Weimin Bao

The existing parameter calibration methods are based on the objective function surface to find the optimal value. This kind of method has the problems such as unstable result, poor convergence performance, low efficiency and failure to find the global optimal value. Based on the analysis of the objective function structure and the information that it provides for the parameter calibration, the essential problems existing in the present method was found in this paper. And the paper also found that the information provided by the parameter function surface is more direct and effective than by the objective function surface. Furthermore, the nonlinear model function can be linearized by the system response relationship between the increment of the dependent variables and the increment of parameters. Based on these researches, this paper proposed the system response parameter calibration method based on the parametric function surface. Firstly, the method is verified by an ideal model. The results showed that all the sensitive parameters could reach the real values not influenced by the different initial parameter values with higher convergence speed and accuracy, which verified that the method is feasible. Lastly, the XAJ Model parameters based on Shaowu basin measured data were also calibrated by the method. The results showed that the stable optimal parameter values could also be quickly got. So the system response parameter calibration method is an effective parameters optimization 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.

In recent decades, the streamflow and sediment of the Yellow River has decreased sharply, especially the sediment discharge. The factors that lead to this phenomenon have become a widely concerned problem of the whole society. The analysis of abrupt change points of hydrological series is the key to divide datum period, so it is an important work in the research of streamflow and sediment reduction cause. So far, many methods have been proposed to detect abrupt change. However, most methods have great uncertainty due to the deficiencies of irrational structure of test statistics, ideal hypothesis or subjectivity. In this paper, a new method called moving average difference method for abrupt change points detection is proposed. It is proved to be effective through comparison with four commonly used methods via both synthetic series and real data case study. The results show that the proposed method has four distinct advantages: (1) The test statistic structure of the method has physical significance and is intuitive to understand; (2) It is more accurate in abrupt change detection; (3) It can detect all of the abrupt change points at one time; (4) It can detect the abrupt changes and calculate the corresponding mutation intensity simultaneously.

The dynamic system response curve (DSRC) is commonly applied as a real-time flood forecasting error correction method to improve the accuracy of real-time flood forecasting. It has been widely recognized that the least squares (OLS/LS) method, employed by DSRC, breaks down ill-posed problems, and therefore, the DSRC method may lead to deterioration in performance caused by meaningless solutions. To address this problem, a diagnostically theoretical analysis was conducted to investigate the relationship between the numerical solution of the Fredholm equation of the first kind and the DSRC method. The analysis clearly demonstrates the derivation of the problem and has implications for an improved approach. To overcome the unstable problem, a new method using regularization techniques (Tikhonov regularization and L-Curve criterion) is proposed. Moreover, in this study, to improve the performance of hydrological models, the new method is used as an error correction method to correct a variable from a hydrological model. The proposed method incorporates the information from a hydrological model structure. Based on the analysis of the hydrological model, the free water storage of the Xinanjiang rainfall-runoff (XAJ) model is corrected to improve the model’s performance. A numerical example and a real case study are presented to compare the two methods. Results from the numerical example indicate that the mean Nash–Sutcliffe efficiency value (NSE) of the regularized DSRC method (RDSRC) decreased from 0.99 to 0.55, while the mean NSE of DSRC decreased from 0.98 to −1.84 when the noise level was increased. The overall performance measured by four different criteria clearly demonstrates the robustness of the RDSRC method. Similar results were obtained for the real case study. The mean NSE of 35 flood events obtained by RDSRC method was 0.92, which is significantly higher than the mean NSE of DSRC (0.7). The results demonstrate that the RDSRC method is much more robust than the DSRC method. The applicability and usefulness of the RDSRC approach for real-time flood forecasting is demonstrated via the numerical example and the real case study.

One-dimensional (1D) hydrodynamic models based on the Saint-Venant equations (the SVN model) have been extensively used for flow simulation in tidal rivers. In rivers with significant tidal effects, the inconsistencies between actual flow characteristics and simplifying assumptions that the Saint-Venant equations are derived based on can introduce unignorable errors into the application of the SVN model. To reduce the errors contributed by the factors the SVN model can not completely account for, such as the tidal effect, an approach of incorporating a function into the calculated velocity by the SVN model was proposed in this study. Fourier series was employed to construct the function. By combining the proposed approach with the flow continuity equation, a 1D hydrodynamic model (the FSV model) was constructed to improve the performance of the SVN model in tidal rivers. The simulation results in the tidal reaches of the Qiantang River and the Changjiang River show that the FSV model performs better than the SVN model alone. It indicates that the proposed approach is reasonable and effective to reduce the errors due to the factors the SVN model can not completely considered in applications.