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., ...More

SCIENTIFIC REPORTS, vol.15, no.1, 2025 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 15 Issue: 1
  • Publication Date: 2025
  • Doi Number: 10.1038/s41598-025-15932-7
  • Journal Name: SCIENTIFIC REPORTS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, Chemical Abstracts Core, MEDLINE, Veterinary Science Database, Directory of Open Access Journals
  • Middle East Technical University Affiliated: No

Abstract

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.