Evaluation of Probabilistic Streamflow Forecasts Based on EPS for a Mountainous Basin in Turkey


Ertas C., Akkol B., Coskun C., Uysal G., Sorman A. A., Sensoy A.

12th International Conference on Hydroinformatics (HIC) - Smart Water for the Future, Güney Kore, 21 - 26 Ağustos 2016, cilt.154, ss.490-497 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 154
  • Doi Numarası: 10.1016/j.proeng.2016.07.543
  • Basıldığı Ülke: Güney Kore
  • Sayfa Sayıları: ss.490-497
  • Anahtar Kelimeler: Ensemble Prediction System, HBV, Probabilistic Streamflow Forecasting, Karasu Basin, RUNOFF, MODEL, PREDICTION
  • Anadolu Üniversitesi Adresli: Evet

Özet

When designing water structures or managing a watershed it is a challenging task to determine the response of a basin to storm and/or snowmelt. In this study, the Upper Euphrates Basin (10,275 km(2) area and elevation range of 1125-3500 m) located at the headwater of Euphrates River, one of Turkey's most important rivers, is selected as the application area. In this region, snowmelt runoff constitutes approximately 2/3 in volume of the total yearly runoff, therefore, runoff modeling and forecasting during spring and early summer is important in terms of energy and water resources management. The aim of the study is to make a forward-oriented, medium-range flow forecasting using Ensemble Prediction System (EPS) which is a pioneer study for Turkey. Conceptual hydrological model HBV, which has a common usage in the literature, is chosen to predict streamflows. According to the results, Nash-Sutcliffe model efficiencies are 0.85 for calibration (2001-2008) and 0.71 for validation (2009-2014) respectively. After calibrating/validating the hydrologic model, EPS data including 51 different combinations produced by ECMWF is used as probability based weather forecasts. Melting period during March-June of 2011 is chosen as the forecast period. The probabilistic skill of EPS based hydrological model results are analyzed to verify the ensemble forecasts. (C) 2016 The Authors. Published by Elsevier Ltd.