Hybrid physical-statistical framework for seasonal streamflow forecasting in the Upper Feather River Basin, California

Ozcan Z., Iseri Y., Ulloa F., Imbulana N., Snider E., Mure-Ravaud M., ...Daha Fazla

SCIENTIFIC REPORTS, cilt.15, sa.1, 2025 (SCI-Expanded, Scopus) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 15 Sayı: 1
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1038/s41598-025-15932-7
  • Dergi Adı: SCIENTIFIC REPORTS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, Chemical Abstracts Core, MEDLINE, Veterinary Science Database, Directory of Open Access Journals
  • Orta Doğu Teknik Üniversitesi Adresli: Hayır

Özet

Seasonal streamflow forecasts are essential given climate-driven extremes that breach stationarity in traditional methods. The complex hydrology and competing demands necessitate improved forecasting in the Upper Feather River Basin (UFRB), a key California State Water Project source upstream of Oroville Dam. We introduce a hybrid framework combining dynamical downscaling via WRF and the WEHY-HCM snow-hydrology model with a lead-time-dependent exponential-smoothing filter that adaptively corrects bias and quantifies uncertainty. Applied to December-July ensemble forecasts for water year 2024 using hindcast error training (2018-2023), this approach reduced RMSE by 8.7-318.3 million m(3) across eight initialization months and eliminated systematic bias. The resulting 10-90% exceedance bands captured similar to 80% of observed flows, offering reliable confidence intervals. This hybrid method delivers accurate, low-bias streamflow forecasts for reservoir operations, flood mitigation, and irrigation planning in the UFRB and provides a transferable template for other basins facing hydroclimatic variability.