Time-varying parameters from the same period in previous years to improve runoff forecasting

Abstract

The time-varying parameter functional (TVPF) method has shown promise in improving runoff forecasting under climate change and human activities. However, it has three notable limitations: data availability, increased model complexity, and the inability to update with newly measured data. To address these challenges, this study proposes an improved runoff forecasting method that relies solely on time-varying parameters from the same period in previous years. The proposed method comprises four steps: (1) identification of time-varying parameters from historical observations; (2) estimation of the current-year parameters by averaging the time-varying parameters from the same period in previous years; (3) runoff forecasts using the estimated current-year parameters; (4) continuous identification and estimation of parameters with newly measured data for each year, enabling a rolling forecast of runoff. The proposed method is compared with both the constant parameter method and TVPF method, using the Xun River and Zuo River basins as case studies. The results reveal that (1) parameters from the same period in the previous three years yield the best runoff forecasts in the Xun River basin, while parameters from the same period in the previous five years produce the best runoff forecasts in the Zuo River basin; (2) the proposed method significantly improves the water balance index (WBI) compared to the TVPF method, providing either superior or comparable performance in structural risk minimization (SRM) evaluation. Overall, this study provides an approach to forecast runoff in a changing environment that reduces data requirements, simplifies model complexity, and improves WBI performance.