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

Abstract

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.

Study on Ensemble Calibration of Flood Forecasting Based on Response curve of rainfall dynamic system and LSTM.

“…The error correction of the model can alleviate the uncertainty of the model prediction (Costabile et al, 2015). The sources of flood forecasting errors are mainly input errors, parameter errors, model state variables, and output errors (Huang et al, 2021;Zhang et al, 2005;Ge et al, 2005), etc. In general, the most direct way to reduce the forecast error is to correct the output error, that is, to correct the error between the forecast result of the model and the observed value.…”

Risk Analysis of Reservoir Flood Routing Calculation Based on Inflow Forecast Uncertainty

“…In addition, the characteristic water levels for the Meishan Reservoir are also available [20] In total, there are 20 rain gauges, two hydrologic stations, and one pan evaporation gauge (E601) distributed in the Meishan basin ( Figure 1). Observed daily data of rainfall, pan evaporation, and discharge for the period of 1980-2010, and hourly rainfall and discharge data for some flood events in the rainy season from April to October were collected for the analysis.…”

“…The Xinanjiang model has been widely studied and applied to flood forecasts in (semi-) humid regions of China since developed in early 1970s [18][19][20]. The saturation excess mechanism was assumed for runoff formation.…”

“…Inflow forecast uncertainty was analyzed through the integration of the Xinanjiang hydrologic model [18][19][20] and the Hydrologic Uncertainty Processor (HUP), which is a sub-model of the Bayesian Forecasting System (BFS) [8]. It is noted that risk usually includes both exposure loss and the possibility of loss, but as an example, only the possibility of loss (reservoir water level exceeding a defined safety control level) was studied in this paper.…”