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

Date

2025-08-01

Author

Özcan, Zeynep
Iseri, Y.
Ulloa, F.
Imbulana, N.
Snider, E.
Mure-Ravaud, M.
Anderson, M. L.
Kavvas, M. L.

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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.

URI

https://hdl.handle.net/11511/116516

Collections

Department of Environmental Engineering, Article