Estimation of seasonal runoff using remote sensing satellite data

Transcription

1 Remote Sensing and Geographic nformation Systems for Design and Operation of Water Resources Systems (Proceedings of Rabat Symposium S3, April 1997). AHS Pub!, no. 242, Estimation of seasonal runoff using remote sensing satellite data A. UNAL SORMAN METU, Civil Engineering Department, Water Resources Centre, Ankara, Turkey CEMAL SAYDAM TUB1TAKBLTEN, Remote Sensing Centre, Ankara, Turkey Abstract Snow hydrology has been a relatively less investigated part of the hydrologie cycle. n the last two decades there has been a growing interest and scientific research to study the role of melting snow for the estimation of runoff mainly contributing to the storage in large reservoirs and to make the procedure operational on a real time basis. The paper describes a project related to satellite snow cover mapping and snowmelt runoff computations, which is currently carried out in eastern Turkey. NTRODUCTON This study was initiated in 1996 through the NATO SfS fund support in order to study areal snow cover, its depth and water equivalent using a geographic information system (GS) database in conjunction with NOAA satellite imagery and ground measurements as well as hydrologie models for eastern Turkey. Such an approach has never been attempted in Turkey before because of a lack of financial support and unavailability of the satellite images. The daily availability, the easy access and the low costs make NOAA/AVHRR data advantageous especially in view of operational monitoring of snow covered areas in large scale basins. Convective methods of snow monitoring have been conducted with point measurement techniques at a microscale (<10 2 m). However, such point measurement techniques are rather difficult to implement at a meso or macroscale (> 10 4 m). Therefore, remote sensing (RS) and GS techniques provide a potentially efficient tool to measure the snow cover over large areas. RS and GS have the potential to provide input data for hydrologie models for runoff forecasting. Practical applications in recent years have shown that remotely sensed model variables are an important source of information for hydrologie models in order to make them operational. Seasonal snowmelt runoff estimates are extremely important in mountainous regions with semiarid climatic conditions, e.g. in eastern Turkey. Knowing the seasonal discharge volume in advance increases the flexibility in planning and operation of water resources systems as well as various water management decisions. For example, the optimum operation of Keban, Karakaya and Ataturk Dams which are located in the Euphrates River basin in Turkey (Fig. 1) depends on the estimated seasonal discharge volumes resulting from snowmelt during the spring months.

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3 Estimation of seasonal runoff using remote sensing satellite data 105 OBJECTVES OF THE RESEARCH n this study, remotely sensed snow cover data obtained by the NOAA (AVHRR) system are used in regression analyses as the main predictor variable to estimate the runoff from snowmelt. One of the major contributors of water to the Keban Dam is the Karasu River which joins the Euphrates River at Keban Dam near the city of Elazig. Snowmelt from mid March to June contributes 6570% of the total annual runoff. The main focus of this paper is the determination of the snow covered areas and snow depth variations during the melting season using NO A A/AVHRR images, point measurements and a GS database. CURRENT RS/GS FACLTES AT THE MDDLE EAST TECHNCAL UNVERSTY (METU) At METU in Ankara, processing and analysis of RS/GS requirements as well as achieving and image processing is carried out by the TUBTAKBLTEN Remote Sensing Centre. The RSCentre is equipped with a SUN/Spark10 workstation with UNX operating system running software packages such as ARC/NFO and Arcview. n addition a A0 size digitizing tablet, an ink jet plotter and A4 laser printers which are linked to the SUN workstation via a network are available. The RSCentre will also be equipped with a real time NOAA AVHRR/HRPT (High Resolution Picture Transmission) receiving station. The installation and operation of a METEOSAT/WEFAX receiver for transmitting AVHRR data from the nstitute of Marine Sciences (MS) will be implemented. All data will be transferred and stored on CDROMs for further analysis. The MS which is located on the Mediterranean coast of Turkey has been receiving AVHRR data since 1994 within the context of the NATO SfS TUBlacksea project. The present collaboration with this institute provides access to existing NOAA (AVHRR) images if they are needed. METHODOLOGY n 1996 the AVHRR/HRPT data were received on a daily basis at MS. The data have been archived on 8 mm exabyte tapes and processed by a RS analyst. The processing of visible and near infrared bands under clear sky conditions will be performed next in order to detect the areal extend of snow cover over the Karasu River basin. The snow covered area will be used to derive the potential snow water equivalent by collecting additional snow data from ground observations. The depth, the density and the snow water equivalent of the snow cover are collected at limited numbers of snow courses by governmental organizations during the winter months at the time of maximum snow accumulation. Once the snow line is determined on satellite images, the information will be supported by ground observations undertaken by governmental organizations. The existing facilities at the RSCentre and the realtime receiving station which have been established are going to be used in the near future to process, analyse and

4 106 A. Unal Sorman & Cemal Say dam interpret NOAA AVHRR data which will be coupled with a digital terrain model (DTM). Digitized map data for the Karasu River basin at a scale of 1: have been received from the General Directorate of Military Mapping Service. Records from meteorological and climate stations were obtained from the State Meteorological Organization (SMO), runoff and snow data records (depth, density and snow water equivalent) were provided by the General Directorate of Electrical Power Research Survey and Development (EE) directly responsible to governmental organizations in charge of collection of ground survey data concerning snow and meteorological/climatological data. GS AND THE DATABASE Ground observations of snow depth, density and water equivalent are the primary data source for the calibration of the regression model. The runoff measurements collected at several gauges provide the database for the actual hydrologie information during snowmelt conditions that can be tied to the point observations of snow depth. On the other hand, topography, vegetation and landuse information from other data sources assembled in a GS will aid estimating the snow depth variation at a basin scale. Topographic components of the database include elevation, slope, aspect, basin boundaries and river networks. The topographic components are extracted using a digital terrain model (DTM) of the Karasu River basin. The DTM, the channel network and hydrological attributes are presented in Figs 2(a)(d) for a subbasin controlled by the runoff station The DTMs for the Karasu River and its tributary were produced by digitizing 50 m contour intervals from the topographic map storing the data as ARC/NFO files. The files were transferred to the SUN/Sparc workstation for registration on the national grid system (NGS). After that it is possible to undertake quantitative analysis using statistical techniques as areaelevation (hypsometric) curve analysis, slope aspect analysis as well as slopeexposure (combined effect of aspect and slope) analysis. The results of slope and aspect analyses can be taken into account in the snow line assessment in order to separate snow linealtitude for different aspects. The combined effect influences the snow distribution because of the variation of solar radiation per unit area is considered. NOAA/AVHRR DATA t is well known that NOAA/AVHRR data are transmitted to the Earth twice a day in five different channels. Daily AVHRR snow cover maps permit the development of RS applications in hydrology and water resources because of their low costs and frequent availability. The ground measured snow depths at representative locations will be correlated with the relative snow reflectance of the AVHRR visible bands. Therefore snow cover maps for the entire study area can be produced by supervised classification (Baumgartner, 1990; Baumgartner & Rango, 1995). Under cloudfree weather conditions, the daily AVHRR data will be useful for snow cover estimations using the visible and near infrared spectral bands. Such

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6 108 A. Unal Sorman & Cental Say dam weather conditions are now being sought for the winter months of the hydrologie year 1996 to derive snow cover maps for early model calibration. However, the correlation of snow reflectance and snow depth is restricted to shallow snow packs. Therefore, passive microwave data from the Special Sensor Microwave mager (SSM/) can be useful for the determination of snow depth and for the assessment of the snow water equivalent of dry snow even under cloudy conditions (Bailey et al., 1993). The analysis of AVHRR data will be useful for studies at regional (basin) scales in combination with a GS database. Comparable snow maps derived from SSM/ data can provide additional information especially by partially cloudy and overcast conditions. The areas of NOAA images which are partially cloud covered by can be replaced with information on snow covered and snow free areas through GS interpolations or with snow cover products from the SSM/ data analysis. The usefulness of each data source as discussed above under different atmospheric conditions is summarized in Table 1 : Table 1 Data source and cloud coverage. Data source Cloud free Partial cloud cover Total cloud cover AVHRR x AVHRR + GS x x SSM/ x x x The percentage of snow covered area is needed as input for snowmelt computations with the Snowmelt Runoff Model (SRM) (Martinec et al., 1994). Based on AVHRR image classification, it will be possible to obtain such data after the establishment of the satellite receiving station at METU. Up to now only images from 1994 till present, received by the nstitute of Marine Sciences (MS) can be processed to derive snow cover maps. For the evaluation, the spectral bands 1, 3 and 4 will be used. The reason for this selection is that band 3 and band 4 are indicators of the snow/cloud discrimination while band 1 describes only the snowpack (Lucas & Harrison, 1989; Baumgartner, 1990). ANALYSS OF RESULTS As a first step the relationship between snow cover variability and elevation, slope and aspect were investigated using a DTM. t is known that there is strong relationship between snow cover and elevation. n mountainous terrain there is a linear trend between snow depth and elevation (US Army Corps of Engineers, 1956). However, elevation alone is not sufficient to account for snow depth variations because other basin parameters (slope and aspect, vegetation and landuse) also influence the deposition of snow. Snow depth variations due to slope and aspect variations are important not only during the snowfall event, but also influence the snow cover distribution after the event. Such an analysis is done using oneway and twoway variance tests. The snow water equivalent values collected from six snow measuring stations throughout the recording period are processed for this analysis. The results shown in Tables 2a and

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8 110 A. Unal Sorman & Cemal Saydam 26/4/96 LU Z3 > LATTUDE,LONGTUDE 30/4/ LATTUDE.LONGTUDE 1/5/ LATTUDE,LONG!TUDE 50 Km Fig. 3 NOAA/AVHRR images with reflectance of snow data along on a trajectory for 26 April 1996, 30 April 1996 and 1 May 1996.

9 Estimation of seasonal runoff using remote sensing satellite data 111 2b indicate that there is a very high correlation between the snow water equivalent and the elevation. Models for snowmelt runoff estimation A mathematical relationship between the brightness level of the snow coverage on three consecutive days in 1996 is derived. As a result the variations of reflectance of snow are estimated with respect to time and space. Figure 3 shows this along a southwestnortheast trajectory of the basin boundary. A regression model is set up to get a first estimation of snowmelt runoff caused by factors such as snow depth or snow water equivalent and other available hydrologie records (runoff, precipitation) as well as the mean air temperature observed during spring and autumn. The input data matrix includes five predictor variables to represent the snow water equivalent at five snow gauging stations and a criterion variable, which is the snowmelt runoff depth for the four month period from March to June. This statistical operation is repeated for each of the runoff gauging stations (EE2154, EE2151 and EE2119) for which runoff records were available. A stepwise regression analysis is performed in order to develop the prediction model no. 1. A summary of this analysis is given in Table 3 for the runoff station EE2154 with a drainage basin size of 2886 km 2. The results of a similar regression analysis are presented in the same table showing the correlation between the depth of snowmelt runoff and the other independent parameters such as the runoff depth at the upstream gauging station (DS2101) and of all other predictor variables representing the snow water Table 3 Stepwise regression summary for snowmelt runoff depth at RGS2154; criterion variable g 254, with a drainage area of A = 2886 km 2. Partial regression coefficient Variable ntered Station D Station type Elev. R R 2 AR 2 s, SA F bo bi bj bj b4 MODEL 1 x s x 2 X, X, Kll K13 K08 K ~ ~ MODEL 2 X» x, X! Quoi Kll K08 RGS X, K x 2 K : Snow gauging station RGS : Runoff gauging station R = Correlation coef. R 2 = Determination coef. AR 2 = Difference Sc = Standard error Se / S, = mprovementtatio F = Fratio bo Regression constant bi bî bj D4 = Partial regression coef.

10 112 A. Unal Sorman & Cemal Say dam equivalent. The selection of the parameters in model no. 2 depends on the magnitude of the partial F at each step. Other selection criteria for the best model were the correlation coefficient (R), the standard error of estimate (S e ), the rationality of the intercept (b 0 ) and the rationality of the slope coefficients (Z?,s). CONCLUSONS (a) Satellite based RS has great potential in applications to determine the areal snow cover for large mountainous regions in Turkey. (b) Quantitative monitoring of snow covered areas with NOAA/AVHRR data can be improved with new hardware and software systems with the capabilities of today's computer technology. (c) Geocoding of the satellite data to the National Coordinate System enables overlaying and other analysis within the GS. (d) Categorization of AVHRR data for snow depth needs further investigation and the incorporation of ground measurements and GS data which provide a successful basis for monitoring the snow depth not only for cloud free days but also for partially cloudy days. REFERENCES Bailey J. O, Barrett, E. C, Beaumont, M. J. &. Herschy, R. W. (1993) Remote Sensing of Snow by Satellite. NRA, Bristol, R and D Project no 207. Baumgartner, M. F. (1990) Snowmelt runoff simulations based on snow cover mapping using digital Landsat MSS and NOAAdata. Tech. Report HL16, US Dept of Agriculture, ARS, Hydrology Lab. Baumgartner, M. F. & Rango, A. (1995) A microcomputer based Alpine snow cover analysis system. Phologram. Engng and Remote Sensing, 61(12), Lucas, R. M. & Harrison, A. R. (1989) A satellite technique for operational snow monitoring in the UK. Final Report, RSU, University of Bristol, UK. Martinec, J., Rango, A. & Roberts, R. (1994) Snowmelt Runoff Model User's Manual, (ed. by M. F. Baumgartner). Geographica Benensia no. P29, Dept of Geography, Univ. of Bern, Switzerland. US Army Corps of Engineers (1956) Snow Hydrology. US Dept of Commerce, Washington DC, USA.