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Snow Cover Mapping for Runoff Simulations Based on Landsat-MSS Data in an Aipine Basin

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Snow Cover Mapping for Runoff Simulations Based on Landsat-MSS Data in an Aipine Basin Hydrologie Applications of Space Technology (Proceedings of the Cocoa Beach Workshop, Florida, August 1985). IAHS Publ. no. 160, 1986. Snow cover mapping for runoff simulations based on Landsat-MSS data in an aipine basin M. F, BAUMGARTNER & K, SEIDEL Institute for Communication Technology, Swiss Federal Institute of Technology, Zurich, Switzerland H, HAEFNER S K. I, ITTEN Remote Sensing Laboratories, Department of Geography, University of Zurich, Switzerland J. MARTINEC Federal Institute for Snow S Avalanche Research, Weissfluhjoch-Davos, Switzerland Abstract For the Martinec-Rango Snowmelt Runoff Model (SRM) a precise determination of the daily changes of the snow cover is the basic input variable. A method was developed for digital snow- cover classification with Landsat-MSS data including an extra­ polation for partly clouded scenes and areas with not available remote sensing data. The runoff simulations were tested in a large basin of the Swiss Alps (Rhine-Felsberg, 3249 km2, 571- 3614 m a.s.l.) regarding the heterogeneous topographic and cli­ matic conditions. Introduction and Objectives The assessment and forecast of the quantities of surface runoff is a key objective for water management. In mountain areas the runoff in spring and summer is primarily affected by the changes of the seasonal snow cover. Various tests have demonstrated that the Martinec-Rango Snow- melt Runoff Model (SRM; Martinec, 1980) is suited to forecast surface runoff under mountaineous conditions (Martinec, 1970 and 1975). The accuracy of the forecast depends especially on a measurement of the daily changes of the snow cover. An up-to- date, fast and exact recording of these changes is only possible by remote sensing techniques (Haefner, 1980; Itten, 1980). Therefore, it is the purpose of this paper to outline a me­ thod to map and determine the snow cover in a large basin by Landsat-MSS data. The satellite images have been processed digitally using an interactive image processing system (Besse, 1981; Egeli, 1983). Special attention has to be given to the difficult and var­ iable topographical and climatical conditions in the Alps. Ser­ ious problems were the often occuring clouds and not recorded data, which complicate the determination of the snow cover. Even less suited images have to be considered, in order to get enough remote sensing information to monitor the varying snow cover during a melting period. The areas with 'missing data' have to be estimated using an extrapolation technique, based on 191 192 M.F.Baumgartner et al. a digital terrain model. The different climatological conditions in the basin and the frequent changes of rain- and snowfall during the melting period complicate the application of the SRM and have to be respected for discharge simulation. A solution is presented to overcome these obstacles for a rather large catchment area in the Swiss Alps and results are presented. Study Site The test site 'Felsberg' is located in the eastern part of the Swiss Alps (Fig. 1) and includes the four Rhine-tributaries 'Vorderrhein', 'Hinterrhein', 'Landwasser' and 'Albula'. Its size is 3249 km , the basin relief 3043 m (lowest point: stream gauge 'Felsberg', 571 m a.s.l., highest point: 'Toedi', 3614 m a.s.l.). The ice covered area (glaciers) totals 3% at the end of the hydrological year (September 30th). FIG.l Location of the 'Felsberg' basin: area of Switzerland: 41,293 km2 area of 'Felsberg' basin (shaded area).- 3,249 km The basin can be divided into three different climatological regions. The western part ('Vorderrhein') has a significant higher amount of precipitation than the eastern part ('Landwas­ ser' and 'Albula'). The southern part ('Hinterrhein') is influ­ enced by the climate of the South Alps, i.e. very high precipi­ tations for a short time during summer and autumn, compared to the more evenly distributed precipitations in the northern part of the Alps. This differences can be explained by diffe­ rently oriented mountain chains and the influence of the wester­ ly winds. The water regime in spring and summer depends mainly on snowmelt, in summer and autumn on rainfall. In many valleys of the Alps the runoff is controlled by reser­ voirs for hydroelectric power generation. This raises a serious problem in the comparison of the measured with the calculated runoff on a daily basis. The recorded (actual) daily minima and maxima and weekly variations reflect the needs for electricity Snow cover mapping for runoff simulations 193 and does not show the natural runoff. Snow Cover Determination by Satellite Data For the determination of the changing snow cover during the snowmelt period Landsat-MSS digital recordings were used. Im­ portant procedures for the processing of the satellite images are : - Preprocessing, as a geometric transformation of the images to a basic coordinate system and resampling to a unique pixelsize - Determination of an adequate classification scheme and training samples for a supervised classification - Snow cover classification - Superimposition of all data into a multivariate data set - Extrapolation to estimate the snow cover for areas with not available data - Problem Spots of Snow Cover Determination Due to the increase of the solar irradiance in spring and summer clouds develop in the late morning (Rott', 1978). The Landsat-MSS sensors (Tab. 1) are recording the radiation reflected from the clouds and therefore the earth surface is invisible. In addition the spectral bands do not allow an automated separation between snow and clouds. In a false color representation a different- Tab. 1: Landsat-MSS characteristics Landsat 1-3 Landsat 4,5 Altitude 920 km 705 km Period 102 min 9 9 min Frame area 185*185 km2 185*185 km2 Resolution 56*79 m2 56*79 m2 Repetition rate 18 days 16 days Band 4 0.5-0.6 um 0.5-0.6 um Band 5 0.6-0.7 um 0.6-0.7 um Band 6 0.7-0.8 um 0.7-0.8 um Band 7 0.8-1.1 um 0.8-1.1 um iation becomes possible, based on the cloud forms and the cloud shadows. For every image a digital cloud mask was produced by visual inspections on a interactive image processing system. Together with missing frame segments (not available remote sen-• sing data) a special procedure was established: an extrapolation technique, based on elevation, aspect and slope for each pixel, allows an estimation of the snow coverage in the unknown parts (Seidel, 1983). This and the request for the snow coverage in different elevation zones lead to the use of a Digital Terrain Model (DTM) as a suitable auxiliary information source for the processing of satellite data. Because of the repetition rate of Landsat (Tab. 1) and the often by heavyly cloud coverage obscured scenes, a sufficient, 194 M.F.Baumgartner et al. number of images during a snowmelt season is very seldom re­ corded. Considering even less suitable scenes, for the snowmelt period 1982 sufficient data were available. The following dates with frame numbers 209/027 and 209/028 have been used: (1) 2 5-MAR82 1.4% cloudcover/18.8% not available (2) 18-MAY82 15.1% cloudcover/13.1% not available (3) 5-JUN82 20.9% cloudcover/ 2.1% not available (4) 11-JUL82 not available (5) 29-JUL82 10.9/0.20.9% cloudcover/ 3.4% not available It was necessary to compensate in (1) for missing lines by doub­ ling the neighboring lines and in (3) for misregistered lines and channels. The frame (209/027) of scenes (4) and (5) did only cover a part of the basin. They were digitally mosaiced with the southwards adjacent frames (209/028). The cloud coverage on the record of July 29th has been re­ duced by a pixelwise comparison with the record of July 11th, i.e. in each doubtful case the picture element in (5) has been assigned to the category of the corresponding element in (4). This procedure is not possible as soon as new snowfall has changed the situation in the mean time. Digital Terrain Model and Basin Perimeter The DTM is used for the extrapolation process mentioned above For the test site 'Felsberg' the DTM was interpolated from the (250*250) rn grid into a raster size of (100*100) m applying the Akima method (Akima, 1974), which is based on a bicubic in terpolation. In order to determine the contribution to the total snow coverage from 'obscured' and 'not available' areas we used a method published earlier (Seidel, 1983). We devided the conti­ nuum of elevation-aspect-slope values into 205 classes: - 5 elevation zones (560-1100, 1100-1600, 1600-2100, 2100-2600, 2600-3600 m a.s.l.) - 8 aspect classes (N, NE, E, SE, S, SW, W, NW, N) - 5 slope classes (<0-15, 16-22, 23-28, 29-36, 37-90c - 1 class for flat terrain The basin boundary has been digitized from the National Topo­ graphic Map 1:50'000. Preprocessing The Landsat-MSS digital products were received in UTM coordi­ nates. They were transformed to the coordinate system of the National Topographic Map (based on a conform, Special-UTM pro­ jection) to facilitate the comparison of the different data sets (satellite images, DTM, masks). With the standard reference point approach, the Landsat images and the map were registered by an affine backward transformation (Steiner, 1977). At the same time a nearest neighbor resampling from 56*79 m MSS reso­ lution to the common raster size of (100*100) m was carried out. Snow cover mapping for runoff simulations 195 - Classification The different Landsat-MSS scenes have been analysed with re­ spect to snow cover. During a supervised classification process each picture element was assigned to one of the following cate­ gories: 'snow free1, 'transition zone' and 'snow covered1.
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