Modelling And Forecasting Snowmelt Runoff Process Using The Hbv Model In The Eastern Part Of Turkey

摘要

Snowmelt runoff in the mountainous eastern part of Turkey is of great importance as it constitutes 60-70% in volume of the total yearly runoff during spring and early summer months. Therefore, determining the amount and timing of snowmelt runoff especially in the Euphrates basin, where large dams are located, is an important task in order to use the water resources of the country in an optimal manner.rnThe HBV model, being one of the well-known conceptual hydrological models used more than 45 countries over the world, is applied for the first time in Turkey to a small basin of 242 km~2 on the headwaters of Euphrates river for 2002-2004 water years. The input data are provided from the automatic snow-meteorological stations installed at various locations and altitudes in upper Euphrates basin operating in real-time. Since ground-based observations can only represent a small part of the region of interest, spatially and temporally distributed snow cover data are acquired through the use of Moderate Resolution Imaging Spectroradiometer (MODIS) optical satellite. In the first part of the study, an automatic model parameter estimation method, Shuffled Complex Evolution, University of Arizona (SCE-UA), is utilized to calibrate the HBV model parameters with a multi-variable criteria using runoff as well as snow-covered area (SCA) to ensure the internal validity of the model. Results show that calibrations against SCA in addition to discharge simulate discharge nearly as well as calibrations against discharge only, but further suggest that longer time periods and more study catchments should be included to achieve more comprehensible conclusions. In the second part of the study, the calibrated HBV model is applied to forecast runoff with a 1-day lead time using gridded input data from Mesoscale Model 5 (MM5) for the 2004 snowmelt period. Promising results indicate the possible operational use of runoff forecasting driven by numerical weather prediction data for flood mitigation, reservoir operation and dam safety.