Integrated Methodology for Seasonal Inflow Forecasting: A Case Study of the Upper Feather River Basin

Seasonal inflow forecasting is crucial for managing water resources and understanding climate impacts on river systems. Recent extreme events have exposed the limitations of traditional statistical water supply forecasting methods, underscoring the urgent need to shift toward frameworks that incorporate more physically based modeling approaches. This study presents an integrated seasonal inflow forecasting methodology applied to the Upper Feather River Basin (UFRB), a major contributor to the California State Water Project. The UFRB, upstream of Oroville Dam, encompasses multiple tributaries, including the North, Middle, South, and West Forks, providing water for urban, industrial, and agricultural needs. The basin is prone to significant runoff events, particularly during rain-on-snow conditions. It also faces challenges with flood control and maintaining dry season base flows. This study introduces an integrated seasonal inflow forecasting methodology, combining deterministic modeling with a statistical correction technique to improve forecast accuracy. The deterministic component includes a climate forecasting system using the WRF model to downscale global climate data, and a snow and inflow forecasting system using WEHY-HCM. The deterministic forecast errors are corrected via the exponential smoothing method. With the developed methodology, Oroville Dam seasonal inflows were forecasted in 2024, and the method worked successfully. Given the basin's importance in California’s water supply and the potential for extreme runoff events, providing accurate seasonal inflow forecasts is critical for optimizing water management. This methodology not only applies to the Upper Feather River Basin but also offers broader implications for water resource management across other basins.