Real‐time forecasting with a conceptual hydrologic model: 2. Applications and results

TLDR

Adaptive filtering techniques identify model and input error statistics, enabling practical uncertainty estimation that further improves the model’s performance. The conceptual hydrologic model, enhanced by adaptive filtering and feedback, delivers encouraging real‑time river discharge forecasts, outperforms a simple linear adaptive black‑box model—especially for lead times comparable to the catchment response—and highlights the importance of realistic, nonstationary uncertainty modeling.

Abstract

The results from an application of a conceptual hydrologic model, combined with filtering and statistical estimation methods, to real‐time forecasting of river discharges are very encouraging. The use of feedback significantly improves the overall forecasting capability of the model even when the model and input error statistics are not perfectly known. Identification of these statistics through adaptive filtering techniques is practical and further improves the performance of the model. Comparison with a simple linear adaptive ‘black box’ model is very favorable for the conceptual hydrologic model, especially for forecast lead times comparably to the response time of the catchment. The results emphasize the importance of using a realistic model of uncertainty accounting for the nonstationarity in the rainfall‐runoff process.