N 76-16 5 74APPLICATIONS OF SATELLITE SNOW COVER IN COMPUTERIZED SHORT-TERM STREAMFLOW FORECASTING V

C. F. Leaf, Fort Collins. Colorado

ABSTRACT

A procedure is described whereby the correlationbetween: (a) satellite derived snow-cover depletionand (b) residual snowpack water equivalent, can beused to update computerized residual flow forecastsfor the Conejos River in southern Colorado.

INTRODUCTION

In the Rocky Mountain West seasonal or total flow fore-casts are generally made utilizing regressions between snowaccumulation and runoff and annual surveys of peak snowpackwater equivalent. Extensions of these early-spring forecaststo a short-term basis using statistical methods is extremelydifficult since precipitation and meteorological conditionsduring the ensuing melt season vary widely from year to year.

One index of runoff, which has been found useful for im-proving residual flow forecasts is areal snow cover. In theUnited States, aerial surveillance of the snow cover wasproposed more than 30 years ago. More recently the areal extentof snow has become an important parameter in the growing em-phasis on applications of remote sensing in the water resourcefield (Rango 1975)• Advanced space technology and several newsatellite snow mapping procedures (Barnes and Bowley 197̂ > Fosterand Rango 1975) promise to make satellite snow cover observationsavailable on a dependable and routine basis. These satellitesnow cover estimates used in combination with conventional snowsurvey data as inputs to computerized simulation models shouldmake the problem of short-term residual volume forecasting lessdifficult in the very near future.

This paper shows how satellite imagery can provide key in-formation for making residual volume streamflow forecasts in theRio Grande basin using a computerized simulation model recentlydeveloped by the U. S. Forest Service. Mapped snow cover esti-mates and model output are summarized for the Conejos River, a282-square-mile tributary of the Rio Grande in southern Colorado.

I/This study has been funded by the USDA, Soil ConservationService, Snow Survey Unit, Denver, Colorado.

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https://ntrs.nasa.gov/search.jsp?R=19760009486 2018-06-12T23:05:16+00:00Z

The demonstration project is one of four currently beingsponsored by NASA to determine the effectiveness of satellite-derived snow-cover estimates in operational streamflow fore-casting (Rango 1975).

JUSTIFICATION

THE RIO GRANDE DRAINAGE BASIN

Waters of the Rio Grande are administered under the RioGrande Compact between three States and by an internationalagreement with Mexico. Accordingly, it is imperative thataccurate short-term forecasting procedures be developed so thatwater is dispatched fairly and efficiently throughout the Basin.In Colorado, the Rio Grande provides irrigation water to theSan Luis Valley. To provide maximum delivery of water to theseusers and still meet the Compact obligations, accurate fore-casts are required on a continuing 10-day basis starting April15 of each year (Qazi and Danielson 1973).

Cone.ios River

The area selected for this study is drained by the ConejosRiver, a key headwater tributary of the Rio Grande River. Figure1 is a map of this basin, and Table 1 summarizes pertinent geo-graphic characteristics of subunits selected for the simulationanalysis discussed subsequently in this report. Annual wateryields, corrected for Platoro Reservoir, which regulates flowsfrom approximately ^ square miles of the drainage basin, aresummarized in Table 2. These data were obtained from surfacewater records published by the U.S. Geological Survey, As seen inTable 2, runoff during the past 17 years has varied from a low of7.9 inches in 1959 to a high in 1965 of 22.1 inches.

METHODS

THE SUBALPINE WATER BALANCE MODEL

In order to make the best use of satellite imagery and toprovide the most flexibility for updating forecasts, it wasdecided to use the "Subalpine Water Balance Model" (Leaf and Brink1973a, 1973b) to simulate flows at 10-day intervals on theConejos River. This model simulates winter snow accumulation,the shortwave and longwave radiation balance, snowpack condition,snowmelt and subsequent runoff on as many as 25 subunits. Eachsubunit is described by relatively uniform slope, aspect, andforest cover. Results from each subunit are compiled into a"composite overview" of an entire drainage basin. Figure 2 isa generalized flow chart of the system.

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177

Table 1- Geographic Description of Conejos River Drainage Basin

Subunit

12345678910

Area I/(ni.2) (fan2)

2513373024401695232

>4

• 5• 5.2.0.0.1• 3.7.7

6535977862103412413684

.8

.0

.1

.2

.2

.6

.7

.1• 5.7

Average Elev.(ft.) (m)

111110101111

,000,500,500,500,000,000

10,5001099

,300,400,500

3,3,3,3,3,3,3,3,2,2,

355507202202355355202141867897

AverageAspect

SENNVfESWESENNEENESWNESW

Avg. Slope(percent)

34342833252327351515

I/ Total: Forest and Open

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MATBAL

1

SNOWPAC

SUBROUTINE

SNONVAP

RECH:REQ. SATISFIE

BY MELT.

RAIN

Figure 2 -Flow chart of "Subalpine Water Balance Model"(Leaf and

179

Calibration of Model to ConeIPS River

Daily temperature extremes in each of the response unitswere estimated by extrapolating observed temperatures at thebase station Wolf Creek Pass IE. Because reliable long-termradiation data are not available in the Conejos drainage, short-wave radiation input to the model was generated from potentialsolar beam radiation adjusted for temperature and the slope/aspect characteristics of each subunit. In order to compute anindex of the radiation received, potential radiation was re-duced by means of a thermal factor. This factor was determinedby degree-day relationships which vary according to season ofthe year.

Peak snowpack accumulation on the Conejos was estimated byextrapolating snow-course data published by the Soil Conser-vation Service. To ensure the proper snow accumulation in eachresponse unit, base station precipitation (at Wolf Creek Pass IE)was adjusted until the specified peak-water equivalent on eachsubunit was reached.

The Conejos River Basin was divided into 10 subunits as seenin Figure 1. Further division of forest and open areas resultedin 20 response units used for the simulation analysis.

RESULTS

RESIDUAL VOLUME FORECASTS (1958-1972)

Figures 3 and 4 show the comparison of simulated vs. actualresidual runoff as of the first day of April, May, June, andJuly for 8 years. The close agreement between observed andsimulated flows indicates that the model is a useful tool forcontinuous forecasting of short-term flows in the Conejos Riverfor the full range of runoff observed during the past 15 years.

TEN-DAY RESIDUAL VOLUME FORECASTS (1973 and 197 )̂

Figure 5 summarizes observed and simulated residual flowson a 10-day basis for 1973 and 1974. Also shown in Figure 4 aresatellite-derived snow cover estimates. Again, agreement is goodeven when residual volume forecasts are made at 10-day intervals.The superimposed snow cover estimates reveal a high correlationwith simulated runoff and suggest that satellite snow cover datacan provide key information for updating model input in a realforecasting situation. Just how this can be accomplished isdiscussed next.

INCORPORATION OF SATELLITE SNOW COVER OBSERVATIONS FOR FORECASTING

Table 3 compares mapped snow cover estimates on the ConejosRiver with residual water equivalent and streamflow for 1973 and197 .̂ In keeping with previous research, Table 3 and Figure 4suggest that depletion vs. runoff relationships vary depending

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Table 2 - Annual Runoff Corrected For Platoro Reservoir, ConejosRiver Basin.

Year Runoff Platoro Res. Adjusted RunoffChange in Storage (in.) (mm)

1958 -,/59*"̂6061*6263*64*65666768*69707172*73*74*

— incr16.69.9-

13-316.88.7

10.320.215.815.015.417.615.811.78.0

19.710.7

les-1.02-1.98

--0.27+0.60-0.70+0.31+1.89-1.24-1.08+0-55-0.46+0.25-0.43+0.01+2.14-1.20

15.67-9-

13.017.48.0

10.622.114.613-915-917.116.011-38.0

21.89-5

396.2200.7

-330.2442.0203.2269.2561.3370.8353-1403.9434.3406.4287.0203.2553-7241.3

I/ * Used in simulation analysis

Table 3 " Mapped Snow Cover vs. Residual Water Equivalent andStreamflow. (Conejos River, Rio Grande Drainage Basin)

Year

1973June 22

1974May 12May 30

Snow Cover(percent)

31

4218

Sim. ResidualWater Equiv.(in.) (mm)

3 76.2

4 101.61 25-4

Observed ResidualStreamflow From Snqwmelt(acre-ft) (m3)

1.379xl05 1.700x10®n

7.015x107; 8.649x10'5.333x10 6.576x10'

!/ Personal communication with Jack Washichek, SoilConservation Service, Denver, Colorado

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on the magnitude of the snowpack and precipitation through thesnowmelt season (Leaf and Haeffner 19?l). For example, in Table3, 31 percent snow cover on June 22 corresponds to a residualflow of 137,900 acre-feet in 1973—a big snow year, whereas onMay 12 in 197^—a l°w snow year—i*O percent snow cover representsa residual flow of only 70,150 acre-feet. However, if one looksat residual water equivalent, one finds that a direct relationshipexists between this variable and the areal extent of snow cover.

The stable correlation between snow-cover depletion andresidual water equivalent is independent of precipitation inputand can be utilized in combination with direct snowpack measure-ments through the melt season to revise model estimates of stream-flow. In most areas, satellite imagery would provide the prim-ary basis for updating streamflow forecasts so long as the drain-age basin is partially snow covered. Streamflow forecasts priorto the onset of snowmelt and during those times when the water-shed is completely covered with snow would rely on direct snow-pack measurements.

CONCLUSIONS

Satellite snow cover data used in combination with therecently developed Subalpine Water Balance Model can provide asound physical basis for making continuous short-term streamflowforecasts in the Upper Rio Grande Basin. Reconstitution studiesof a 15-year streamflow record indicate that the model is ade-quate for making residual- volume forecasts at time intervals asshort as 10 days. A comparison of model results with mapped snowcover estimates indicates that an apparently stable relationshipbetween satellite derived snow cover and residual water equiva-lent can be the primary basis for modifying short-term forecastsand ultimately delivery schedules to ensure the maximum amountof water for Colorado users while at the same time meetingCompact commitments downstream.

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REFERENCES

Barnes, J.C., and Bowley, C.J.1974. Handbook of techniques for satellite snow mapping. ERT

Document No.O^O?-A, 99 p. Prepared for NASA/Goddard SpaceFlight Center, Greenbelt, Maryland.

Foster, J.L., and A. Rango.1975-A method for improving the location of the snowline in

forested areas using satellite imagery. NASA Rep. X-910-75-41, 8 p. Goddard Space Flight Center, Greenbelt, Mary-land.

Leaf, Charles F., and Glen E. Brink.1973& Computer simulation of snowmelt within a Colorado sub-

alpine watershed. USDA For. Serv. Res. Pap. RM-99, 22 p.Rocky Mt. For. and Range Exp. Stn., Fort Collins, Colo.

Leaf, Charles F., and Glen E. Brink.1973b Hydrologic simulation model of Colorado subalpine forest.

USDA For. Serv. Res. Pap. RM-107, 23 p. Rocky Mt. For. andRange Exp. Stn. Fort Collins, Colo.

Leaf, Charles F. and Arden D. Haeffner_1971• A model for updating streamflow forecasts based on areal

snow cover and a precipitation index. West. Snow Conf.(Billings, Mont., Apr. 1967) Proc. 39:9~l6.

Qazi, Razig A. and Jeris A Danielson.1973- A predictive model for upper Rio-Grande index flows. Water

Resources Bulletin, Vol. 9, No. 2, p. 284-290. Amer. WaterResources Assoc. Urbana, 111.

Rango, Albert.1975. Applications of remote sensing to watershed management.

NASA Rep. X-913-75-86, (for presentation at the Symp. onWatershed Management, ASCE, Aug. 11-13, 1975, Logan Utah),Goddard Space Flight Center, Greenbelt, Maryland.

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