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.

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

Harmonic Synthesis Design Methodology for Dynamic Spring Balancing

1996 ◽  
Vol 118 (4) ◽  
pp. 733-740 ◽  
Author(s):  
Eungsoo Shin ◽  
D. A. Streit
Keyword(s):  
Dynamic System ◽  
Initial Conditions ◽  
Phase 1 ◽  
System Optimization ◽  
Optimization Method ◽  
Optimization Techniques ◽  
System Response ◽  
Two Phase ◽  
Walking Machine ◽  
Synthesis Design

A new spring balancing technique, called a two-phase optimization method, is presented. Phase 1 uses harmonic synthesis to provide a system configuration which achieves an approximation to a desired dynamic system response. Phase 2 uses results of harmonic synthesis as initial conditions for dynamic system optimization. Optimization techniques compensate for nonlinearities in machine dynamics. Example applications to robot manipulators and to walking machine legs are presented and discussed.

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.

scholarly journals Does model performance improve with complexity? A case study with three hydrological models

2015 ◽  
Vol 523 ◽  
pp. 147-159 ◽  
Author(s):  
Rene Orth ◽  
Maria Staudinger ◽  
Sonia I. Seneviratne ◽  
Jan Seibert ◽  
Massimiliano Zappa
Keyword(s):  
Model Performance ◽  
Hydrological Models

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 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

scholarly journals Evaluating the Effect of Numerical Schemes on Hydrological Simulations: HYMOD as A Case Study

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 329
Author(s):  
Shiyan Zhang ◽  
Khalid Al-Asadi
Keyword(s):  
Hydrological Model ◽  
Optimal Parameter ◽  
Model Performance ◽  
Hydrological Models ◽  
Numerical Schemes ◽  
Order Of Accuracy ◽  
Explicit Schemes ◽  
Implicit Schemes ◽  
The Relationship

The importance of numerical schemes in hydrological models has been increasingly recognized in the hydrological community. However, the relationship between model performance and the properties of numerical schemes remains unclear. In this study, we employed two types of numerical schemes (i.e., explicit Runge-Kutta schemes with different orders of accuracy and partially implicit Euler schemes with different implicit factors) in the hydrological model (HYMOD) to simulate the flow hydrograph of the Leaf River basin from 1948 to 1988. Results computed by different numerical schemes were compared and the relationships between model performance and two scheme properties (i.e., the order of accuracy and the implicit factor) were discussed. Results showed that the more explicit schemes generally lead to the overestimation of flow hydrographs, whereas the more implicit schemes lead to underestimation. In addition, the numerical error tended to decrease with increasing orders of accuracy. As a result, the optimal parameter sets found by low-order schemes significantly deviated from those found by the analytical solution. The findings of this study can provide useful implications for designing suitable numerical schemes for hydrological models.

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
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