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.