Hydrological Aspects of Alpine and High Mountain Areas (Proceedings of the Exeter Symposium, Juiy 1982). IAHS Publ. no. 138.
Operational snow mapping by satellites
TOM ANDERSEN
Norwegian Water Resources and Electricity Board, Division of Hydrology, P.O.Box 5091, Majorstua, Oslo 3, Norway
ABSTRACT A method of deriving snow information from weather and ocean satellites is described. The method is developed in order to improve operational forecasting of inflow to reservoirs for hydroelectric power production in the snow-melting period. Due to the topography typical for Norwegian mountain basins, the snowline concept is not useful. A better description of the snow distribution is achieved by using the satellite data to determine the area of snow cover within each picture element by using reference areas with known snow coverage.
The method is well suited for drainage basins larger than 200 km , located above the tree limit. Advanced
equipment is required for handling the data and extracting the information. Cloud cover is the limiting factor for the use of the method. If the method is used in an operational routine, the snow maps should be ready one or two days after the data are obtained. Such snow maps can improve the discharge forecasts and thus the power production, especially if at least limited data on the water equivalent of snow are available.
INTRODUCTION
In Norway the present production system for electricity is entirely based on hydro-electric power. A great number of existing power plants have reservoirs at about 1000 m a.m.s.l. accumulating inflow from high-mountain areas. In these areas the snow accumulation starts in September and October and the runoff from melting snow amounts to about 50% of the annual runoff. Consequently snow storage is a very important factor in the planning of the power production.
In most of the power-plant basins, snow surveys are undertaken once or twice during the accumulation period. The last survey is carried out at the end of the winter before the snow starts melting.
Normally the surveys are based on direct measurements, but in some important basins surveys based on the gamma-ray method are also carried out. When snowmelt has started the snow conditions change and the high-mountain basins are not accessible for regular snow surveys. Besides, the representativeness of point measurements is not of the best in the melting period.
However, information about the snow storage remaining is of great interest to water-power engineers for prediction of inflow into reservoirs and planning the optimal management of power plants.
Areas above the tree line are suitable for studies by satellites,
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and data from satellites have proved to yield valuable information on the areal extent of snow in periods when no information had earlier been available (Rango, 1975).
AVAILABLE SATELLITE DATA
In operational routines data on the total snow storage in a basin and on the distribution of the storage within the basin are most important. At present it is not possible to detect the water equivalent of snow from satellites. The areal extent of the snow cover can be detected and is used as a measure of the snow storage.
There are, however, strong limitations in the interpretation of snow-cover data (Martinec, 1980), and additional information on the water equivalent of the snow should be available.
The first Norwegian studies on the application of satellite data in hydrology were based on data from Landsat (0degaard, 1974} . The ground resolution proved to be better than needed for operational purposes, but the 18 day and 9 day repetition rate of Landsat did not fulfil the operational requirements. The next experiments were made with NOAA images, but the analogue presentation made it difficult to reach a satisfactory resolution.
The limitations experienced with both Landsat data and analogue NOAA data, made it necessary to develop routines for computer-based processing of digital data from TIROS-N and NOAA. The large pixel size for NOAA-images, about 1 km , proved to be no real problem because most water power basins have an area of several hundred square kilometres which is large enough for this resolution.
An important factor in operational use is the availability of the data. Data from the satellites TIROS-N and NOAA are received regularly at the Norwegian satellite receiving station in Troms(zS, northern Norway. If desirable a magnetic tape containing the digital data can be received in the processing centre the day after the satellite has passed. This arrangement makes it possible to do the data processing and make maps of snow cover only one or two davs after the data were recorded.
THE SNOW DISTRIBUTION
In the mountain areas of Norway the ground is entirely covered by snow every winter. There is no snowline between snow-covered areas and snow-free areas. In this period it is not possible to use present satellite data to estimate the amount of water in the snow cover. The method can only be used when the snow cover has started to melt and parts of the ground are uncovered.
Due to the typical topography in most Norwegian high-mountain basins, the snow distribution in spring will be characterized by zones of almost snow-free ground in the lowest part of the basin, then an increasing amount of snow patches and, finally, almost continuous snow cover in the highest parts. Thus it is impossible to talk about a definite "snowline", a term which is commonly used in areas of more Alpine mountain characteristics.
Because of the uneven snow distribution it is not satisfactory to
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classify each pixel(picture element)as either snow-free or snow- covered. More information on the snow cover is available in the data and some of this information is extracted by using the method presented here.
THE PRINCIPLES OF THE METHOD
To s o l v e t h e problem of t h e uneven snow d i s t r i b u t i o n , t h e snow- covered a r e a i s c a l c u l a t e d f o r each of the p i x e l s . Consider a s q u a r e on t h e ground 900 m x 900 m i n s i z e . The square i s covered by dark s o i l and some low v e g e t a t i o n which i s normal for t h e h i g h mountain b a s i n s . I f t h e a r e a i s g r a d u a l l y covered by 1/10, 2 / 1 0 , 3/10 e t c , of snow, the r e f l e c t a n c e from t h e a r e a w i l l a l s o i n c r e a s e up t o a v a l u e which, f i n a l l y , c o r r e s p o n d s t o complete snow c o v e r . The r e f l e c t a n c e of the a r e a w i l l t h e r e f o r e change from t h a t of s o i l t o t h a t of snow, depending on t h e s n o w / s o i l r a t i o .
In o r d e r t o e s t a b l i s h a r e l a t i o n s h i p between snow cover and r e f l e c t a n c e , r e f e r e n c e a r e a s w i t h known snow coverage have been s e l e c t e d . Two t y p e s of f i e l d are u s e d , a r e a s with f u l l snow cover and snow-free a r e a s (two t o four of each k i n d ) . Each f i e l d
c o n t a i n s 20 t o 40 p i x e l s . Based on t h e s a t e l l i t e b r i g h t n e s s v a l u e s of each p i x e l , t h e mean r e s p o n s e f o r t h e two t y p e s of f i e l d s a r e computed.
Each p i c t u r e element has a d i g i t a l v a l u e i n t h e range of 0-255 which i n d i c a t e s the amount of r e f l e c t e d l i g h t . T y p i c a l v a l u e of
the b r i g h t n e s s i n snow-free a r e a s i s 20 while the g r e y - s c a l e l e v e l HO could r e p r e s e n t a completely snow-covered r e f e r e n c e f i e l d . This means t h a t about 90 of the a v a i l a b l e 256 grey l e v e l s a r e used i n t h e snow-mapping r o u t i n e s .
As an approximation a l i n e a r r e l a t i o n s h i p i s assumed between t h e p i x e l b r i g h t n e s s v a l u e and t h e snow c o v e r a g e , see F i g . l . For each image under study the mean grey l e v e l s for t h e r e f e r e n c e f i e l d s a r e d e c i d e d and then the grey l e v e l s f o r 20%, 40%, 60% and 80% snow coverage are computed.
DATA PROCESSING
P r i o r t o t h e m e l t i n g season t h e b a s i n s i n c l u d e d i n the study a r e d i g i t i z e d and connected t o t h e UTM c o o r d i n a t e system. The d i g i t i z e d b a s i n o u t l i n e s a r e s t o r e d i n t h e computer memory. An example i s shown i n F i g . 2 . The p r o c e s s i n g i s based on an
i n t e r a c t i v e system c a l l e d ERMAN I I , and t h e programs are run on an IBM-3 70 computer w i t h a t w o - s c r e e n d i s p l a y system. Black and white o u t p u t i s a v a i l a b l e v i a a T e c t r o n i x h a r d - c o p y u n i t connected t o t h e computer.
When a scene w i t h s a t i s f a c t o r y cloud c o n d i t i o n s i s r e c e i v e d on a magnetic t a p e , a s u b s e t i s made of t h e a r e a of i n t e r e s t . Geometric
c o r r e c t i o n and r e g i s t r a t i o n t o UTM p r o j e c t i o n i s performed u s i n g w e 1 1 - d i s t r i b u t e d g r o u n d - c o n t r o l p o i n t s w i t h known c o o r d i n a t e s . The r e f e r e n c e a r e a s are i d e n t i f i e d on the image and the r e l a t i o n s h i p between snow coverage and b r i g h t n e s s values i s e s t a b l i s h e d .
The a c t u a l s a t e l l i t e d a t a a r e mixed w i t h the d i g i t i z e d b a s i n
0 50 100
