首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到20条相似文献，搜索用时 15 毫秒
1.
The discharge hydrograph estimation in rivers based on reverse routing modeling and using only water level data at two gauged sections is here extended to the most general case of significant lateral flow contribution, without needing to deploy rainfall–runoff procedures. The proposed methodology solves the Saint‐Venant equations in diffusive form also involving the lateral contribution using a “head‐driven” modeling approach where lateral inflow is assumed to be function of the water level at the tributary junction. The procedure allows to assess the discharge hydrograph at ends of a selected river reach with significant lateral inflow, starting from the stage recorded there and without needing rainfall data. Specifically, the MAST 1D hydraulic model is applied to solve the diffusive wave equation using the observed stage hydrograph at the upstream section as upstream boundary condition. The other required data are (a) the observed stage hydrograph at the downstream section, as benchmark for the parameter calibration, and (b) the bathymetry of the river reach, from the upstream section to a short distance after the downstream gauged section. The method is validated with different flood events observed in two river reaches with a significant intermediate basin, where reliable rating curves were available, selected along the Tiber River, in central Italy, and the Alzette River, in Luxembourg. Very good performance indices are found for the computed discharge hydrographs at both the channel ends and along the tributaries. The mean Nash‐Sutcliffe value (NSq) at the channel ends of two rivers is found equal to 0.99 and 0.86 for the upstream and downstream sites, respectively. The procedure is also validated on a longer stretch of the Tiber River including three tributaries for which appreciable results are obtained in terms of NSq for the computed discharge hydrographs at both the channel ends for three investigated flood events.  相似文献   

2.
《水文科学杂志》2013,58(1):66-82
Abstract

An adaptive model for on-line stage forecasting is proposed for river reaches where significant lateral inflow contributions occur. The model is based on the Muskingum method and requires the estimation of four parameters if the downstream rating curve is unknown; otherwise only two parameters have to be determined. As the choice of the forecast lead time is linked to wave travel time along the reach, to increase the lead time, a schematization of two connected river reaches is also investigated. The variability of lateral inflow is accounted for through an on-line adaptive procedure. Calibration and validation of the model were carried out by applying it to different flood events observed in two equipped river reaches of the upper-middle Tiber basin in central Italy, characterized by a significant contributing drainage area. Even if the rating curve is unknown at the downstream section, the forecast stage hydrographs were found in good agreement with those observed. Errors in peak stage and time to peak along with the persistence coefficient values show that the model has potential as a practical tool for on-line flood risk management.  相似文献   

3.
The diffusive wave equation with inhomogeneous terms representing hydraulics with uniform or concentrated lateral inflow into a river is theoretically investigated in the current paper. All the solutions have been systematically expressed in a unified form in terms of response function or so called K-function. The integration of K-function obtained by using Laplace transform becomes S-function, which is examined in detail to improve the understanding of flood routing characters. The backwater effects usually resulting in the discharge reductions and water surface elevations upstream due to both the downstream boundary and lateral inflow are analyzed. With a pulse discharge in upstream boundary inflow, downstream boundary outflow and lateral inflow respectively, hydrographs of a channel are routed by using the S-functions. Moreover, the comparisons of hydrographs in infinite, semi-infinite and finite channels are pursued to exhibit the different backwater effects due to a concentrated lateral inflow for various channel types.  相似文献   

4.
Abstract

A composite model for real time forecasting of flash floods in the Ayalon stream in central Israel has been constructed. The model is composed of four kinds of sub-models: an autoregressive model for discharges at upstream stations on the two major tributaries; a travel-time model for the flow from these stations to the downstream station located on the main stem of the stream; a time-area concentration curve for subwatershed drainage between the upstream and downstream stations; and a recession curve for the downstream station. The model incorporates an adaptive mechanism for continuous correction of forecast errors. This mechanism is calibrated during an initial period of operation, and is subsequently operated throughout a flow event. The model issues simultaneous forecasts for seven lead times ranging from 0.5 to 3.5 h. This provides a proper input for a flood warning system which is required for safe operation of a major highway running along the banks of a torrent stream in the metropolitan area of Tel-Aviv.  相似文献   

5.
The aim of this paper is to quantify peakflow attenuation and/or amplification in a river, investigating lateral flow from the intermediate catchment during floods. This is a challenge for the study of the hydrological response of permeable/intermittent streams, and our contribution refers to a modelling framework based on the inverse problem for the diffusive wave model applied in a karst catchment. Knowing the upstream and downstream hydrographs on a reach between two stations, we can model the lateral one, given information on the hydrological processes involved in the intermediate catchment. The model is applied to 33 flood events in the karst reach of the Iton River in French Normandy where peakflow attenuation is observed. The monitored zone consists of a succession of losing and gaining reaches controlled by strong surface‐water/groundwater (SW/GW) interactions. Our results show that despite a high baseflow increase in the reach, peakflow is attenuated. Model application shows that the intensity of lateral outflow for the flood component is linked to upstream discharge. A combination of river loss and overbank flow for highest floods is proposed for explaining the relationships. Our approach differentiates the role of outflow (river loss and overbank flow) and that of wave diffusion on peakflow attenuation. Based on several sets of model parameterization, diffusion is the main attenuation process for most cases, despite high river losses of up to several m3/s (half of peakflow for some parameterization strategies). Finally, this framework gives new insight into the SW/GW interactions during floods in karst basins, and more globally in basins characterized by disconnected river‐aquifer systems.  相似文献   

6.
The spatial representativeness of gauging stations was investigated in two low‐mountainous river basins near the city of Trier, southwest Germany. Longitudinal profiles during low and high flow conditions were sampled in order to identify sources of solutes and to characterize the alteration of flood wave properties during its travel downstream. Numerous hydrographs and chemographs of natural flood events were analysed in detail. Additionally, artificial flood events were investigated to study in‐channel transport processes. During dry weather conditions the gauging station was only representative for a short river segment upstream, owing to discharge and solute concentrations of sources contiguous to the measurement site. During artificial flood events the kinematic wave velocity was considerably faster than the movement of water body and solutes, refuting the idea of a simple mixing process of individual runoff components. Depending on hydrological boundary conditions, the wave at a specific gauge could be entirely composed of old in‐channel water, which notably reduces the spatial representativeness of a sampling site. Natural flood events were characterized by a superimposition of local overland flow, riparian water and the kinematic wave process comprising the downstream conveyance of solutes. Summer floods in particular were marked by a chronological occurrence of distinct individual runoff components originating only from a few contributing areas adjacent to the stream and gauge. Thus, the representativeness of a gauge for processes in the whole basin depends on the distance of the nearest significant source to the station. The consequence of our study is that the assumptions of mixing models are not satisfied in river basins larger than 3 km2. Copyright © 2006 John Wiley & Sons, Ltd.  相似文献   

7.
A Bayesian Geostatistical Approach to evaluate unknown upstream flow hydrographs in multiple reach systems is implemented. The methodology was, firstly, tested through three synthetic examples of river confluences, that differ in the available data, boundary conditions and number of the estimated inflow time series. Input discharge hydrographs were routed downstream by means of the widely known HEC-RAS river analysis system to obtain the downstream stage hydrographs used as known observations for the reverse procedure. In almost all cases, the observed water levels were corrupted with random errors to highlight the reliability of the methodology in preventing instabilities and overfitting. Then the procedure was applied to the real case study of the Parma–Baganza river confluence located at the city of Parma (Italy) to assess the tributary Baganza River inflow hydrograph (supposed completely ungauged) using water level data collected downstream on the main reach. The results show that the methodology properly reproduces the unknown inflows even in presence of errors affecting the downstream water levels. The practical applicability of the proposed approach is also demonstrated in complex river systems.  相似文献   

8.
Eight data-driven models and five data pre-processing methods were summarized; the multiple linear regression (MLR), artificial neural network (ANN) and wavelet decomposition (WD) models were then used in short-term streamflow forecasting at four stations in the East River basin, China. The wavelet–artificial neural network (W-ANN) method was used to predict 1-month-ahead monthly streamflow at Longchuan station (LS). The results indicate better performance of MLR and wavelet–multiple linear regression (W-MLR) in analysing the stationary trained dataset. Four models showed similar performance in 1-day-ahead streamflow forecasting, while W-MLR and W-ANN performed better in 5-day-ahead forecasting. Three reservoirs were shown to have more influence on downstream than upstream streamflow and models had the worst performance at Boluo station. Furthermore, the W-ANN model performed well for 1-month-ahead streamflow forecasting at LS with consideration of a deterministic component.  相似文献   

9.
Z. X. Xu  J. Y. Li 《水文研究》2002,16(12):2423-2439
The primary objective of this study is to investigate the possibility of including more temporal and spatial information on short‐term inflow forecasting, which is not easily attained in the traditional time‐series models or conceptual hydrological models. In order to achieve this objective, an artificial neural network (ANN) model for short‐term inflow forecasting is developed and several issues associated with the use of an ANN model are examined in this study. The formulated ANN model is used to forecast 1‐ to 7‐h ahead inflows into a hydropower reservoir. The root‐mean‐squared error (RMSE), the Nash–Sutcliffe coefficient (NSC), the A information criterion (AIC), B information criterion (BIC) of the 1‐ to 7‐h ahead forecasts, and the cross‐correlation coefficient between the forecast and observed inflows are estimated. Model performance is analysed and some quantitative analysis is presented. The results obtained are satisfactory. Perceived strengths of the ANN model are the capability for representing complex and non‐linear relationships as well as being able to include more information in the model easily. Although the results obtained may not be universal, they are expected to reveal some possible problems in ANN models and provide some helpful insights in the development and application of ANN models in the field of hydrology and water resources. Copyright © 2002 John Wiley & Sons, Ltd.  相似文献   

10.
Synchronously and accurately estimating the flood discharges and dynamic changes in the fluid density is essential for hydraulic analysis and forecasting of flash floods, as well as for risk assessment. However, such information is rare for steep mountain catchments, especially in regions that are hotspots for earthquakes. Therefore, six hydrological monitoring sites were established in the main stream and tributaries of the 78.3‐km2 Longxi River catchment, an affected region of the Wenchuan earthquake region in China. Direct real‐time monitoring equipment was installed to measure the flow depths, velocities, and fluid total pressures of the flood hydrographs. On the basis of field measurements, real‐time mean cross‐sectional velocities during the flood hydrographs could be derived from easily obtainable parameters: cross‐sectional maximum velocities and the calibrated dimensionless parameter Kh . Real‐time discharges were determined on the basis of a noncontact method to establish the effective rating curves of this mountainous stream, ranging from 1.46 to 386.34 m3/s with the root mean square errors of ≤10.22 m3/s. Compared with the traditional point‐velocity method and empirical Manning's formula, the proposed noncontact method was reliable and safe for monitoring whole flood hydrographs. Additionally, the real‐time fluid density during the flood hydrographs was calculated on the basis of the direct monitoring parameters for fluid total pressures and water depths. During the flood hydrograph, transient flow behaviour with higher fluid density generally occurred downstream during the flood peak periods when the flow was in the supercritical flow regime. The observed behaviour greatly increased the threat of damage to infrastructure and human life near the river. Thus, it is important to accurately estimate flood discharge and identify for fluid densities so that people at risk from an impending flash flood are given reliable, advanced warning.  相似文献   

11.
Forecasting river flow is important to water resources management and planning. In this study, an artificial neural network (ANN) model was successfully developed to forecast river flow in Apalachicola River. The model used a feed‐forward, back‐propagation network structure with an optimized conjugated training algorithm. Using long‐term observations of rainfall and river flow during 1939–2000, the ANN model was satisfactorily trained and verified. Model predictions of river flow match well with the observations. The correlation coefficients between forecasting and observation for daily, monthly, quarterly and yearly flow forecasting are 0·98, 0·95, 0·91 and 0·83, respectively. Results of the forecasted flow rates from the ANN model were compared with those from a traditional autoregressive integrated moving average (ARIMA) forecasting model. Results indicate that the ANN model provides better accuracy in forecasting river flow than does the ARIMA model. Copyright © 2004 John Wiley & Sons, Ltd.  相似文献   

12.
Jan F. Adamowski 《水文研究》2008,22(25):4877-4891
In this study, short‐term river flood forecasting models based on wavelet and cross‐wavelet constituent components were developed and evaluated for forecasting daily stream flows with lead times equal to 1, 3, and 7 days. These wavelet and cross‐wavelet models were compared with artificial neural network models and simple perseverance models. This was done using data from the Skrwa Prawa River watershed in Poland. Numerical analysis was performed on daily maximum stream flow data from the Parzen station and on meteorological data from the Plock weather station in Poland. Data from 1951 to 1979 was used to train the models while data from 1980 to 1983 was used to test the models. The study showed that forecasting models based on wavelet and cross‐wavelet constituent components can be used with great accuracy as a stand‐alone forecasting method for 1 and 3 days lead time river flood forecasting, assuming that there are no significant trends in the amplitude for the same Julian day year‐to‐year, and that there is a relatively stable phase shift between the flow and meteorological time series. It was also shown that forecasting models based on wavelet and cross‐wavelet constituent components for forecasting river floods are not accurate for longer lead time forecasting such as 7 days, with the artificial neural network models providing more accurate results. Copyright © 2008 John Wiley & Sons, Ltd.  相似文献   

13.
《国际泥沙研究》2020,35(1):97-104
The flood season is the main period of flow,sediment transport,and sedimentation in the lower Yellow River(LYR).Within the flood season,most of the flow,sediment transport,and sedimentation occurs during flood events.Because of the importance of floods in forming riverbeds in the LYR,the regularity of sediment transport and sedimentation during floods in the LYR was studied.Measured daily discharge and sediment transport rate data for the LYR from 1960 to 2006 were used.A total of 299 floods were selected;these floods had a complete evolution of the flood process from the Xiaolangdi to the Lijin hydrological stations.For five hydrological stations(Xiaolangdi,Huayuankou,Gaocun,Aishan,and Lijin),a correlation was first established for floods of different magnitudes between the average sediment transport rate at a given station and the average sediment concentration at the closest upstream station.The results showed that the sediment transport rate at the downstream station was strongly correlated with the inflow(upstream station) sediment concentration during a flood event.A relation then was established between sedimentation in the LYR and the average sediment concentration at the Xiaolangdi station during a flood event.From this relation,the critical sediment concentrations were obtained for absolute erosion,sedimentation equilibrium,and absolute deposition during floods of different magnitudes in the LYR.The results of the current study contri b ute to a better understanding of the mechanisms of sediment transport and the regularity of sedimentation in the LYR during floods,and provide technical support to guide the joint operation of reservoirs and the regulation of the LYR.  相似文献   

14.
Due to natural heterogeneity in runoff processes, the analysis of response of stream channels to the variation of lateral inflow is therefore viewed in terms of stochastic spatiotemporal processes. Based on the representation theorem, a closed-form expression is derived to describe the spectral response characteristic of stream subject to spatiotemporal fluctuations in lateral inflow. It provides a basis for evaluating the induced discharge variability in stream channels. It is found that the evolutionary power spectrum of the stream flow discharge process and therefore the variance is increased with the distance from the upstream boundary and the characteristic length scale of the lateral inflow process. Flow discharge prediction in the downstream region has a high degree of uncertainty by solving the deterministic partial differential equation.  相似文献   

15.
Jan F. Adamowski   《Journal of Hydrology》2008,353(3-4):247-266
In this study, a new method of stand-alone short-term spring snowmelt river flood forecasting was developed based on wavelet and cross-wavelet analysis. Wavelet and cross-wavelet analysis were used to decompose flow and meteorological time series data and to develop wavelet based constituent components which were then used to forecast floods 1, 2, and 6 days ahead. The newly developed wavelet forecasting method (WT) was compared to multiple linear regression analysis (MLR), autoregressive integrated moving average analysis (ARIMA), and artificial neural network analysis (ANN) for forecasting daily stream flows with lead-times equal to 1, 2, and 6 days. This comparison was done using data from the Rideau River watershed in Ontario, Canada. Numerical analysis was performed on daily maximum stream flow data from the Rideau River station and on meteorological data (rainfall, snowfall, and snow on ground) from the Ottawa Airport weather station. Data from 1970 to 1997 were used to train the models while data from 1998 to 2001 were used to test the models. The most significant finding of this research was that it was demonstrated that the proposed wavelet based forecasting method can be used with great accuracy as a stand-alone forecasting method for 1 and 2 days lead-time river flood forecasting, assuming that there are no significant trends in the amplitude for the same Julian day year-to-year, and that there is a relatively stable phase shift between the flow and meteorological time series. The best forecasting model for 1 day lead-time was a wavelet analysis model. In testing, it had the lowest RMSE value (13.8229), the highest R2 value (0.9753), and the highest EI value (0.9744). The best forecasting model for 2 days lead-time was also a wavelet analysis model. In testing, it had the lowest RMSE value (31.7985), the highest R2 value (0.8461), and the second highest EI value (0.8410). It was also shown that the proposed wavelet based forecasting method is not particularly accurate for longer lead-time forecasting such as 6 days, with the ANN method providing more accurate results. The best forecasting model for 6 days lead-time was an ANN model, with the wavelet model not performing as well. In testing, the wavelet model had an RMSE of 57.6917, an R2 of 0.4835, and an EI of 0.4366.  相似文献   

16.
The optimal operation of dam reservoirs can be programmed and managed by predicting the inflow to these structures more accurately. To this end, there are various linear and nonlinear models. However, some hydrological problems like inflow with extreme seasonal variation are not purely linear or nonlinear. To improve the forecasting accuracy of this phenomenon, a linear Seasonal Auto Regressive Integrated Moving Average (SARIMA) model is combined with a nonlinear Artificial Neural Network (ANN) model. This new model is used to predict the monthly inflow to the Jamishan dam reservoir in West Iran. A comparison of the SARIMA and ANN models with the proposed hybrid model’s results is provided accordingly. More specifically, the models’ performance in forecasting base and flood flows is evaluated. The effect of changing the forecasting period length on the models’ accuracy is studied. The results of increasing the number of SARIMA model parameters up to five are investigated to achieve more accurate forecasting. The hybrid model predicts peak flood flows much better than the individual models, but SARIMA outperforms the other models in predicting base flow. The obtained results indicate that the hybrid model reduces the overall forecast error more than the ANN and SARIMA models. The coefficient of determination of the hybrid, ANN and SARIMA models were 0.72, 0.64 and 0.58, and the root mean squared error values were 1.02, 1.16 and 1.27 respectively, during the forecast period. Changing the forecasting length also indicated that these models can be used in the long term without increasing the forecast error.  相似文献   

17.
IINTRODUCTIONIntherecentdecadesfrequentflooddisasterscausedseriousdamagesandclaimedthousandsoflives,suchasthe1998floodintheYangtzeRiverandthe1996floodintheYellowRiver.The1998floodintheYangtzeandtheSonghuaRiversbroughtdirectlossesofmorethan$30billions.Lowdischargehighstageisthemaincharacterofthefloods.Forexample,thehighestfloodstagein1998wasI.sinhigheranddischargewas14000m3/slowerthanthosein1954atLuoshanStationoftheVangtzeRiver.Anewmodelisrequiredtobedevelopedforaccuratepredictionoffl…  相似文献   

18.
Although artificial neural networks (ANNs) have been applied in rainfall runoff modelling for many years, there are still many important issues unsolved that have prevented this powerful non‐linear tool from wide applications in operational flood forecasting activities. This paper describes three ANN configurations and it is found that a dedicated ANN for each lead‐time step has the best performance and a multiple output form has the worst result. The most popular form with multiple inputs and single output has the average performance. In comparison with a linear transfer function (TF) model, it is found that ANN models are uncompetitive against the TF model in short‐range predictions and should not be used in operational flood forecasting owing to their complicated calibration process. For longer range predictions, ANN models have an improved chance to perform better than the TF model; however, this is highly dependent on the training data arrangement and there are undesirable uncertainties involved, as demonstrated by bootstrap analysis in the study. To tackle the uncertainty issue, two novel approaches are proposed: distance analysis and response analysis. Instead of discarding the training data after the model's calibration, the data should be retained as an integral part of the model during its prediction stage and the uncertainty for each prediction could be judged in real time by measuring the distances against the training data. The response analysis is based on an extension of the traditional unit hydrograph concept and has a very useful potential to reveal the hydrological characteristics of ANN models, hence improving user confidence in using them in real time. Copyright © 2006 John Wiley & Sons, Ltd.  相似文献   

19.
Abstract

The rating curve model (RCM) proposed by Moramarco and co-authors is modified here for flood forecasting purposes without using rainfall information. The RCM is a simple approach for discharge assessment at a river site of interest based on relating the local recorded stage and the remote discharge monitored at an upstream gauged river site located some distance away. The proposed RCM for real-time application (RCM-RT), involves only two parameters and can be used for river reaches where significant lateral flows occur. The forecast lead time depends on the mean wave travel time of the reach. The model is found to be accurate for a long reach of the Po River (northern Italy) and for two branches of the Tiber River (central Italy) characterized by different intermediate drainage areas and wave travel times. Moreover, the assessment of the forecast uncertainty coming from the model parameters is investigated by performing a Monte Carlo simulation. Finally, the model capability to accurately forecast the exceedence of fixed hydrometric thresholds is analysed.

Editor D. Koutsoyiannis; Associate editor C. Perrin  相似文献   

20.
The cascading failure of multiple landslide dams can trigger a larger peak flood discharge than that caused by a single dam failure.Therefore,for an accurate numerical simulation,it is essential to elucidate the primary factors affecting the peak discharge of the flood caused by a cascading failure,which is the purpose of the current study.First,flume experiments were done on the cascading failure of two landslide dams under different upstream dam heights,downstream dam heights,and initial downstream reservoir water volumes.Then,the experimental results were reproduced using a numerical simulation model representing landslide dam erosion resulting from overtopping flow.Finally,the factors influencing the peak flood discharge caused by the cascading failure were analyzed using the numerical simulation model.Experimental results indicated that the inflow discharge into the downstream dam at the time when the downstream dam height began to rapidly erode was the main factor responsible for a cascading failure generating a larger peak flood discharge than that generated by a single dam failure.Furthermore,the results of a sensitivity analysis suggested that the upstream and downstream dam heights,initial water volume in the reservoir of the downstream dam,upstream and downstream dam crest lengths,and distance between two dams were among the most important factors in predicting the flood discharge caused by the cascading failure of multiple landslide dams.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号