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1.
《水文科学杂志》2013,58(3):582-595
Abstract This paper explores the potential for seasonal prediction of hydrological variables that are potentially useful for reservoir operation of the Three Gorges Dam, China. The seasonal flow of the primary inflow season and the peak annual flow are investigated at Yichang hydrological station, a proxy for inflows to the Three Gorges Dam. Building on literature and diagnostic results, a prediction model is constructed using sea-surface temperatures and upland snow cover available one season ahead of the prediction period. A hierarchical Bayesian approach is used to estimate uncertainty in the parameters of the prediction model and to propagate these uncertainties to the predictand. The results show skill for both the seasonal flow and the peak annual flow. The peak annual flow model is then used to estimate a design flood (50-year flood or 2% exceedence probability) on a year-to-year basis. The results demonstrate the inter-annual variability in flood risk. The predictability of both the seasonal total inflow and the peak annual flow (or a design flood volume) offers potential for adaptive management of the Three Gorges Dam reservoir through modification of the operating policy in accordance with the year-to-year changes in these variables. 相似文献
2.
《水文科学杂志》2013,58(6)
Abstract Abstract Reservoirs play a vital role in flood prevention and disaster relief in China. The objectives of the project described in this study were to establish a reservoir flood forecasting and control system and to design and develop corresponding application software. This paper introduces the current reservoir flood control and operation practice with this system in China. Using modern integration technologies, an application software for this Reservoir Flood Forecasting and Control System (RFFCS) has been developed and updated since 1995. The structure of the system and its main functions, telemetric data acquisition and processing, the hydrological database, flood forecasting, and reservoir operation components are described in detail. The working environment, key technologies and standardization design are emphasized. Having been successfully applied to 212 reservoirs in China, the software has proved to be reliable and user-friendly. In its latest version, the software supports reservoir flood forecasting and flood dispatch decisions. The future research direction and the extension of the software function are also discussed. 相似文献
3.
Estimation of reservoir flood control operation risks with considering inflow forecasting errors 总被引：2，自引：1，他引：1
Baowei Yan Shenglian Guo Lu Chen 《Stochastic Environmental Research and Risk Assessment (SERRA)》2014,28(2):359-368
A method for quantifying inflow forecasting errors and their impact on reservoir flood control operations is proposed. This approach requires the identification of the probability distributions and uncertainty transfer scheme for the inflow forecasting errors. Accordingly, the probability distributions of the errors are inferred through deducing the relationship between its standard deviation and the forecasting accuracy quantified by the Nash–Sutcliffe efficiency coefficient. The traditional deterministic flood routing process is treated as a diffusion stochastic process. The diffusion coefficient is related to the forecasting accuracy, through which the forecasting errors are indirectly related to the sources of reservoir operation risks. The associated risks are derived by solving the stochastic differential equation of reservoir flood routing via the forward Euler method. The Geheyan reservoir in China is selected as a case study. The hydrological forecasting model for this basin is established and verified. The flood control operation risks in the forecast-based pre-release operation mode for different forecasting accuracies are estimated by the proposed approach. Application results show that the proposed method can provide a useful tool for reservoir operation risk estimation and management. 相似文献
4.
Abstract Seasonal design floods which consider information on seasonal variation are very important for reservoir operation and management. The seasonal design flood method currently used in China is based on seasonal maximum (SM) samples and assumes that the seasonal design frequency is equal to the annual design frequency. Since the return period associated with annual maximum floods is taken as the standard in China, the current seasonal design flood cannot satisfy flood prevention standards. A new seasonal design flood method, which considers dates of flood occurrence and magnitudes of the peaks (runoff), was proposed and established based on copula function. The mixed von Mises distribution was selected as marginal distribution of flood occurrence dates. The Pearson Type III and exponential distributions were selected as the marginal distribution of flood magnitude for annual maximum flood series and peak-over-threshold samples, respectively. The proposed method was applied at the Geheyan Reservoir, China, and then compared with the currently used seasonal design flood methods. The case study results show that the proposed method can satisfy the flood prevention standard, and provide more information about the flood occurrence probabilities in each sub-season. The results of economic analysis show that the proposed design flood method can enhance the floodwater utilization rate and give economic benefits without lowering the annual flood protection standard. Citation Chen, L., Guo, S. L., Yan, B. W., Liu, P. & Fang, B. (2010) A new seasonal design flood method based on bivariate joint distribution of flood magnitude and date of occurrence. Hydrol. Sci. J. 55(8), 1264–1280. 相似文献
5.
Abstract In a typical reservoir routing problem, the givens are the inflow hydrograph and reservoir characteristic functions. Flood attenuation investigations can be easily accomplished using a hydrological or hydraulic routing of the inflow hydrograph to obtain the reservoir outflow hydrograph, unless the inflow hydrograph is unavailable. Although attempts for runoff simulation have been made in ungauged basins, there is only a limited degree of success in special cases. Those approaches are, in general, not suitable for basins with a reservoir. The objective of this study is to propose a procedure for flood attenuation estimation in ungauged reservoir basins. In this study, a kinematic-wave based geomorphic IUH model was adopted. The reservoir inflow hydrograph was generated through convolution integration using the rainfall excess and basin geomorphic information. Consequently, a fourth-order Runge-Kutta method was used to route the inflow hydrograph to obtain the reservoir outflow hydrograph without the aid of recorded flow data. Flood attenuation was estimated through the analysis of the inflow and outflow hydrographs of the reservoir. An ungauged reservoir basin in southern Taiwan is presented as an example to show the applicability of the proposed analytical procedure. The analytical results provide valuable information for downstream flood control work for different return periods. 相似文献
6.
《水文科学杂志》2013,58(5):974-991
Abstract The aim is to build a seasonal flood frequency analysis model and estimate seasonal design floods. The importance of seasonal flood frequency analysis and the advantages of considering seasonal design floods in the derivation of reservoir planning and operating rules are discussed, recognising that seasonal flood frequency models have been in use for over 30 years. A set of non-identical models with non-constant parameters is proposed and developed to describe flows that reflect seasonal flood variation. The peak-over-threshold (POT) sampling method was used, as it is considered to provide significantly more information on flood seasonality than annual maximum (AM) sampling and has better performance in flood seasonality estimation. The number of exceedences is assumed to follow the Poisson distribution (Po), while the peak exceedences are described by the exponential (Ex) and generalized Pareto (GP) distributions and a combination of both, resulting in three models, viz. Po-Ex, Po-GP and Po-Ex/GP. Their performances are analysed and compared. The Geheyan and the Baiyunshan reservoirs were chosen for the case study. The application and statistical experiment results show that each model has its merits and that the Po-Ex/GP model performs best. Use of the Po-Ex/GP model is recommended in seasonal flood frequency analysis for the purpose of deriving reservoir operation rules. 相似文献
7.
Identifying flood seasonality is critical in hydrologic applications as well as water resources management. We develop an entropy-based method (EBM) for identifying flood seasonality and partitioning the entire flood season into multiple sub-seasons. The performance of the proposed EBM is evaluated using a Monte Carlo simulation test and compared with current methods. The Three Gorges Reservoir (TGR) basin in the Yangtze River is selected as a case study to test the applicability of the proposed method. Results of Monte Carlo simulation test demonstrate that the EBM performs better than the probability change-point method and the improved relative frequency method with less bias and higher efficiency. The case study results illustrate that the EBM can appropriately divide the entire flood season of the TGR into pre-flood season (from June 1st to June 20th), main-flood season (from June 21th to September 10th) and post-flood season (from September 11th to September 30th). The flood limited water levels (FLWL) in these three sub-seasons can then be derived, which are 150 m, 145 m and 149 m, respectively. Compared with conventional operation rule, the seasonal FLWL scheme can generate more hydropower (0.93 billion KWh) annually with a reliability of 99.86%. Therefore, it is meaningful to divide the entire flood season into three sub-seasons and apply seasonal FLWL for TGR operation. 相似文献
8.
IOANNIS NALBANTIS 《水文科学杂志》2013,58(2):269-284
Abstract A modelling scheme is developed for real-time flood forecasting. It is composed of (a) a rainfall forecasting model, (b) a conceptual rainfall-runoff model, and (c) a stochastic error model of the ARMA family for forecast error correction. Initialization of the rainfall-runoff model is based on running this model on a daily basis for a certain period prior to the flood onset while parameters of the error model are updated through the Recursive Least Squares algorithm. The scheme is suitable for the early stages of operation of flood forecasting systems in the presence of inadequate historical data. A validation framework is set up which simulates real-time flood forecasting conditions. Thus, the effects of the procedures for rainfall-runoff model initialization, forecast error correction and rainfall forecasting are assessed. Two well-known conceptual rainfall-runoff models (the Soil Moisture Accounting model of the US National Weather Service River Forecast Service—SMA-NWSRFS and TANK) together with data from a Greek basin are used for illustration purposes. 相似文献
9.
Quantifying differences between reservoir inflows and dam site floods using frequency and risk analysis methods 总被引：1，自引：1，他引：0
Reservoirs are the most important constructions for water resources management and flood control. Great concern has been paid to the effects of reservoir on downstream area and the differences between inflows and dam site floods due to the changes of upstream flow generation and concentration conditions after reservoir’s impoundment. These differences result in inconsistency between inflow quantiles and the reservoir design criteria derived by dam site flood series, which can be a potential risk and must be quantificationally evaluated. In this study, flood frequency analysis (FFA) and flood control risk analysis (FCRA) methods are used with the long reservoir inflow series derived from a multiple inputs and single output model and a copula-based inflow estimation model. The results of FFA and FCRA are compared and the influences on reservoir flood management are also discussed. The Three Gorges Reservoir (TGR) in China is selected as a case study. Results show that the differences between the TGR inflow and dam site floods are significant which result in changes on its flood control risk rates. The mean values of TGR’s annual maximum inflow peak discharge and 3 days flood volume have increased 5.58 and 3.85% than the dam site ones, while declined by 1.82 and 1.72% for the annual maximum 7 and 15 days flood volumes. The flood control risk rates of middle and small flood events are increased while extreme flood events are declined. It is shown that the TGR can satisfy the flood control task under current hydrologic regime and the results can offer references for better management of the TGR. 相似文献
10.
ABSTRACTAmong various strategies for sediment reduction, venting turbidity currents through dam outlets can be an efficient way to reduce suspended sediment deposition. The accuracy of turbidity current arrival time forecasts is crucial for the operation of reservoir desiltation. A turbidity current arrival time (TCAT) model is proposed. A multi-objective genetic algorithm (MOGA), a support vector machine (SVM) and a two-stage forecasting technique are integrated to obtain more effective long lead-time forecasts of inflow discharge and inflow sediment concentration. The multi-objective genetic algorithm (MOGA) is applied for determining the optimal inputs of the forecasting model, support vector machine (SVM). The two-stage forecasting technique is implemented by adding the forecasted values to candidate inputs for improving the long lead-time forecasting. Then, the turbidity current arrival time from the inflow boundary to the reservoir outlet is calculated. To demonstrate the effectiveness of the TCAT model, it is applied to Shihmen Reservoir in northern Taiwan. The results confirm that the TCAT model forecasts are in good agreement with the observed data. The proposed TCAT model can provide useful information for reservoir sedimentation management during desilting operations. 相似文献
11.
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
12.
ABSTRACT In order to provide more accurate reservoir-operating policies, this study attempts to implement effective monthly forecasting models. Seven inflow forecasting schemes, applying discrete wavelet transformation and artificial neural networks are proposed and provided to forecast the monthly inflow of Dez Reservoir. Based on some different performance indicators the best scheme is achieved comparing to the observed data. The best forecasting model is coupled with a simulation-optimization framework, in which the performance of five different reservoir rule curves can be compared. Three applied rules are based on conventional Standard operation policy, Regression rules, and Hedging rule, and two others are forecasting-based regression and hedging rules. The results indicate that forecasting-based operating rule curves are superior to the conventional rules if the forecasting scheme provides results accurately. Moreover, it can be concluded that the time series decomposition of the observed data enhances the accuracy of the forecasting results efficiently. 相似文献
13.
准确、及时的入库洪水预报,对三峡水库综合效益的发挥和长江流域水旱灾害防御、水资源利用、流域综合管理等具有重要作用。基于预报误差的最优分布估计和分布函数动态参数假定,提出了一种三峡水库入库洪水概率预报方法,并进行了洪水概率预报业务试验。结果表明:本文所提方法科学可行,计算快捷,使用方便,便于在实时作业预报中应用推广;概率预报结果较确定性预报结果,在水量预报、预警效果等方面均有所改善,1～5 d预见期预报的确定性系数提高0.1%～3.4%,水量误差减少0.1%～4.8%,可为三峡水库实时调度提供更可靠的预报信息;所提出的三峡水库入库洪水概率预报业务化产品,可提供更多风险信息,为三峡水库的科学调度,尤其是洪水资源化利用提供更好的优化决策支撑。 相似文献
14.
ABSTRACTThe potential of the most recent pre-processing tool, namely, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), is examined for providing AI models (artificial neural network, ANN; M5-model tree, M5-MT; and multivariate adaptive regression spline, MARS) with more informative input–output data and, thence, evaluate their forecasting accuracy. A 130-year inflow dataset for Aswan High Dam, Egypt, is considered for training, validating and testing the proposed models to forecast the reservoir inflow up to six months ahead. The results show that, after the pre-processing analysis, there is a significant enhancement in the forecasting accuracy. The MARS model combined with CEEMDAN gave superior performance compared to the other models – CEEMDAN-ANN and CEEMDAN-M5-MT – with an increase in accuracy of, respectively, about 13–25% and 6–20% in terms of the root mean square error. 相似文献
15.
Abstract The effect of data pre-processing while developing artificial intelligence (AI) -based data-driven techniques, such as artificial neural networks (ANN), model trees (MT) and linear genetic programming (LGP), is studied for Pawana Reservoir in Maharashtra, India. The daily one-step-ahead inflow forecasts are compared with flows generated from a univariate autoregressive integrated moving average (ARIMA) model. For the full-year data series, a large error is found mainly due to the occurrence of zero values, since the reservoir is located in an intermittent river. Hence, all the techniques are evaluated using two data series: 18 years of daily full-year inflow data (from 1 January to 31 December); and 18 years of daily monsoon season inflow data (from 1 June to 31 October) to take into account the intermittent nature of the data. The relevant range of inputs for each category is selected based on autocorrelation and partial autocorrelation analyses of the inflow series. Conventional pre-processing methods, such as transformation and/or normalization of data, do not perform well because of the large variation in magnitudes, as well as the many zero values (65% of the full-year data series). Therefore, the input data are pre-processed into un-weighted moving average (MA) series of 3 days, 5 days and 7 days. The 3-day MA series performs better, maintaining the peak inflow pattern as in the actual data series, while the coarser-scale (5-day and 7-day) MA series reduce the peak inflow pattern, leading to more errors in peak inflow prediction. The results indicate that AI methods are powerful tools for modelling the daily flow time series with appropriate data pre-processing, in spite of the presence of many zero values. The time-lagged recurrent network (TLRN) ANN modelling technique applied in this study maps the inflow forecasting in a better way than the standard multilayer perceptron (MLP) neural networks, especially in the case of the seasonal data series. The MT technique performs equally well for low and medium inflows, but fails to predict the peak inflows. However, LGP outperforms the other AI models, and also the ARIMA model, for all inflow magnitudes. In the LGP model, the daily full-year data series with more zero inflow values performs better than the daily seasonal models. Citation Jothiprakash, V. & Kote, A. S. (2011) Improving the performance of data-driven techniques through data pre-processing for modelling daily reservoir inflow. Hydrol. Sci. J. 56(1), 168–186. 相似文献
16.
Abstract A real-time operational methodology has been developed for multipurpose reservoir operation for irrigation and hydropower generation with application to the Bhadra reservoir system in the state of Karnataka, India. The methodology consists of three phases of computer modelling. In the first phase, the optimal release policy for a given initial storage and inflow is determined using a stochastic dynamic programming (SDP) model. Streamflow forecasting using an adaptive AutoRegressive Integrated Moving Average (ARIMA) model constitutes the second phase. A real-time simulation model is developed in the third phase using the forecast inflows of phase 2 and the operating policy of phase 1. A comparison of the optimal monthly real-time operation with the historical operation demonstrates the relevance, applicability and the relative advantage of the proposed methodology. 相似文献
17.
《水文科学杂志》2013,58(2)
Abstract An updating technique is a tool to update the forecasts of mathematical flood forecasting model based on data observed in real time, and is an important element in a flood forecasting model. An error prediction model based on a fuzzy rule-based method was proposed as the updating technique in this work to improve one- to four-hour-ahead flood forecasts by a model that is composed of the grey rainfall model, the grey rainfall—runoff model and the modified Muskingum flow routing model. The coefficient of efficiency with respect to a benchmark is applied to test the applicability of the proposed fuzzy rule-based method. The analysis reveals that the fuzzy rule-based method can improve flood forecasts one to four hours ahead. The proposed updating technique can mitigate the problem of the phase lag in forecast hydrographs, and especially in forecast hydrographs with longer lead times. 相似文献
18.
Suspended matter is an important indicator of water quality in freshwater systems. The flood‐induced turbidity current plays a dominant role in the seasonal dynamic of suspended matter in the Liuxihe Reservoir (23°45′50″N; 113°46′52″E), a large, stratified reservoir at the Tropic of Cancer in southern China. Field measurements show that loading and distribution of suspended matter in the reservoir differ in typical wet, dry and medium years, as a result of different discharge volumes and water level variation patterns. Using historical data and the practical demand for water supply and flood control, we generalized two feasible reservoir operational modes: flood impounding mode (drawing down the reservoir to a low level before flood events to impound inflow during the flooding season) and moderate level change mode (drawing down the reservoir to a moderate level before flood events, then keeping the level within the flood control level during runoff events). To examine the effects of different operational modes and outlet depths on the reservoir's flood‐induced turbidity current, a numerical simulation model was applied in three types of hydrological conditions. The results show that the mode with moderate drawdown and recharge processes can decrease loading of suspended matter in spring and promote turbidity current release during flood events, and upper withdrawal can improve the effects of turbid water release. We suggest that more attention should be focused on water quality management in the reservoir operation stage, severe artificial water level fluctuation being avoided and selective withdrawal becoming an optional management measure. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
19.
Jungho Kim Laura Read Lynn E. Johnson David Gochis Rob Cifelli Heechan Han 《水文科学杂志》2020,65(10):1652-1666
ABSTRACT This study experiments with reservoir representation schemes to improve the ability to model active water management in the National Water Model (NWM). For this purpose, we developed an integrated water management model, NWM-ResSim, by coupling the NWM with HEC-ResSim, and two reservoir representation schemes are tested: simulation of reservoir operations and retrieval of scheduled operations. The experiments focus on a pilot reservoir domain in the Russian River basin – Lake Mendocino, California – and its contributing watershed. The evaluation results suggest that the NWM-ResSim improves the simulation performance of reservoir outflow from this managed reservoir over the NWM default level pool routing scheme. The degree of this improvement depends on the suitability of the operation guidance; the reservoir operations simulation scheme could have acceptable errors for the purposes of water resources management, but not for flood operations. Results of the retrieval scheme of scheduled operations demonstrated better performance for sub-daily flood operations. 相似文献
20.
《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