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

Abstract

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.

Real-time correction of water stage forecast using combination of forecasted errors by time series models and Kalman filter method

“…Hence, the forecast error correction can be carried out by a time series model, such as a ARMA, which takes advantage of the autocorrelation of past errors (e.g., Lekkas et al 2001;Nalbantis 2000;Broersen 2007;Wu et al 2012). The error correction method based on the time series model takes advantage of a tendency in the error sign to preserve the sequences of positive (overestimation) or negative errors (underestimation) (Nalbantis 2000).…”

“…Bloschl et al (2008) employed the ensemble KF to update states (grid soil moisture) of a spatially distributed flash forecasting model based on observed runoff. Another updating procedure directly adjusts model outputs, and does not import them into the model (e.g., Lundberg 1982;Xiong and O'Connor 2002;Wu et al 2012). Lundberg (1982) used the autoregressive (AR) model to improve the runoff estimation produced by the HBV model.…”

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

“…Since the development of real-time correction theory, many real-time correction models, including autoregressive models [8,9], Kalman filtering models [10][11][12][13], fuzzy models [14,15], and neural network models [16], etc., have been developed. Recently, combined approaches, such as the combination of filtering and error forecasting procedures, and the combination of forecasted errors with time series models and the Kalman filter method [17][18][19], were developed for real-time correction, and demonstrated their ability to provide improved results. Among these models, the auto-regressive and Kalman filtering models are found to produce better results in real-time correction than others [7].…”

The dichotomous Markov process with nonparametric test application; a decision support method in long-term river behavioural analysis: the Zayandeh Rud River; a case study from central Iran

“…Real-time river management (Wu et al, 2012) is essentially a multifaceted decision making process. In its nature, Real-time river management is connected with hydrologic alterations, which are affected by upstream artificial reservoirs and precipitation variability (Zhao et al, 2012).…”