scholarly journals Development of a Regularized Dynamic System Response Curve for Real-Time Flood Forecasting Correction

Water ◽  
2018 ◽  
Vol 10 (4) ◽  
pp. 450 ◽  
Author(s):  
Yiqun Sun ◽  
Weimin Bao ◽  
Peng Jiang ◽  
Wei Si ◽  
Junwei Zhou ◽  
...  
Keyword(s):  
Dynamic System ◽  
Real Time ◽  
Response Curve ◽  
Flood Forecasting ◽  
System Response

scholarly journals Sediment yield error correction by dynamic system response curve method in real-time flood forecasting

2020 ◽  
Author(s):  
Lu Hou ◽  
Weimin Bao ◽  
Wei Si ◽  
Peng Jiang ◽  
Peng Shi ◽  
...  
Keyword(s):  
Dynamic System ◽  
Real Time ◽  
Sediment Yield ◽  
Response Curve ◽  
Yellow River ◽  
Management Strategies ◽  
Real Data ◽  
Flood Forecasting ◽  
Rain Gauge ◽  
System Response

Abstract Real-time flood forecasting requires accurate and reliable estimates of the uncertainty to make efficient flood event management strategies. However, the accuracy of flood forecasts can be severely affected by errors in the estimates of sediment yield in the loess region. To improve the accuracy of sediment-laden flood forecasts generated using streamflow-sediment coupled (SSC) model, an error feedback correction method based on the dynamic system response curve (DSRC) is proposed. The physical basis of the system response curve is the sediment concentration of the hydrological model. The theoretical basis of the method is the differential of the system response function of the sediment yield time series. The effectiveness of DSRC method is evaluated via an ideal case and three real-data cases with different basin scales of the Yellow River. Results suggest that the DSRC method can effectively improve the accuracy and stability of sediment transport forecasts by providing accurate estimates of the sediment yield errors. The degree of forecast improvement is scale dependent and is more significant for larger basins with lower rain gauge densities. Besides, the DSRC method is relatively simple to apply without the need to modify either the model structure or parameters in real-time flood forecasting.

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

2018 ◽  
Vol 54 (7) ◽  
pp. 4730-4749 ◽  
Author(s):  
Y. Sun ◽  
W. Bao ◽  
P. Jiang ◽  
X. Ji ◽  
S. Gao ◽  
...  
Keyword(s):  
Dynamic System ◽  
Real Time ◽  
Response Curve ◽  
Flood Forecasting ◽  
System Response ◽  
Curve Method

Updating real‐time flood forecasts via the dynamic system response curve method

2015 ◽  
Vol 51 (7) ◽  
pp. 5128-5144 ◽  
Author(s):  
Wei Si ◽  
Weimin Bao ◽  
Hoshin V. Gupta
Keyword(s):  
Dynamic System ◽  
Real Time ◽  
Response Curve ◽  
System Response ◽  
Curve Method

scholarly journals Improved Dynamic System Response Curve Method for Real‐Time Flood Forecast Updating

2019 ◽  
Vol 55 (9) ◽  
pp. 7493-7519 ◽  
Author(s):  
Wei Si ◽  
Hoshin V. Gupta ◽  
Weimin Bao ◽  
Peng Jiang ◽  
Wenzhuo Wang
Keyword(s):  
Dynamic System ◽  
Real Time ◽  
Response Curve ◽  
System Response ◽  
Flood Forecast ◽  
Curve Method ◽  
Forecast Updating

The Research of Rainfall Error Correction Based on System Response Curve

2013 ◽  
Vol 368-370 ◽  
pp. 335-339
Author(s):  
Wei Si ◽  
Wei Min Bao ◽  
Hong Yan Wang ◽  
Si Min Qu
Keyword(s):  
Error Correction ◽  
Real Time ◽  
Response Curve ◽  
Flood Forecasting ◽  
Good Effect ◽  
System Response ◽  
Error Feedback ◽  
Model Tracing ◽  
Curve Method ◽  
Source Of Information

The rainfall error affects the accuracy of flood forecasting directly, and the error correction is very important to improve the accuracy of real-time flood forecasting. The system response curve was introduced into the real-time flood forecast updating system, and the error feedback updating model tracing the source of information was established in this paper. In order to certificate the feasibility, rationality and effectiveness of the method multi-directionally, the system response curve method and the second order autoregressive error correction were used in updating the 13 flood events of the Wangjiaba sub-basin of Huaihe River Basin respectively. The results show that the system response curve (SRC) method has physical conception, reasonable structure and good effect. The method will not lose the forecasting period and without increasing the parameters. In this study, the average NS of system response curve method was larger than 0.920. So, this method can be widely used in rainfall error correction for real-time flood forecasting.

Multi-source error correction for flood forecasting based on dynamic system response curve method

2021 ◽  
pp. 125908
Author(s):  
Zhongmin Liang ◽  
Yixin Huang ◽  
Vijay P. Singh ◽  
Yiming Hu ◽  
Binquan Li ◽  
...  
Keyword(s):  
Dynamic System ◽  
Error Correction ◽  
Response Curve ◽  
Flood Forecasting ◽  
System Response ◽  
Curve Method

scholarly journals Development of dynamic system response curve method for estimating initial conditions of conceptual hydrological models

2018 ◽  
Vol 20 (6) ◽  
pp. 1387-1400
Author(s):  
Yiqun Sun ◽  
Weimin Bao ◽  
Peng Jiang ◽  
Xuying Wang ◽  
Chengmin He ◽  
...  
Keyword(s):  
Dynamic System ◽  
Response Curve ◽  
Initial Conditions ◽  
Model Performance ◽  
System Response ◽  
Hydrological Models ◽  
Flood Prediction ◽  
Ill Posed ◽  
Value Decomposition

Abstract The dynamic system response curve (DSRC) has its origin in correcting model variables of hydrologic models to improve the accuracy of flood prediction. The DSRC method can lead to unstable performance since the least squares (LS) method, employed by DSRC to estimate the errors, often breaks down for ill-posed problems. A previous study has shown that under certain assumptions the DSRC method can be regarded as a specific form of the numerical solution of the Fredholm equation of the first kind, which is a typical ill-posed problem. This paper introduces the truncated singular value decomposition (TSVD) to propose an improved version of the DSRC method (TSVD-DSRC). The proposed method is extended to correct the initial conditions of a conceptual hydrological model. The usefulness of the proposed method is first demonstrated via a synthetic case study where both the perturbed initial conditions, the true initial conditions, and the corrected initial conditions are precisely known. Then the proposed method is used in two real basins. The results measured by two different criteria clearly demonstrate that correcting the initial conditions of hydrological models has significantly improved the model performance. Similar good results are obtained for the real case study.

scholarly journals Improvement in Ridge Coefficient Optimization Criterion for Ridge Estimation-Based Dynamic System Response Curve Method in Flood Forecasting

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3483
Author(s):  
Kexin Liu ◽  
Weimin Bao ◽  
Yufeng Hu ◽  
Yiqun Sun ◽  
Dongjing Li ◽  
...  
Keyword(s):  
Dynamic System ◽  
Response Curve ◽  
Estimation Method ◽  
Real Data ◽  
Optimization Criterion ◽  
Sum Of Squares ◽  
System Response ◽  
Ridge Estimation ◽  
Coefficient Optimization ◽  
Curve Method

The ridge estimation-based dynamic system response curve (DSRC-R) method, which is an improvement of the dynamic system response curve (DSRC) method via the ridge estimation method, has illustrated its good robustness. However, the optimization criterion for the ridge coefficient in the DSRC-R method still needs further study. In view of this, a new optimization criterion called the balance and random degree criterion considering the sum of squares of flow errors (BSR) is proposed in this paper according to the properties of model-simulated residuals. In this criterion, two indexes, namely, the random degree of simulated residuals and the balance degree of simulated residuals, are introduced to describe the independence and the zero mean property of simulated residuals, respectively. Therefore, the BSR criterion is constructed by combining the sum of squares of flow errors with the two indexes. The BSR criterion, L-curve criterion and the minimum sum of squares of flow errors (MSSFE) criterion are tested on both synthetic cases and real-data cases. The results show that the BSR criterion is better than the L-curve criterion in minimizing the sum of squares of flow residuals and increasing the ridge coefficient optimization speed. Moreover, the BSR criterion has an advantage over the MSSFE criterion in making the estimated rainfall error more stable.

scholarly journals System response curve correction method of runoff error for real-time flood forecast

2020 ◽  
Vol 51 (6) ◽  
pp. 1312-1331
Author(s):  
Qian Li ◽  
Caisong Li ◽  
Huanfei Yu ◽  
Jinglin Qian ◽  
Linlin Hu ◽  
...  
Keyword(s):  
Real Time ◽  
River Basin ◽  
Forecast Model ◽  
Flood Forecasting ◽  
Correction Method ◽  
System Response ◽  
Flood Forecast ◽  
Feedback Correction ◽  
Underlying Surface Conditions ◽  
Source Of Information

Abstract Multiple factors including rainfall and underlying surface conditions make river basin real-time flood forecasting very challenging. It is often necessary to use real-time correction techniques to modify the forecasting results so that they reach satisfactory accuracy. There are many such techniques in use today; however, they tend to have weak physical conceptual basis, relatively short forecast periods, unsatisfactory correction effects, and other problems. The mechanism that affects real-time flood forecasting error is very complicated. The strongest influencing factors corresponding to this mechanism affect the runoff yield of the forecast model. This paper proposes a feedback correction algorithm that traces back to the source of information, namely, modifies the watershed runoff. The runoff yield error is investigated using the principle of least squares estimation. A unit hydrograph is introduced into the real-time flood forecast correction; a feedback correction model that traces back to the source of information. The model is established and verified by comparison with an ideal model. The correction effects of the runoff yield errors are also compared in different ranges. The proposed method shows stronger correction effect and enhanced prediction accuracy than the traditional method. It is also simple in structure and has a clear physical concept without requiring added parameters or forecast period truncation. It is readily applicable in actual river basin flood forecasting scenarios.

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