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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Vydáno v:	Water resources research Ročník 54; číslo 7; s. 4730 - 4749
Hlavní autoři:	Sun, Y., Bao, W., Jiang, P., Ji, X., Gao, S., Xu, Y., Zhang, Q., Si, W.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Washington John Wiley & Sons, Inc 01.07.2018
Témata:	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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Abstract	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
AbstractList	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. 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. 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 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
Author	Gao, S. Zhang, Q. Bao, W. Jiang, P. Xu, Y. Sun, Y. Ji, X. Si, W.
Author_xml	– sequence: 1 givenname: Y. surname: Sun fullname: Sun, Y. organization: Hohai University – sequence: 2 givenname: W. surname: Bao fullname: Bao, W. organization: Hohai University – sequence: 3 givenname: P. orcidid: 0000-0002-8472-1221 surname: Jiang fullname: Jiang, P. email: peng.jiang.j@gmail.com organization: Desert Research Institute – sequence: 4 givenname: X. surname: Ji fullname: Ji, X. organization: Liaoning Administration for Hydrology and Water Resources Investigation – sequence: 5 givenname: S. surname: Gao fullname: Gao, S. organization: Liaoning Administration for Hydrology and Water Resources Investigation – sequence: 6 givenname: Y. surname: Xu fullname: Xu, Y. organization: Liaoning Administration for Chaihe Reservoir – sequence: 7 givenname: Q. surname: Zhang fullname: Zhang, Q. organization: Bei Fang Investigation, Design and Research CO.LTD – sequence: 8 givenname: W. surname: Si fullname: Si, W. organization: Hohai University
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Copyright	2018. American Geophysical Union. All Rights Reserved. 2018. American Geophysical Union. All rights reserved.
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Snippet	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...
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SubjectTerms	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
Title	Development of Multivariable Dynamic System Response Curve Method for Real‐Time Flood Forecasting Correction
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