Development of Multivariable Dynamic System Response Curve Method for Real‐Time Flood Forecasting Correction

Error correction method is widely used to improve the performance of flood forecasting. The Dynamic System Response Curve method (DSRC) has been proposed as an error correction method to improve the performance of hydrological modeling. One of the critical problems is the unstable performance caused...

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Veröffentlicht in:Water resources research Jg. 54; H. 7; S. 4730 - 4749
Hauptverfasser: Sun, Y., Bao, W., Jiang, P., Ji, X., Gao, S., Xu, Y., Zhang, Q., Si, W.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Washington John Wiley & Sons, Inc 01.07.2018
Schlagworte:
algorithms
Case studies
Dynamical systems
Economic models
Error correction
Error correction & detection
Estimation
Flood forecasting
Floods
Hydrologic models
Hydrology
Lead time
Methods
model validation
Modelling
Performance enhancement
River discharge
Variables
water
ISSN:0043-1397, 1944-7973
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Zusammenfassung:Error correction method is widely used to improve the performance of flood forecasting. The Dynamic System Response Curve method (DSRC) has been proposed as an error correction method to improve the performance of hydrological modeling. One of the critical problems is the unstable performance caused by the ill‐posed property of the model structure and the inability of estimating multiple variables. To address this problem, the original structure of DSRC was modified to enable the capability of estimating multiple variables. Using the variable forgetting factor recursive least squares algorithm (VFF‐RLS), we proposed an improved version of DSRC (VFF‐RLS‐MDSRC). The proposed method was tested in a synthetic case to examine the ability to correct state variables of a hydrological model. In addition, it was compared with the autoregressive technique in a real case study to evaluate the effects on the improvement of model performance. The results of the synthetic study indicate that the proposed method can significantly improve the performance of both the model output and the state variables. The results of the real case study indicate that the performance obtained by the proposed method tends to have a slower decline trend when increasing the lead time compared with autoregressive technique. Key Points Present a simple method that serves to overcome the difficulties when estimating multiple variables Present a new error correction method using the adaptive filter technique Compare the proposed method with the autoregressive technique in two real basins
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ISSN:0043-1397
1944-7973
DOI:10.1029/2018WR022555