Estimation of flow duration and mass flow curves in ungauged tributary streams


Vaheddoost B., YILMAZ M. U., Safari M. J. S.

Journal of Cleaner Production, cilt.409, 2023 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 409
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1016/j.jclepro.2023.137246
  • Dergi Adı: Journal of Cleaner Production
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Business Source Elite, Business Source Premier, CAB Abstracts, Chimica, Communication Abstracts, Compendex, INSPEC, Metadex, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, Civil Engineering Abstracts
  • Anahtar Kelimeler: Advanced hydrologic model, Coruh river, Stochastic model, Ungauged tributary streams
  • Kırklareli Üniversitesi Adresli: Evet

Özet

The mastery in forecasting the streamflow rates is of great importance in the design, planning and resilience against droughts. Likewise, the application of flow duration and mass flow curves in the design of the reservoir capacity, energy generation, water allocation, etc. especially at the tributary reaches is a great challenge mostly due to the lack of information and data records. In this study, we have developed a methodology to obtain the flow duration curve (FDC) and mass flow curve (MFC) in tributary stream stations with the help of estimated streamflow rates. The procedure suggests using two alternative approaches in the selection of the reference station on the mainstream. The streamflow in the reference station is decomposed into direct runoff (DR) and base flow (BF) using one-parameter digital filter method. Together with the precipitation records in the tributary station, the DR and BF on the reference station are then used to estimate the FDC and MFC. The multivariate adaptive regression spline (MARS) and random forest (RF) methods are used to alternate each other, and the residual of the models are simulated using the autoregressive conditionally heteroscedastic (ARCH) approach to develop the hybrid MARS-ARCH and RF-ARCH models. A data set related to Coruh River Basin, in Turkey is used to confirm the methodology, while results with R2 ≥ 0.92, reasonable bias, and relative error in the estimation of the expected FDC and MFC rates indicated the robustness of the suggested methodology.