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Flaming Gorge Reservoir
Evaluation of the Correlation between Historical Snotel Data
and Unregulated Observed April–July Unregulated Inflow
by
Rick Clayton
Connie Plato
Snow Hydrology 2000
University of Utah
2
Introduction
Flaming Gorge Reservoir, located on the Green River in northeastern Utah and
southeastern Wyoming, is one of several large water resource projects developed under
authority of the Colorado River Storage Project (CRSP). The project was authorized by the
Federal Government in 1956 to provide the upper basin states with additional storage capacity
so that these states could better meet the water delivery requirements stated in the Colorado
River Compact of 1902. This document apportioned the Colorado River water resources
among the upper basin and lower basin states and required that the upper basin states of
Wyoming, Colorado, Utah, and New Mexico, provide a minimum annual flow of 8.23 million
acre-feet (MAF) to the lower basin states of Arizona, California and Nevada. During drought
years, meeting this requirement can cause the upper basin states to have severe water
shortages. The construction of Flaming Gorge Reservoir, among other upper basin reservoirs,
provides insurance to the upper basin states that they will have adequate water resources to
meet the lower basin deliveries while still providing for upper basin demands, even in drought
years.
Flaming Gorge Dam was constructed from the period from 1958 through 1963 and is
located 43 miles north of Vernal Utah. The dam is a thin arched concrete structure that
provides 448 feet of head and 3.8 million acre-feet (MAF) of storage capacity along its 91 mile
long reservoir at its maximum water surface elevation of 6040 feet above sea level. The
contributing basin above the reservoir is the Upper Green River Basin. This basin, located in
the southwestern corner of Wyoming, provides 19,000 square miles of area between the
elevations of 6040 and 12960 feet above sea level and contributes on average 1.72 MAF of
inflow annually. The hydrology of this basin is snowmelt dominated as a result of three main
mountain ranges. Bordered by the Uinta Mountain Range to the south, the Wyoming Range to
the west, and the Wind River Range to the northeast, nearly 70% of the unregulated inflow into
Flaming Gorge can be attributed to snowmelt runoff. This can be seen in Figure 1 below
which shows the annual mean unregulated inflow hydrograph for Flaming Gorge Reservoir.
The unsteady and variable inflow into the reservoir makes regulating the water surface
elevation challenging. To maximize the storage facility while minimizing downstream
flooding potential, the water surface elevation is intentionally drawn down to provide storage
3
capacity for the spring runoff each year. Without some advanced knowledge of the expected
runoff volume, the reservoir operator has no way to determine how far to draw down the
reservoir in any given year. To help with this problem, the Colorado Basin River Forecast
Center (CBRFC) provides inflow forecasts of the expected unregulated inflow volumes during
each runoff season. Starting in January, the CBRFC issues a forecasted unregulated inflow for
the period from April through July(A-J).
Figure 1 (Flaming Gorge Annual Mean Unregulated Inflow Hydrograph)
Flaming Gorge Reservoir
Annual Mean Unregulated Inflow Hydrograph(KAF)
600000
Unregulated Inflow (KAF)
Unregulated Inflow (KAF)
500000
400000
300000
200000
100000
0
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Month of the Water Year
The A-J forecast issued by the CBRFC is a product of statistical models which take into
account the antecedent precipitation, climate conditions, snow pack levels and soil moisture
conditions of the basin. Each month, as new data becomes available, the CBRFC updates their
forecast. The forecast update that is issued in April is considered to be the first accurate
prediction of the A-J unregulated inflow because it is assumed that by April 1st, the snow pack
level has reached its peak for the year. The reservoir operator of Flaming Gorge uses the April
forecast to pattern its releases for the runoff season to satisfy the water users as well as any
downstream constraints. Advanced planning in reservoir operation is crucial to maximizing
the water and energy resources of the reservoir. By providing a schedule of the release pattern
several months into the future, the Western Area Power Authority (WAPA), the authority that
4
markets power generated at Flaming Gorge, can sell contracts that set the price and quantity of
electricity that will be delivered to the energy users during the runoff season. Environmental
groups are also interested in the scheduled release pattern for the runoff season because of the
endangered fish that live in the river below the dam. The operation of Flaming Gorge is
required under the National Environmental Policy Act (NEPA) to provide flows to the Lower
Green River that allow the endangered fish to survive. Recreational groups are also interested
in the release patterns as they affect rafting and fishing along the Lower Green River.
The development of the snotel data acquisition system to collect timely and accurate
snow related data was thought to be a major improvement over the older snow course methods
used previously. In the 1970s and 1980s, 16 snotel stations were installed in the Upper Green
River basin in locations that were thought to best reflect the snow pack condition for the basin.
Data has been collected at these stations for periods ranging from 14 to 22 years. Daily records
of the condition of the snow pack are now available. Previously, snow pack data was collected
once each month by the snow course program. It was assumed that the advancement in data
acquisition technology would provide for better forecasting of unregulated inflows into
Flaming Gorge Reservoir. The subject of this paper is to analyze this assumption and
determine if, in fact, the April forecasts issued by the CBRFC for A-J unregulated inflow
volumes into Flaming Gorge Reservoir are more accurate as a result of the snotel system.
Analysis Method
To determine if forecast accuracy has improved, two analyses were performed. First,
the snotel station data sets were correlated with actual A-J unregulated inflow into Flaming
Gorge to generate the most predictive regression equation possible for the data sets. The
regression equation was developed to predict A-J unregulated inflow based on April 1 snow
water equivalent (SWE) using all stations within the boundaries of the Upper Green River
Basin. The second analysis performed was a statistical evaluation of the accuracy of the
historical April forecasts that have been issued for Flaming Gorge. These forecasts were
compared to actual A-J unregulated inflows to determine the errors generated as well as any
trends that describe the forecast performance through time. The goal of these two analyses was
to answer three main questions. First, does the existence of the snotel data acquisition system
enhance the forecasting capabilities of the CBRFC for Flaming Gorge A-J unregulated
5
inflows? Second, of all the parameters that are used in the forecast process (i.e. snow pack,
soil moisture, antecedent precipitation), how much of the predictive capability is attached to
the snow pack data in comparison to the other parameters? Finally, if snow data was the only
data available for developing an A-J unregulated inflow forecast, what is the best regression
equation that could be developed for Flaming Gorge?
The snotel stations in the Upper Green River Basin are located in three distinct
geographic areas where snow accumulates. Four stations are located on the northern slope of
the Uinta Mountains in the Henrys Fork and the Blacks Fork sub-basins. The elevation of these
stations range from 8250 to 10100 feet above sea level. There are 7 stations located on the
eastern slope of the Wyoming Range with elevations between 7700 to 9500 feet above sea
level. The five remaining stations are located on the western slope of the Wind River Range at
an elevations ranging from 8750 to 9500 feet above sea level. Table 1-1 contains the names,
locations, elevations and average April 1st SWE for each snotel station in the Upper Green
River Basin.
Table 1-1 (Snotel Site Descriptions for the Upper Green River Basin)
Snotel Site Name
Elevation
Location
Average April 1
Hickerson Park
9150
Northern Slope of Uinta Mountains(Henry’s Fork Drainage)
8.11
Hole-in-Rock
9150
Northern Slope of Uinta Mountains(Henry’s Fork Drainage)
7.37
Hewinta G.S.
9500
Northern Slope of Uinta Mountains (Black’s Fork Drainage)
11.32
Steel Creek
10100
Northern Slope of Uinta Mountains(Black’s Fork Drainage)
15.91
Kelly R.S.
8180
Eastern Slope of Wyoming Range(Ham’s Fork Drainage)
16.14
Hams Fork
7840
Eastern Slope of Wyoming Range(Ham’s Fork Drainage)
11.22
Indian Creek
9425
Eastern Slope of Wyoming Range(Ham’s Fork Drainage)
25.05
Snider Basin
8250
Eastern Slope of Wyoming Range(La Barge Creek Drainage)
13.20
Triple Peaks
8500
Eastern Slope of Wyoming Range(South Cottonwood Creek Drainage)
23.86
Blind Bull Summit
8650
Eastern Slope of Wyoming Range(Horse Creek Drainage)
24.95
East Rim Divide
7930
Western Slope of Wind River Range (Headwaters of Green River)
11.88
Loomis Park
8240
Western Slope of Wind River Range (Headwaters of Green River)
16.09
Kendall R.S.
7740
Western Slope of Wind River Range (Headwaters of Green River)
13.99
New Fork Lake
8340
Western Slope of Wind River Range (New Fork River Drainage)
11.09
Elkhart Park G.S.
9400
Western Slope of Wind River Range (New Fork River Drainage)
12.66
Big Sandy Opening
9100
Western Slope of Wind River Range (Big Sandy River Drainage)
13.35
SWE (in.)
6
Area-Elevation analysis was performed to see what percentage of the total basin area
could be related to specific snotel stations. The basin area related to elevation is plotted in
Figure 2. In terms of elevation, the snotel stations fall between the 14200 and 18000 square
miles area. This indicates that the snotel system measures the snow pack for a 3800 square
mile region, which is only 20% of the total basin area. Areas above and below this region are
not accounted for. In general, snow does not accumulate to a high degree at low elevations in
comparison to higher elevations. In wet years, however, the lower elevation snow that falls into
the Upper Green River Basin is not accurately predicted by the snotel system. This probably
accounts for errors in the forecast during wet years. The highest 1000 square mile area of the
basin, which is mostly located in the Wind River Range, is considered wilderness area and
snotel stations are presently prohibited there. This area is a substantial contributor in terms of
snow accumulation and since data is not available, contributes a high degree of variability to
the errors in the forecast.
Figure 2(Snotel Elevations related to Area/Elevation Curve)
Flaming Gorge Basin Area/Elevation Curve
Snotel Location Related to Basin Area/Elevation
18000
Snotel Elevations
Basin Area (square miles)
16000
14000
12000
10000
8000
6000
4000
2000
0
5000
6000
7000
8000
9000
10000
Elevation (feet above sea level)
11000
12000
13000
7
The historical records for these stations vary as the stations were installed throughout
the 1970s and 1980s. Between 1985 and 1986, 10 of the 16 stations were installed which
provided a better description of the snow conditions in the basin. It was assumed that the
CBRFC used the April 1st SWE for the stations in the Upper Green River Basin as one of the
input parameters to generate the April forecast each year. It was further assumed that the April
1st SWE for each station, best described the maximum snow pack condition for the given year.
In most years, the peak SWE for the stations fall on or about April 1st with a few exceptions.
Thought was given to using the peak SWE data sets instead of the April 1st data sets.
However, it was decided April 1st SWE data should be used because it is the most comparable
to the CBRFC April forecast. The data sets for April 1st SWE at each station for each
available year were compiled and are provided in Tables A1 through A16 (Appendix A). All
of the snotel data analyzed in this report was provided by the Natural Resources Conservation
Service (NRCS) and is assumed to be correct and accurate.
To generate the regression equation, all 16 datasets were combined by various methods
of weighting to generate a single data set. The first method of combining the data was the
arithmetic mean method. This method gives each station an equal weight when all the data is
aggregated together into a single descriptive value. High elevation stations have the same
influence as low elevation stations which is not a reasonable assumption. As previously stated,
snow generally accumulates more at higher elevations and thus should be weighted more
heavily. However this method was tried as a base condition that could be compared to other
more sophisticated methods. The second method used, was based on elevation differences
between each stations. The stations were divided into low, mid and high elevation data sets.
For the first iteration of this technique, the arithmetic mean of the SWE for all stations below
8250 feet was identified as the low elevation snow value for the basin. The stations between
8250 and 9000 feet were aggregated into the mid-elevation snow value and all stations above
9000 feet were aggregated to the high elevation snow value for the basin. The percentage area
that corresponds to the interval was obtained from the area-elevation data and was used as a
weighing coefficient. Three iterations were performed by this method using different cutoff
elevations to determine the best regression combination. Table 2 describes the three iterations
that were tried. All of the data generated from this analysis can be found in Tables A17
through A26 in Appendix.
8
Table 2 (Area-Elevation Weighting Method Iteration Data)
Iteration 1
Iteration 2
Iteration 3
Low Elevation
High Elevation
Percentage
Resulting
Cutoff
Cutoff
Area
R^2
Multiplier
Statistic
Low
---
8250
.8342
Mid
8250
9000
.0708
High
9000
---
.0949
Low
---
8500
.8564
Mid
8500
9250
.0655
High
9250
---
.0780
Low
---
8000
.7987
Mid
8000
8750
.0799
High
8750
---
.1214
66.5%
61.6%
59.9%
These three data sets were also analyzed using multiple regression analysis, however,
the statistical methods used to generate the coefficients does not account for the sign of the
coefficient and negative coefficients were outcomes for the algorithm.
In the case of using
snowmelt to predict runoff, it is assumed that negative coefficients are physically not possible.
In other words, it is not physically possible for lower overall SWE values to generate higher
inflow levels than higher overall SWE values. Negative coefficients were not acceptable for
this analysis and thus this analysis was aborted.
.
The final method for aggregating the data sets together was done using an equation
developed by the NRCS and is used for generating a basin wide index, which is found on the
Snow-Precipitation Update Report. On this report, all snotel stations for a given basin are
listed with the current SWE and precipitation. A basin wide aggregation of the SWE percent
of average is calculated based on the individual data values in the report. The aggregated value
is obtained through an equation that weighs each station according to the average SWE for the
given station. In other words, stations that average higher SWE on a given day are weighted
appropriately higher than stations that have lower average SWE on the same day. In the NRCS
report the following equation is used to generate a basin wide percent of average SWE.
9

SWE i 


SWE


i

SWE
i 1 

i 

 % Averagei
SWE

i
n
The value generated from the above equation is an index that describes the basin snow pack
with a single value in terms of a percentage of average condition. From the individual station
data sets collected, a single data set was developed for this analysis that described the
percentage of average SWE over the entire basin on April 1st. Aggregated values were
generated for the years from 1986 to 1998. Prior to 1986, several of the snotel stations were
not yet operational thus data was not available. In 1999, several of these stations did not report
SWE data. For these reasons, those years were excluded from the analysis.
The historical April forecasts for the unregulated inflow in Flaming Gorge Reservoir
span the history of the reservoir. The forecast data set used for this report covers the period
from 1965 to 1999. Actual measured unregulated inflow volumes for this same period were
obtained from the Hydrologic Database (HDB) at the Bureau of Reclamation (BOR). Analysis
between the April forecast data set and the actual unregulated inflow data set was performed to
statistically determine if the forecasts have improved through time. It was assumed that the
most significant change that has occurred from 1965 to 1998 was the installation of the snotel
system. It was also assumed that other improvements, such as improved new modeling
techniques and computational facilities, have not improved forecasting abilities significantly.
Results
The results of aggregating the individual station data sets using the arithmetic mean
method gave an R^2 statistic of 49%. Figure 3 describes the correlation between the predicted
and actual A-J unregulated inflow from 1986 through 1998. The elevation weighting method
obtained a maximum R^2 statistic of 66.5% using 8250 and 9000 feet as the cutoff points for
the elevation intervals. Figure 4 shows the correlations obtained for these data sets.
10
Figure 3(Actual Unregulated Inflow versus Arithmetic Mean Predicted Inflow)
Flaming Gorge Unregulated Inflow
Correlation between Arithmetic Mean Predicted Inflow and Actual
Actual Unregulated Inflow (AF)
2500000
R2 = 0.4907
2000000
1500000
1000000
500000
0
700000
800000
900000
1000000
1100000
1200000
1300000
1400000
1500000
Predicted April-July Unregulated Inflow (AF)
Figure 4(Actual Unregulated Inflow versus ElevationPredicted Inflow)
Flaming Gorge Unregulated Inflow
Correlation Between Elevational Weighting Method and Actual A-J Unregulated
Inflow
Actual Unregulated Inflow (AF)
2400000
2000000
R2 = 0.6656
1600000
1200000
800000
400000
0
Predicted April-July Inflow Volume (AF)
1600000
11
The NRCS equation that was used to aggregate the individual station data sets into a
single snow description index for the Upper Green River Basin gave an R^2 of 65.5% when
correlated with the actual A-J unregulated inflow. Figure 5 describes the relationship between
the actual and predicted unregulated A-J inflow into Flaming Gorge. Of the three analysis
methods that were used to correlate the SWE data sets to the actual A-J unregulated inflow, the
elevation weighting method proved to give the highest correlation.
Figure 5(Actual Unregulated Inflow verses NRCS Predicted Inflow)
Flaming Gorge Unregulated Inflow
Correlation Between NRCS Equation Result and Actual April-July Unregulated
Inflow
Actual Unregulated Inflow (AF)
2500000
R2 = 0.6553
2000000
1500000
1000000
500000
0
600000 700000 800000 900000 100000 110000 120000 130000 140000 150000 160000 170000
0
0
0
0
0
0
0
0
Predicted April-July Inflow Volume (AF)
Based on the best elevation weighting method iteration, a regression equation was obtained for
forecasting A-J unregulated inflow into Flaming Gorge using April 1st SWE data from snotel
stations in the Upper Green River Basin. Table 3 describes the steps required to generate a
predicted A-J unregulated inflow based on April 1st snotel data.
12
Table 3(Steps Required to Generate Forecasted A-J Unregulated Inflow)
Step
Action
1
Find the arithmetic mean for the April 1st SWE for the following stations = Low
a.
b.
c.
d.
e.
2
Find the arithmetic mean for the April 1st SWE for the following stations = Mid
a.
b.
c.
d.
3
Blind Bull Summit
Triple Peak
New Fork Lake
Snider Basin
Find the arithmetic mean for the April 1st SWE for the following stations = High
a.
b.
c.
d.
e.
f.
g.
h.
4
Loomis Park
Kelley R.S.
East Rim Divide
Hams Fork
Kendall R.S.
Steel Creek Park
Hewinta
Indian Creek
Elkhart Park G.S.
Hickerson Park
Hole-in-Rock
Big Sandy Opening
Spring Creek Divide
((Low)*0.8342+(Mid)*0.0708+(High)*0.0949)*76036 = A-J Predicted Unregulated Inflow
Discussion
The historical performance of the CBRFC in terms of how well the April forecast has
predicted the actual unregulated inflow into Flaming Gorge was also analyzed. April forecasts
were compared to actual unregulated inflows for the period from 1965 to 1999. Figure 2
shows graphically the relationship between these data sets.
13
Figure 6(Actual and Forecast Unregulated Inflows into Flaming Gorge)
Flaming Gorge Reservoir
April-July Unregulated Inflow Related to April 1 Forecast
Unregulated Inflow Volume (KAF)
2500
April-July Unregulated Inflow
April 1 Forecast
2000
1500
1000
500
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
0
Year
The difference between the April forecast and the actual is the forecast error for the
given year. The yearly errors are represented in Figure 7.
Figure 7(Historical Forecast Errors)
Flaming Gorge Reservoir
April-July Unregulated Inflow Errors(KAF)
800
600
Inflow Error (KAF)
400
200
0
-200
-400
-600
Apri 1 Forecast Error
-800
-1000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
1982
1981
1980
1979
1978
1977
1976
1975
1974
1973
1972
1971
1970
1969
1968
1967
1966
1965
-1200
Year
Figure 7 appears to indicate that earlier forecasts were more accurate then later
forecasts so statistical analysis was performed to determine if this was true. It was originally
assumed that the snotel system would improve forecasting performance, however, Figure 7
14
seems to contradict this assumption. Since 10 of the stations were implemented in 1986, it was
decided that this year was a good break point to separate the snotel data set from the snow
course data set. Correlations between the forecast and actual data sets were performed on the
period prior to 1986 as well as after 1986 to establish if there were any difference in the
forecast performance. Figure 8 shows the correlation between forecast and actual data prior to
1986, while Figure 9 shows the same correlation for the data after 1986. From the R^2
correlation of the two data sets, it appears that the snotel system has, in fact, improved the
forecasting capabilities of the CBRFC.
Figure 8(Correlation Prior to 1986)
Flaming Gorge Reservoir
April-July Unregulated Inflow Related to April 1 Forecast (1965-1985)
Unregulated Inflow Volume (KAF)
2500
2250
April 1 Forecast Related to A-J Unregulated Inflow
2000
1750
R2 = 0.4381
1500
1250
1000
750
500
250
0
0
250
500
750
1000
1250
1500
1750
Actual April-July Unregulated Inflow (KAF)
2000
2250
2500
15
Figure 9(Correlation After 1986)
Flaming Gorge Reservoir
April-July Unregulated Inflow Related to April 1 Forecast(1986-1999)
Unregulated Inflow Volume (KAF)
2500
April 1 Forecast Related to A-J Unregulated Inflow
2250
2000
R2 = 0.7051
1750
1500
1250
1000
750
500
250
0
0
250
500
750
1000
1250
1500
1750
2000
2250
2500
Actual April-July Unregulated Inflow (KAF)
fdfd
Conclusion
The purpose of this project was to evaluate the available data to determine the answer
to three main questions. The first question that was asked was ‘Does the existence of the
snotel data acquisition system enhance the forecasting capabilities of the CBRFC for Flaming
Gorge A-J unregulated inflows?’. The accuracy of the forecasts has definitely improved since
1986 when the majority of the snotel sites were installed. The R^2 statistic for the correlation
of actual and forecast data increased from 49% to 71%, which indicates that, statistically, the
forecasts are much more accurate in recent years.
The next question that was presented was, ‘Of all of the parameters that are used in the
forecast process (i.e. snow pack, soil moisture, antecedent precipitation), how much of the
predictive capability is attached to the snow pack data in comparison to the other parameters?’
To answer this question, comparison between the R^2 statistics generated from the obtained
regression equation correlation (Figure 4) and the R^2 statistic from the forecast-actual
correlation (Figure 9). The difference between these R^2 statistics is attributable to the
parameters other than snotel SWE that are present in the forecast but not in the analysis done
16
for this project. While SWE alone gave an R^2 of 66.5% and the forecast gave an R^2 of
70.5%, it can be calculated that 94.5% of the predictive capabilities are attributable to the SWE
parameter.
The final question that was originally asked was, ‘If snow data was the only data
available for developing a A-J unregulated inflow forecast, what is the best regression equation
that could be developed for Flaming Gorge?’ The analysis performed using elevation as a
criteria for aggregating the individual data together gave an R^2 statistic of 66.5%. It seemed
reasonable that this value approached the maximum possible value as it was close to the R^2
value obtained from the forecast regression. Although small improvements might be made, it
is assumed that the equation generated from this analysis, is the best equation that could be
developed from the data used.
Water Year 2000 is well into the runoff cycle and April 1st has passed. The CBRFC
April forecast this year is calling for 1000 KAF of unregulated inflow into Flaming Gorge
Reservoir.
From the regression equation developed for this project, the predicted A-J
unregulated inflow into Flaming Gorge is 1090 KAF. By July 31st the actual value will be
available to see which of these forecast methods is more accurate.
17
References
Bestgen, K.R., Crist, L.W., Hayse, J.W., LaGory, K.E., Lyons, J.K., Muth, R.T., Ryan,
T.P.,Valdez, R.A., Flow and Temperature Recommendations for Endangered Fishes in
the Green River Downstream of Flaming Gorge Dam., Bureau of Reclamation, 2000.
Natural Resources Conservation Services, Snow-Precipitation Update.,
URL: ftp.wcc.nrcs.usda.gov/data/snow/update/.
The United States Department of the Interior, Water and Power Resources Service, Project
Data., United States Government Printing Office, Denver, 1981.
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