scholarly journals Application of rainfall dynamic system response curve method for streamflow and sediment simulation in loess region

2020 ◽  
Vol 32 (2) ◽  
pp. 528-538
Author(s):  
BAO Weimin ◽  
◽  
GU Yuwei ◽  
SI Wei ◽  
HOU Lu ◽  
...  
Keyword(s):  
Dynamic System ◽  
Response Curve ◽  
System Response ◽  
Curve Method ◽  
Loess Region

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

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.

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 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

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.

Efficient Wellbore Cement Sheath Design Using the SRC (System Response Curve) Method (SPE94176)

2005 ◽  
Author(s):  
D. Fourmaintraux ◽  
A.-P. Bois ◽  
C. Franco ◽  
B. Fraboulet ◽  
P. Brossollet
Keyword(s):  
Response Curve ◽  
System Response ◽  
Curve Method ◽  
Cement Sheath

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.

Efficient Wellbore Cement Sheath Design Using the SRC (System Response Curve) Method

2005 ◽  
Author(s):  
Dominique M. Fourmaintraux ◽  
Axel-Pierre Bois ◽  
Christophe franco ◽  
Bernard Fraboulet ◽  
Pierre brossolllet
Keyword(s):  
Response Curve ◽  
System Response ◽  
Curve Method ◽  
Cement Sheath
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