Snow indicates the potential stored water volume that is an important source of water supply, which has been the most valuable and indispensable natural resource throughout the history of the world. Euphrates and Tigris, having the biggest dams of Turkey, are the two largest trans-boundary rivers that originate in Turkey and pass throughout the water deficit nations Syria, Iran, Iraq and Saudi Arabia bringing life as well as water all their way. Snowmelt runoff originating from the mountains of Eastern Turkey accounts for 60 to 70 % of total annual discharge observed in Euphrates and Tigris. For an optimum operation of the dams, maximizing energy production, mitigation of floods and satisfying water rights, hydrological models which can both simulate and forecast the river discharges of Euphrates and Tigris are needed. In this study a hydrological model, snowmelt runoff model (SRM), is used in conjunction with remote sensing and geographic information systems to forecast the river discharges in the headwaters of Euphrates River, Upper Euphrates Basin. NOAA and MODIS satellite images were used to derive the snow covered area (SCA) information required by SRM. Linear reduction methodologies based on accumulated air temperature, with constant or varying gradient, were developed to get the continuous daily SCA values from the discrete daily satellite images. Temperature and precipitation forecasts were gathered from two different numerical weather prediction models, namely European Center for Medium Range Weather Forecasts (ECMWF) and Mesoscale Model Version 5 (MM5) from Turkish State Meteorological Services. These data sets provided t+24 hour forecasts of both temperature and precipitation. Temperature, precipitation and SCA information are fed into SRM. Discharge forecasts obtained from the model outputs are compared with the observed values. The overall performance of the model was seen as promising. Possible reasons of the mismatches between the forecasted and observed values are searched. Experiences gained throughout the study are summarized and recommendations on further forecast studies are mentioned.

The purpose of this dissertation is to develop and evaluate techniques for improvement of seasonal streamflow forecasts in the Upper Rio Grande (URG) basin in the U.S. Southwest. Three techniques are investigated. The first technique is an investigation of the effects of the El Ni??o/Southern Oscillation (ENSO) on temperature, precipitation, snow water equivalent (SWE), and the resulting streamflow at a monthly time scale, using data from 1952 to 1999 (WY). It was seen that the effects of ENSO on temperature and precipitation were confined to certain months, predominantly at the beginning and end of the winter season, and that the effect of these modulations of temperature and precipitation by ENSO can be seen in the magnitude and time variation of SWE and streamflow. The second part is a comparison of the use for snowmelt-runoff modeling of the newly available snowcover product based on imagery from the satellite-borne Moderate Resolution Imaging Spectroradiometer (MODIS) with the long-time standard snowcover product from the National Hydrological Remote Sensing Center (NOHRSC). This comparison is made using the Snowmelt Runoff Model (SRM) in two watersheds located inside the URG basin. This comparison is important because the MODIS snowcover product could greatly improve the availability of snowcover information because of its high spatial (500m) and temporal (daily) resolutions and extensive (global) coverage. Based on the results of this comparison, the MODIS snowcover product gives comparable snowcover information compared to that from NOHRSC. The final part is an investigation of streamflow forecasting using mass-balance models. Two watersheds used in the comparison of MODIS and NOHRSC snowcover products were again used. The parameters of the mass-balance models are obtained in two different ways and streamflow forecasts are made on January 1st, February 1st, March 1st and April 1st. The first means of parameter estimation is to use the parameter values from 1990 to 2001 SRM streamflow simulations and the second means is by optimization. The results of this investigation show that mass-balance models show potential to improve the long-term streamflow forecasts in snowmelt-dominated watersheds if dependable precipitation forecasts can be provided.