Real-time correction of water stage forecast during rainstorm events using combination of forecast errors

This study proposes a real-time error correction method for the forecasted water stage using a combination of forecast errors estimated by the time series models, AR(1), AR(2), MA(1) and MA(2), and the average deviation model to update the water stage forecast during rainstorm events. During flood forecasting and warning operations, the proposed real-time error correction method takes advantage of being individually and continuously implemented and the results not being updated to the hydrological model and hydraulic routings so as to save computational time by recalibrating the parameters of the proposed methods with real-time observation. For model validation, the current study adopts the observed and forecasted data on a severe typhoon, Morakot, collected at eight water level gauges in Southern Taiwan and provided by the flood forecast system FEWS_Taiwan, which is linked with the reliable quantitative precipitation forecast (QPF) at 3 h of lead time provided by the Center Weather Bureau in Taiwan, as the model validation. The results of numerical experiments indicate that the proposed real-time error correction method can effectively reduce the errors of forecasted water stages at the 1-, 2-, and 3-h lead time and so enhance the reliability of forecast information issued by the FEWS_Taiwan. By means of real-time estimating potential forecast error, the uncertainties in hydrology, modules as well as associated parameters, and physiographical features of the river can be reduced.     

Modeling the effect of uncertainties in rainfall characteristics on flash flood warning based on rainfall thresholds

Natural Hazards - This study proposes a risk assessment framework for quantifying the reliability of the rainfall threshold used in flash flood warning, which should be influenced by the...     

Modeling risk analysis for forecasting peak discharge during flooding prevention and warning operation

This work proposes a risk analysis model to evaluate the risk of underestimating the predicted peak discharge, i.e. the exceedance of probability due to the uncertainties in rainfall information (rainfall depth, duration, and storm pattern) and the parameters of the rainfall-runoff model (Sacramento Soil Moisture Accounting model, SAC-SMA) during the flooding prevention and warning operation. The proposed risk analysis model is combined with the multivariate Monte Carlo simulation method and the Advance First-Order Second-Moment method (AFOSM). The observed rainfall and discharge measured at Yu-feng Basin study area in Shihmen reservoir watershed is used in the model development and application. The results of the model application indicate that the proposed risk analysis model can analyze the sensitivity of the uncertainty factors for the predicted peak discharge and evaluates the variation of the probability of exceeding the predicted peak discharge with respect to the rainfall depth and storm duration. In addition, the result of risk analysis for a real rainstorm event, Typhoon Morakot, shows that the proposed model successfully explores the risk of underestimating the predicted peak discharge using SAC-SMA and forecasted rainfall information and provides a probabilistic forecast of the peak discharge.