Stochastic and analytical approaches for sediment accumulation in river reservoirs

Akar T., Aksoy H.

HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES, cilt.65, sa.6, ss.984-994, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 65 Sayı: 6
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1080/02626667.2020.1728474
  • Dergi Adı: HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, IBZ Online, PASCAL, Agricultural & Environmental Science Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Compendex, Geobase, INSPEC, Pollution Abstracts, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.984-994
  • Anahtar Kelimeler: Juniata River, moving average model, river reservoir, storage volume, suspended sediment discharge, LOAD PREDICTION, WATERSHED PRIORITIZATION, CLIMATE-CHANGE, SOIL-EROSION, MODEL, HYDROLOGY, STATIONARITY, SOCIETY, WAVELET, YIELD
  • Akdeniz Üniversitesi Adresli: Evet

Özet

Sediment accumulation in a river reservoir is studied by stochastic time series models and analytical approach. The first-order moving average process is found the best for the suspended sediment discharge time series of the Juniata River at Newport, Pennsylvania, USA. Synthetic suspended sediment discharges are first generated with the chosen model after which analytical expressions are derived for the expected value and variance of sediment accumulation in the reservoir. The expected value and variance of the volume of sediment accumulation in the reservoir are calculated from a thousand synthetic time series each 38 years long and compared to the analytical approach. Stochastic and analytical approaches perfectly trace the observation in terms of the expected value and variability. Therefore, it is concluded that the expected value and variance of sediment accumulation in a reservoir could be estimated by analytical expressions without the cost of synthetic data generation mechanisms.