Bayesian Theory Based Self-Adapting Real-Time Correction Model for Flood Forecasting

Real-time correction models provide the possibility to reduce uncertainties in flood prediction. However, most traditional techniques cannot accurately capture many sources of uncertainty and provide a quantitative evaluation. To account for a wide variety of uncertainties in flood forecasts and overcome the limitations of stationary samples in a changing climate, a Bayesian theory based Self-adapting, Real-time Correction Model (BSRCM) was proposed. BSRCM uses the Autoregressive Moving Average (ARMA (n, m)) model as the prior distribution for the flood hydrograph, and the autoregressive model or order p (AR(p)) as the likelihood function to describe the likelihood relationship between the predicted and observed discharges, on the basis the posterior distribution of real values of discharge at any step can be deduced under the framework of Bayesian theory. Combined with the Xin’anjiang hydrological model, it was applied for flood forecasting in the Misai basin in southern China. Results from this study indicate that: (1) BSRCM can achieve a good precision and perform better than AR(p) in the study region; (2) BSRCM provides not only deterministic results but also rich uncertainty information for real-time correction results, such as the mean, error variance, and confidence intervals of flow discharge at any time during the flood event; (3) BSRCM can achieve better performance with a longer lead time; (4) BSRCM can achieve a good precision even with a small sample for parameter estimates. In addition to good precision, BSRCM can also provide further scientific grounding in flood control, operations and decision making for risk management.