
so that folks weren’t just shooting from the hip.
Thus SWSI’s were conjured up - a way to objectively
quantify where we are today with respect to
historical occurrences.
Early SWSI’s were first developed in Colorado and
Oregon. They used snowpack, precipitation as
indeces of future runoff, current streamflow and
reservoir storage as indeces of current conditions.
These tended to be problematic in terms of data
collection - streamflow was a weak link in the
system.
Newer version dropped the streamflow component in
favor of forecast runoff, typically an April-july
forecast volume or in some cases, April- September.
Other problems cropped up as various states began
to develop SWSI’s and the problem of consistency or
comparability once again popped up. Many states
did not have year round reservoir storage data,
especially for smaller reservoirs managed by small
water districts. During the ‘non management
season’ many reservoir operators do not keep
storage data, inflow or outflow data for their
operations - basically a set the gate and walk away
till the runoff and irrigation season starts again.
Also, many areas did not see a need for a
continuous, 12 months per year SWSI, preferring a
relatively simple application that would be a
January through June system. There was basically
no need for product generation during the fall and
winter. There are still inconsistencies from area
to area but they are becoming more consistent over
time.
Forecast values of runoff have replaced observed
values of snowpack and precipitation as one of the
main components of the SWSI. The rational behind
this is: forecast runoff is based on snowpack,
precipitation, and other variables. Thus by using
forecast runoff, those variables are automatically
incorporated into the system, reducing the amount
of processing required. Observed or projected
reservoir storage is the other main components
currently used.
The process is available to all who wish to use it
for whatever purpose - such as an engineering firm
developing a SWSI for a client.
The Process:
1. Historical data collection and quality control
a. observed runoff for the point and time frame
in question (typically April-July runoff, or
monthly if running a continuous system)
b. Reservoir storage - (monthly for April July, January-July or all months depending on what
type of SWSI you intend to set up.)
* Side note: remember that reservoir data are
typically ‘EOM’ or end of the month values so that
an April EOM is taken on April 30, not on April 1 I know this seems stupid and yes it is (technically
EOM is at 00:00 which could be taken as the first
or the 30th of any month but that’s how it is in
reservoir data base archival) therefore, an April
EOM would correspond with May 1 forecast, snowpack
and precipitation conditions.
Also on reservoir data, make sure you know what the
data represent: total storage, live storage, some
combination of live storage (conservation pool,
dead storage, fish pool, etc) or useable capacity
and that whatever data you use will be replicatable
in the future. For example - the total storage in
Bear Lake is near 5 million acre feet and some but
the published lake content is 1,421,000 or 1.4
million acre feet which is the ‘useable capacity’
or that amount of water that can be withdrawn for
use, that which is tied to a water right (somebody
owns it and can call for it).
2. If using more than one reservoir (basin wide
SWSI), collect all the appropriate reservoir and
streamflow data - a SWSI can be as large or small
as the application requires.
3. Construct a matrix in a spreadsheet of the
following form:
1.
Number
2.
Year
3.
Resv
4.
stream
5.
Comb
6. 7.
Prob SWSI
Where: number is the number of years, 1 through
whatever the highest year is,
Year is the year of occurrence
Resv is the total reservoir storage, in acre feet
(single reservoir or the combined storage of all)
Stream is the observed runoff data in acre feet for
the SWSI point
Comb is the sum of the reservoir storage data and
the observed streamflow data in acre feet.
Prob is the probability of occurrence.
average condition.
50% is the
SWSI is the actual SWSI value.
The final 3 columns are calculated from the first 4
columns. The combined reservoir and streamflow
column is self explanatory - simply the summ of Yi
(streamflow and reservoir) for any year. This is
the total surface water supply available for that
season.
Probability is actually the plotting position as no
probability density function is assumed - you may
if you wish, assume a function and simply apply the
appropriate formulas to assign a probability to the
point. Most PP’s use the Weibull equation of
M/(n+1)
* Sidenote: the probability is the best overall
categorization for reference. It is intuitive,
easily explained and can be used to compare area to
area in quantitative terms. The SWSI value on the
other hand, is a scale of -4 to +4, a conversion
from the probability scale that makes the SWSI
“comparable to the Palmer soil moisture Index” also
of the same scale. it is not intuitive, not easily
understood nor comparable from area to area due to
the linear conversion from a probability function
to an artificial scale. (a SWSI of -2.1 is not the
same from area to area, depending on the sample
population and its fit with respect to the PDF,
whereas a probability of 30% is) I prefer to use
probability and candidly think that both the SWSI
and palmer scales stink.
SWSI calculation: this simply converts a scale of 0
to 100 to a scale of -4 to +4.
(probability - 50)/12
You now have a matrix ready for an ascending sort
function. The probability column, number column
and the SWSI column are static - they are never
included in the sort function.
The sorting must be done on the combined reservoir
and streamflow column as the principal column -it
is this column that dictates the total water supply
for any season and is of most interest. The other
3 columns, the year, streamflow and reservoir data
are sorted along with the combined data as a BLOCK
- the combined column determines where each year
ends up, but we want the year and the other data to
remain with their associated combined total.
What you have now is a matrix sorted in ascending
order - lowest to highest of water supply
conditions. You can see the probability of any
occurrence either high or low. You can see what
component caused it to be in its specific position
and you can insert the current years data to
determine where you are. Given this position, you
can see the years that are analogs to this year for
management analysis.
This system is simple, uses a minimum of data,
easily maintained and operated and conveys a
maximum of information for operations.
Anything above 90%: roll up your pants and take the
brick out of the toilet.
Anything below 10%: shut the reservoir gates and
conserve all you can.
Historically, you can then go back and determine
where various events were classified such as
agricultural water shortages and set relative SWSI
values: anything below a SWSI of X.X and water
shortages are likely, or water rationing or
flooding, etc.
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