Abstract: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.

Precipitation is a significant parameter in many aspects such as agriculture, water management and climate variability. To characterize rainy season variations is important to understand precipitation variability under the effect of climate change. In this study, rainy season features (i.e., onset, retreat and rainy‐season precipitation) over the Huaihe River basin (HRB) and the response to different types of El Niño–Southern Oscillation (ENSO), that is, central Pacific warming (CPW), eastern Pacific warming (EPW), eastern Pacific cooling (EPC), conventional ENSO and ENSO Modoki in the developing and decaying phases are evaluated. The multi‐scale moving t test was used to capture onset and retreat of rainy season. The possible dynamic causes of ENSO‐induced precipitation over the HRB, such as monsoon and atmospheric circulation, were also explored. Results show that (a) onset (retreat) of rainy season progressed northwards (westwards), with rainy‐season precipitation increasing from north to south; (b) rainy‐season precipitation showed a strong correlation to Sea Surface Temperature (SST) in Niño regions. Dry and wet signals were identified in different regions of the HRB in the developing and decaying phases of CPW, EPC and EPW. Special attention should be paid on decaying EPW, where totally dry signals were found, which can reach down to 25% below average precipitation; (c) developing El Niño Modoki and decaying El Niño showed totally dry signals, with decaying El Niño Modoki and conventional La Niña demonstrating overall increasing precipitation; (d) different performances of ENSO‐induced precipitation during rainy season over the HRB are attributable to the combined influences of the monsoon from the India Ocean and the anticyclonic flow in the western North Pacific (WNP). Stronger anticyclone and monsoon are generally associated with increasing rainy‐season precipitation. These results can improve predictability of rainy season features and ENSO‐induced precipitation over the Huaihe River basin.

Abstract:As a critical parameter of the steady uniform friction model, the roughness coefficient changes with flow unsteadiness in flood events; i.e., the flow conditions of the stream segment significantly affect the flow resistance. In this study, a modified formula was established to improve the unsteady friction simulation; ten terms relating to the first-and second-order time and space partial derivatives of hydraulic parameters were selected as additional terms. The results of a hydraulic experiment show that the hysteresis between flow depth and mean cross-sectional velocity cannot be neglected in unsteady flows that disturb the performance of a steady uniform friction model. Six terms have a strong correlation with objective friction. Further, three of them have a small variance in correlation coefficient. Then, the composition of the proposed formula was determined. The results show that adding too many additional terms provides better performance in the calibration phase, yet reduces the accuracy of the validation phase because of an overfitting phenomenon. The optimal number of additional terms is three, and the established formula can improve the unsteady friction simulation.

It has been observed in literature that for unsteady flow conditions the one-to-one relationships between flow depth, cross-sectional averaged velocity, and frictional resistance as determined from steady uniform flow cases may not be appropriate for these more complex flow systems. Thus, a general friction resistance formula needs to be modified through the addition of new descriptive terms to account for flow unsteadiness, in order to eliminate errors due to uniform and steady-flow assumptions. An extended Chezy formula incorporating both time and space partial derivatives of hydraulic parameters was developed using dimensional analysis to investigate the relationship between flow unsteadiness and friction resistance. Results show that the proposed formula performs better than the traditional Chezy formula for simulating real hydrograph cases whereby both formula coefficients are individually identified for each flood event and coefficients are pre-determined using other flood events as calibration cases. Although the extended Chezy formula as well as original Chezy formula perform worse with the increasing degree of flow unsteadiness, its results are less dramatically affected by unsteadiness intensity thereby improving estimations of flood routing. As a result, it tends to perform much better than traditional Chezy formula for severe flood events. Under more complex conditions whereby peak flooding events may occur predominantly under unsteady flow, the extended Chezy model may provide as a valuable tool for researchers, practitioners, and water managers for assessing and predicting impacts for flooding and for the development of more appropriate mitigation strategies and more accurate risk assessments.

No abstract

No abstract

Ａ ｂｉｖａｒｉａｔｅ ｃｏｕｐｌｉｎｇ ｒｉｖｅｒ ｆｌｏｏｄ ｒｏｕｔｉｎｇ ｍｏｄｅｌ ｂａｓｅｄ ｏｎ ｃｏｎｔｉｎｕｉｔｙ ｅｑｕａｔｉｏｎＡｂｓｔｒａｃｔ： Ａ ｓｉｇｎｉｆｉｃａｎｔ ｉｎａｂｉｌｉｔｙ ｏｆ ｔｈｅ ｅｘｉｓｔｉｎｇ ｒｉｖｅｒ ｆｌｏｏｄ ｒｏｕｔｉｎｇ ｍｏｄｅｌｓ ｉｓ ｔｈｅｉｒ ｌｉｍｉｔａｔｉｏｎｓ ｔｏ ｓｉｍｕｌａｔｅ ｓｉｎｇｌｅ ｖａｒｉａｂｌｅ （ ｄｉｓｃｈａｒｇｅ ｏｒ ｗａｔｅｒ ｓｔａｇｅ） ． Ｔｈｅ ｒｅｓｅａｒｃｈ ｐｒｏｐｏｓｅｓ ａ " ｇｅｎｅｒａｌ ｂｉｖａｒｉａｔｅ" ｃｏｕｐｌｉｎｇ ｒｏｕｔｉｎｇ ｍｅｔｈｏｄ ｔｈａｔ ｉｍｐｒｏｖｅｓ ｔｈｅ ｕｎｉｖｅｒｓａｌｉｔｙ ｏｆ Ｍｕｓｋｉｎｇｕｍ ｍｅｔｈｏｄ ａｎｄ ｃａｎ ｓｉｍｕｌａｔｅ ｄｏｕｂｌｅ ｖａｒｉａｂｌｅｓ ｓｉｍｕｌｔａｎｅｏｕｓｌｙ． Ｔｈｅ ｐｒｏｐｏｓｅｄ ｍｏｄｅｌ ｉｓ ｂａｓｅｄ ｏｎ ｔｈｅ ｆｌｏｗ ｃｏｎｔｉｎｕｉｔｙ ｅｑｕａｔｉｏｎ ａｎｄ ｔｗｏ ｄｉｆ• ｆｅｒｅｎｔ ｆｏｒｍｓ ｏｆ ｒｉｖｅｒ ｒｅａｃｈ ｓｔｏｒａｇｅ ｅｑｕａｔｉｏｎ： （１） ｔｈｅ ｓｔｏｒａｇｅ ｏｆ ａ ｒｉｖｅｒ ｃｈａｎｎｅｌ ｅｑｕａｌｓ ｔｈｅ ｐｒｏｄｕｃｔ ｏｆ ｔｈｅ ｍｅａｎ ｃｒｏｓｓ-ｓｅｃｔｉｏｎａｌ ａｒｅａ ａｎｄ ｔｈｅ ｒｉｖｅｒ ｃｈａｎｎｅｌ ｌｅｎｇｔｈ； （２） ｔｈｅ ｓｔｏｒａｇｅ ｏｆ ａ ｒｉｖｅｒ ｃｈａｎｎｅｌ ｅｑｕａｌｓ ｔｈｅ ｐｒｏｄｕｃｔ ｏｆ ｔｈｅ ｍｅａｎ ｄｉｓｃｈａｒｇｅ ｏｆ ａ ｒｉｖｅｒ ｃｈａｎｎｅｌ ａｎｄ ｔｈｅ ｆｌｏｗ ｔｒａｖｅｌ ｔｉｍｅ． Ｉｎ ｏｒｄｅｒ ｔｏ ｃｏｎｓｉｄｅｒ ｔｈｅ ｒｅｐｒｅｓｅｎｔａｔｉｖｅ ｏｆ ｄｉｖｅｒｓｅ ｆａｃｔｏｒｓ， ｉｎｃｌｕｄｉｎｇ ｇｅｏｇｒａｐｈｉｃａｌ ｓｃｏｐｅ， ｒｉｖｅｒ ｃｈａｎｎｅｌ ｆｅａｔｕｒｅｓ， ｆｌｏｏｄ ｍａｇｎｉｔｕｄｅ， ｈｙｄｒａｕｌｉｃ ｃｈａｒａｃｔｅｒｉｓｔｉｃｓ ａｎｄ ｅｔ ａｌ， ｔｈｅ ｐｒｏｐｏｓｅｄ ｍｏｄｅｌ ｉｓ ｔｅｓｔｅｄ ｂｙ ｏｂｓｅｒｖｅｄ ｄａｔａ ｏｆ ｆｌｏｏｄ ｓｅａｓｏｎｓ ｗｈｉｃｈ ｉｓ ｓｅｌｅｃｔ• ｅｄ ｆｏｒｍ １６ ｒｉｖｅｒｓ ｃｈａｎｎｅｌｓ ｏｆ ４ ｒｉｖｅｒ ｂａｓｉｎｓ ｉｎ Ｃｈｉｎａ． Ｔｈｅ ｒａｔｉｏｎａｌｉｔｙ ｏｆ ｍｏｄｅｌ ｓｔｒｕｃｔｕｒｅ ａｎｄ ｐｅｒｆｏｒｍａｎｃｅ ｏｆ ｍｏｄｅｌ ｓｉｍｕｌａｔｉｏｎｓ ａｒｅ ｄｅｔｅｒｍｉｎｅｄ ｃｏｍｐｒｅｈｅｎｓｉｖｅｌｙ． Ｗｈｅｎ ｃｏｍｐａｒｅｄ ｗｉｔｈ Ｍｕｓｋｉｎｇｕｍ ｒｏｕｔｉｎｇ ｍｅｔｈｏｄ， ｔｈｅ ａｐｐｒｏａｃｈ ｃａｎ ｌｅａｄ ｔｏ ｍｏｒｅ ａｃｃｕｒａｔｅ ｓｉｍｕｌａｔｉｏｎｓ ａｎｄ ｔｈｅ ｐｅｒｆｏｒｍａｎｃｅ ｉｓ ｍｏｒｅ ｓｔａｂｌｅ ｔｈａｎ Ｍｕｓｋｉｎｇｕｍ ｒｏｕｔｉｎｇ ｍｅｔｈｏｄ． Ｔｈｅ ｐｒｏｐｏｓｅｄ ｍｏｄｅｌ ｉｓ ｍｏｒｅ ｖｅｒｓａｔｉｌｅ ｔｈａｎ Ｍｕｓｋｉｎｇｕｍ ｍｏｄｅｌ ｉｎ ｒｅａｌ ｃａｓｅｓ．

The water level is a critical hydraulic parameter for inland ship safe navigation, as well as an important variable in inland waterway transport minoring and assistant systems. As a basic and traditional method, the one-dimensional (1D) hydrodynamic model is adapted to simulate river sections/waterway segments to obtain water levels numerically. However, the friction factor, i.e., Manning’s coefficient n, is a sensitive parameter for the traditional 1D hydrodynamic model. Its calibration or identification is not only very time-consuming but also unpractical. Due to its sensitivity to the simulation results, usually, one identified parameter cannot be adopted into other flow scenarios. It has been concluded that the unfitness of the traditional empirical quasi-steady friction formulae leads to these consequences/phenomena. Besides finding advanced parameter calibration algorithms and updating friction parameters dynamically, employing a true unsteady friction formula to replace the quasi-steady friction formula is a thorough solution to the problem. In this study, we introduced a newly proposed 1D unsteady friction formula to the momentum equation of the Saint-Venant Equations, thus a modified 1D hydrodynamic model was developed. To validate its capability in simulating water levels, the modified model was adopted into the Xia-la-xian – La-he-lian section of Daying River; and compared with the traditional model with the Manning formula. Results showed that the modified hydrodynamic model performs better in both water level and cross-sectional average velocity simulation. The research results can be used to support the construction of intelligent water level warning systems, intelligent shipping, and digital waterway transportation platforms.