Rivers with fluvial equilibrium are characterized by bed deformation adjustment. The erosion-deposition area in cross-section reflects this characteristic, which is a base of researching the river scour and deposition evolution by time series analysis. With an erosion-deposition area indicator method proposed in this paper, the time series of erosion-deposition area quantity at Bygl and Shhk stations were obtained with the series duration of 31 years from 1976 to 2006. After analysis of its trend and mutation, three different tendencies about the evolution were observed in general from the quasi-equilibrium phase through a rapid shrinkage to the final new quasi-equilibrium. It is also found that the trend of erosion-deposition area series will change once a big flood occurred in some of the tributaries, and its ever greater influence is due to the decrease of deluge with the completion of upstream reservoirs. Almost all the turning points were coincident with the time when hyper-concentrated sediment flood occurred in some tributaries. With the time series of clear mutations since the late 1990s, the Inner Mongolian Reach has been in a new equilibrium phase. This can be concluded in two aspects. 1. The absence of big floods and sediment transportation from tributaries result in the river shrinkage, and to regain the channel flow-carrying capacity in Inner Mongolian Reach a large flood is needed both of high peak discharge and of lengthy interval to destroy the new equilibrium. 2. The proposed method of erosion-deposition area indicator is of great help to channel scour-deposition evolution analysis because it can demonstrate real time deformation of cross section in quantity.The Inner Mongolian Reach is located in the upstream of the lower Yellow River with 10 tributaries of seasonal rivers originating in the Hobq Desert. In recent decades, the deposition rate along this reach has increased with rapid shrinkage of its main channel and a remarkable decrease of its flow-carrying capacity under the influence of the changing climate, reservoir regulation and incoming sediment from its 10 tributaries; the unstableness of river regime, severe bank collapse and the deposition of tributary entrance make it a grim situa-
The construction of check dams in northwestern China has resulted in nonstationary changes in flood peak discharge series; the stationary assumption of the conventional hydrological frequency analysis is no longer satisfied. According to the characteristics of the construction and operation of check dams, the nonstationarity of flood peak discharge series are largely induced by changes in the effective runoff generation area (i.e., the basin area minus the area controlled by check dams). Knowing the power function relationship between the flood peak discharge and the basin area, we can remove the influence of the effective runoff generation area and convert the original nonstationary series into a stationary series. This de-nonstationarity method can achieve stationarity in the first and second moments simultaneously. Therefore, we can calculate the design value of the reconstructed series using the conventional frequency analysis method. According to the effective runoff generation area under design conditions, we can then obtain the corresponding design flood of the original series. We applied this method to the Mahuyu River basin to obtain the design flood under nonstationarity. Due to the consideration of the deterministic influence of check dams during the de-nonstationarity process, the uncertainty analyzed by the bootstrap method is obviously small.
Due to climate change and human activities, the statistical characteristics of annual runoff series of many rivers around the world exhibit complex nonstationary changes, which seriously impact the frequency analysis of annual runoff and are thus becoming a hotspot of research. A variety of nonstationary frequency analysis methods has been proposed by many scholars, but their reliability and accuracy in practical application are still controversial. The recently proposed Mechanism-based Reconstruction (Me-RS) method is a method to deal with nonstationary changes in hydrological series, which solves the frequency analysis problem of the nonstationary hydrological series by transforming nonstationary series into stationary Me-RS series. Based on the Me-RS method, a calculation method of design annual runoff under the nonstationary conditions is proposed in this paper and applied to the Jialu River Basin (JRB) in northern Shaanxi, China. From the aspects of rationality and uncertainty, the calculated design value of annual runoff is analyzed and evaluated. Then, compared with the design values calculated by traditional frequency analysis method regardless of whether the sample series is stationary, the correctness of the Me-RS theory and its application reliability is demonstrated. The results show that calculation of design annual runoff based on the Me-RS method is not only scientific in theory, but also the obtained design values are relatively consistent with the characteristics of the river basin, and the uncertainty is obviously smaller. Therefore, the Me-RS provides an effective tool for annual runoff frequency analysis under nonstationary conditions.
Prediction of the peak break‐up water level, which is the maximum instantaneous stage during ice break‐up, is desirable to allow effective ice flood mitigation, but traditional hydrologic flood routing techniques are not efficient in addressing the large uncertainties caused by numerous factors driving the peak break‐up water level. This research provides a probability prediction framework based on vine copulas. The predictor variables of the peak break‐up water level are first chosen, the pair copula structure is then constructed by using vine copulas, the conditional density distribution function is derived to perform a probability prediction, and the peak break‐up water level value can then be estimated from the conditional density distribution function given the conditional probability and fixed values of the predictor variables. This approach is exemplified using data from 1957 to 2005 for the Toudaoguai and Sanhuhekou stations, which are located in the Inner Mongolia Reach of the Yellow River, and the calibration and validation periods are divided at 1986. The mean curve of the peak break‐up water level estimated from the conditional distribution function can capture the tendency of the observed series at both the Toudaoguai and Sanhuhekou stations, and more than 90% of the observed values fall within the 90% prediction uncertainty bands, which are approximately twice the standard deviation of the observed series. The probability prediction results for the validation period are consistent with those for the calibration period when the nonstationarity of the marginal distributions for the Sanhuhekou station are considered. Compared with multiple linear regression results, the uncertainty bands from the conditional distribution function are much narrower; moreover, the conditional distribution function is more capable of addressing the nonstationarity of predictor variables, and the conclusions are confirmed by jackknife analysis. Scenario predictions for cases in which the peak break‐up water level is likely to be higher than the bankfull water level can also be conducted based on the conditional distribution function, with good performance for the two stations.
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