WRF-Hydro Model Application in a Data-Scarce, Small and Topographically Steep Catchment in Samsun, Turkey


Arabian Journal for Science and Engineering, vol.45, no.5, pp.3781-3798, 2020 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 45 Issue: 5
  • Publication Date: 2020
  • Doi Number: 10.1007/s13369-019-04251-5
  • Journal Name: Arabian Journal for Science and Engineering
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, Metadex, Pollution Abstracts, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.3781-3798
  • Ankara Yıldırım Beyazıt University Affiliated: Yes


© 2019, King Fahd University of Petroleum & Minerals.Floods due to heavy rainfall are one of the most frequent and widespread natural hazards. Rainfall is one of the key variables in flood modeling. For topographically steep catchments, flood modeling requires accurate rainfall sources in both time and space. The objective of this study is to compare different rainfall sources in physics-based distributed hydrologic model, (Weather Research and Forecasting) WRF-Hydro, in a data-scarce, small and topographically steep catchment. For this purpose, the model was calibrated and validated for the three catastrophic flood events that occurred in the Terme basin of eastern Black Sea region in Samsun, Turkey. The rainfall datasets include weather radar data and the Hydro-Estimator satellite rainfall product as nowcasting products, and WRF model precipitation data as a forecasting product and gauge-based data. Our results indicated that the tested rainfall products have different limitations and potentials depending on the rainfall process, so the accuracy of the results is greatly affected by the accuracy of rainfall products. Among the flood hydrographs, WRF precipitation data, bias-adjusted radar data and gauge data gave best Nash–Sutcliffe efficiency (NSE) results with calibrated parameters in simulations belonging to floods observed on November 22, 2014, August 2, 2015, and May, 28, 2016, respectively.