共查询到20条相似文献，搜索用时 15 毫秒
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《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
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空间集总式水文模型的洪水预报精度会受到面平均雨量估计误差的严重影响.点雨量监测值的误差类型、误差大小以及流域的雨量站点密度和站点的空间分布都会影响到面平均雨量的计算.为提高实时洪水预报精度,本文提出了一种基于降雨系统响应曲线洪水预报误差修正方法.通过此方法估计降雨输入项的误差,从而提高洪水预报精度.此方法将水文模型做为输入和输出之间的响应系统,用实测流量和计算流量之间的差值做为信息,通过降雨系统响应曲线,使用最小二乘估计原理,对面平均雨量进行修正,再用修正后的面平均雨量重新计算出流过程.将此修正方法结合新安江模型使用理想案例进行检验,并应用于王家坝流域的16场历史洪水以及此流域不同雨量站密度的情况下,结果证明均有明显修正效果,且在雨量站密度较低时修正效果更加明显.该方法是一种结构简单且不增加模型参数和复杂度的实时洪水修正的新方法. 相似文献
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A review of advances in flash flood forecasting 总被引：1，自引：0，他引：1
Flash flooding is one of the most hazardous natural events, and it is frequently responsible for loss of life and severe damage to infrastructure and the environment. Research into the use of new modelling techniques and data types in flash flood forecasting has increased over the past decade, and this paper presents a review of recent advances that have emerged from this research. In particular, we focus on the use of quantitative precipitation estimates and forecasts, the use of remotely sensed data in hydrological modelling, developments in forecasting models and techniques, and uncertainty estimates. Over the past decade flash flood forecast lead‐time has expanded up to six hours due to improved rainfall forecasts. However the largest source of uncertainty of flash flood forecasts remains unknown future precipitation. An increased number of physically based hydrological models have been developed and used for flash flood forecasting and they have been found to give more plausible results when compared with the results of conceptual, statistical, and neural network models. Among the three methods for deciding flash flood occurrence discussed in this review, the rainfall comparison method (flash flood guidance) is most commonly used for flash flood forecasting as it is easily understood by the general public. Unfortunately, no existing model is capable of making reliable flash flood forecasts in urban watersheds even though the incidence of urban flash flooding is increasing due to increasing urbanisation. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
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Renata J. Romanowicz Peter C. Young Keith J. Beven Florian Pappenberger 《Advances in water resources》2008
Operational flood forecasting requires accurate forecasts with a suitable lead time, in order to be able to issue appropriate warnings and take appropriate emergency actions. Recent improvements in both flood plain characterization and computational capabilities have made the use of distributed flood inundation models more common. However, problems remain with the application of such models. There are still uncertainties associated with the identifiability of parameters; with the computational burden of calculating distributed estimates of predictive uncertainty; and with the adaptive use of such models for operational, real-time flood inundation forecasting. Moreover, the application of distributed models is complex, costly and requires high degrees of skill. This paper presents an alternative to distributed inundation models for real-time flood forecasting that provides fast and accurate, medium to short-term forecasts. The Data Based Mechanistic (DBM) methodology exploits a State Dependent Parameter (SDP) modelling approach to derive a nonlinear dependence between the water levels measured at gauging stations along the river. The transformation of water levels depends on the relative geometry of the channel cross-sections, without the need to apply rating curve transformations to the discharge. The relationship obtained is used to transform water levels as an input to a linear, on-line, real-time and adaptive stochastic DBM model. The approach provides an estimate of the prediction uncertainties, including allowing for heterescadasticity of the multi-step-ahead forecasting errors. The approach is illustrated using an 80 km reach of the River Severn, in the UK. 相似文献
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M. N. French R. L. Bras W. F. Krajewski 《Stochastic Environmental Research and Risk Assessment (SERRA)》1992,6(1):27-45
A procedure for short-term rainfall forecasting in real-time is developed and a study of the role of sampling on forecast ability is conducted. Ground level rainfall fields are forecasted using a stochastic space-time rainfall model in state-space form. Updating of the rainfall field in real-time is accomplished using a distributed parameter Kalman filter to optimally combine measurement information and forecast model estimates. The influence of sampling density on forecast accuracy is evaluated using a series of a simulated rainfall events generated with the same stochastic rainfall model. Sampling was conducted at five different network spatial densities. The results quantify the influence of sampling network density on real-time rainfall field forecasting. Statistical analyses of the rainfall field residuals illustrate improvement in one hour lead time forecasts at higher measurement densities. 相似文献
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复杂河道洪水预报系统研究——以淮河王家坝至小柳巷区间流域为例 总被引：2，自引：1，他引：1
随着社会经济的快速发展,洪水灾害造成的损失日益严重.洪水预报作为一项重要的防洪非工程措施,对防洪、抗洪工作起着至关重要的作用.淮河洪水危害的严重性和洪水演进过程的复杂性使得淮河洪水预报系统的研究长期以来受到高度重视.本文以王家坝至小柳巷区间流域为例,以河道洪水演算为主线,采用新安江三水源模型进行子流域降雨径流预报,概化具有行蓄洪区的干流河道,进行支流与干流、行蓄洪区与干流的洪水汇流耦合计算,采用实时更新的基于多元回归的方法确定水位流量关系,并以上游站点降雨径流预报模型提供的流量作为上边界条件、以下游站点的水位流量关系作为下边界条件,结合行蓄洪调度模型,建立具有行蓄洪区的河道洪水预报系统,再与基于K-最近邻(KNN)的非参数实时校正模型耦合,建立淮河中游河道洪水预报系统.采用多年资料模拟取得了较好的预报效果,并以2003和2007年大洪水为例进行检验,模拟结果精度较高,也证明了所建预报系统的合理性和适用性. 相似文献
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《水文科学杂志》2013,58(1):114-118
Abstract A reliable flood warning system depends on efficient and accurate forecasting technology. A systematic investigation of three common types of artificial neural networks (ANNs) for multi-step-ahead (MSA) flood forecasting is presented. The operating mechanisms and principles of the three types of MSA neural networks are explored: multi-input multi-output (MIMO), multi-input single-output (MISO) and serial-propagated structure. The most commonly used multi-layer feed-forward networks with conjugate gradient algorithm are adopted for application. Rainfall—runoff data sets from two watersheds in Taiwan are used separately to investigate the effectiveness and stability of the neural networks for MSA flood forecasting. The results indicate consistently that, even though the MIMO is the most common architecture presented in ANNs, it is less accurate because its multi-objectives (predicted many time steps) must be optimized simultaneously. Both MISO and serial-propagated neural networks are capable of performing accurate short-term (one- or two-step-ahead) forecasting. For long-term (more than two steps) forecasts, only the serial-propagated neural network could provide satisfactory results in both watersheds. The results suggest that the serial-propagated structure can help in improving the accuracy of MSA flood forecasts. 相似文献
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BMA集合预报在淮河流域应用及参数规律初探 总被引：1，自引：1，他引：0
以淮河流域吴家渡水文站作为试验站点,采用基于贝叶斯平均法(BMA)的集合预报模型处理来源于马斯京根法、一维水动力学方法、BPNN(Back Propagation Neural Network)的预报流量序列,通过分析BMA的参数以及其预报结果,对各方法在淮河典型站点流量预报中的适用性进行验证与分析.经2003—2016年19场洪水模拟检验可知,BMA模型能够有效避免模型选择带来的洪水预报误差放大效应,可以提供高精度、鲁棒性强的洪水预报结果.通过进一步比较各模型统计最优的频率与BMA权重值之间的相关性,发现权重值不适用于对单场洪水预报精度评定,而适用于描述多场洪水预报中,模型为最优的统计频率;基于大量先验信息,提前获取BMA的权重等参数,将是指导模型选择、降低洪水预报不确定性、改进洪水预报技术的有效手段. 相似文献
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The objective of the study is to evaluate the potential of a data assimilation system for real-time flash flood forecasting over small watersheds by updating model states. To this end, the Ensemble Square-Root-Filter (EnSRF) based on the Ensemble Kalman Filter (EnKF) technique was coupled to a widely used conceptual rainfall-runoff model called HyMOD. Two small watersheds susceptible to flash flooding from America and China were selected in this study. The modeling and observational errors were considered in the framework of data assimilation, followed by an ensemble size sensitivity experiment. Once the appropriate model error and ensemble size was determined, a simulation study focused on the performance of a data assimilation system, based on the correlation between streamflow observation and model states, was conducted. The EnSRF method was implemented within HyMOD and results for flash flood forecasting were analyzed, where the calibrated streamflow simulation without state updating was treated as the benchmark or nature run. Results for twenty-four flash-flood events in total from the two watersheds indicated that the data assimilation approach effectively improved the predictions of peak flows and the hydrographs in general. This study demonstrated the benefit and efficiency of implementing data assimilation into a hydrological model to improve flash flood forecasting over small, instrumented basins with potential application to real-time alert systems. 相似文献
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Maria Clara Fava Maurizio Mazzoleni Narumi Abe Eduardo Mario Mendiondo Dimitri P. Solomatine 《水文科学杂志》2020,65(7):1096-1111
ABSTRACTPoorly monitored catchments could pose a challenge in the provision of accurate flood predictions by hydrological models, especially in urbanized areas subject to heavy rainfall events. Data assimilation techniques have been widely used in hydraulic and hydrological models for model updating (typically updating model states) to provide a more reliable prediction. However, in the case of nonlinear systems, such procedures are quite complex and time-consuming, making them unsuitable for real-time forecasting. In this study, we present a data assimilation procedure, which corrects the uncertain inputs (rainfall), rather than states, of an urban catchment model by assimilating water-level data. Five rainfall correction methods are proposed and their effectiveness is explored under different scenarios for assimilating data from one or multiple sensors. The methodology is adopted in the city of São Carlos, Brazil. The results show a significant improvement in the simulation accuracy. 相似文献
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Abstract Application of the concept of combining the estimated forecast output of different rainfall-runoff models to yield an overall combined estimated output in the context of real-time river flow forecasting is explored. A Real-Time Model Output Combination Method (RTMOCM) is developed, based on the structure of the Linear Transfer Function Model (LTFM) and utilizing the concept of the Weighted Average Method (WAM) for model output combination. A multiple-input single-output form of the LTFM is utilized in the RTMOCM. This form of the LTFM model uses synchronously the daily simulation-mode model-estimated discharge time series of the rainfall-runoff models selected for combination, its inherent updating structure being used for providing updated combined discharge forecasts. The RTMOCM is applied to the daily data of five catchments, using the simulation-mode estimated discharges of three selected rainfall-runoff models, comprising one conceptual model (Soil Moisture Accounting and Routing Procedure—SMAR) and two black-box models (Linear Perturbation Model—LPM and Linearly-Varying Variable Gain Factor Model—LVGFM). In order to get an indication of the accuracy of the updated combined discharge forecasts relative to the updated discharge forecasts of the individual models, the LTFM is also used for updating the simulation-mode discharge time series of each of the three individual models. The results reveal that the updated combined discharge forecasts provided by the RTMOCM, with parameters obtained by linear regression, can improve on the updated discharge forecasts of the individual rainfall-runoff models. 相似文献
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Seung-Jae Lee Elizabeth A. Wentz Patricia Gober 《Stochastic Environmental Research and Risk Assessment (SERRA)》2010,24(2):283-295
Managing environmental and social systems in the face of uncertainty requires the best possible forecasts of future conditions.
We use space–time variability in historical data and projections of future population density to improve forecasting of residential
water demand in the City of Phoenix, Arizona. Our future water estimates are derived using the first and second order statistical
moments between a dependent variable, water use, and an independent variable, population density. The independent variable
is projected at future points, and remains uncertain. We use adjusted statistical moments that cover projection errors in
the independent variable, and propose a methodology to generate information-rich future estimates. These updated estimates
are processed in Bayesian Maximum Entropy (BME), which produces maps of estimated water use to the year 2030. Integrating
the uncertain estimates into the space–time forecasting process improves forecasting accuracy up to 43.9% over other space–time
mapping methods that do not assimilate the uncertain estimates. Further validation studies reveal that BME is more accurate
than co-kriging that integrates the error-free independent variable, but shows similar accuracy to kriging with measurement
error that processes the uncertain estimates. Our proposed forecasting method benefits from the uncertain estimates of the
future, provides up-to-date forecasts of water use, and can be adapted to other socio-economic and environmental applications. 相似文献
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针对降雨输入不确定性对实时洪水预报影响的问题,本文采用不考虑未来预报降雨、考虑未来预报降雨、考虑预报降雨的降雨量误差和降雨时间误差4种方法,以陕西省两个半湿润流域（陈河流域和大河坝流域）为研究区域,分析不同预见期和不同降雨输入情况下洪水预报的精度.研究表明：相对于不考虑未来降雨情况,考虑未来降雨后在预报预见期较长时对预报结果精度提升较大,在预见期较短时对预报结果精度提升不显著;暴雨中心位置不同对预报精度影响也不同,当暴雨中心位于流域下游时降雨量误差对流量预报误差影响更大;降雨量误差主要影响洪量相对误差和洪峰相对误差,且这种影响是线性的,对确定性系数的影响是非线性的二次函数,降雨时间误差主要影响峰现时间误差. 相似文献
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This study examines the short-range forecast accuracy of the Pennsylvania State University-National Center for Atmospheric
Research Mesoscale Model (MM5) as applied to the July 2006 episode of the Indian summer monsoon (ISM) and the model's sensitivity
to the choice of different cumulus parameterization schemes (CPSs), namely Betts-Miller, Grell (GR) and Kain-Fritsch (KF).
The results showed that MM5 day 1 (0–24 h prediction) and day 2 (24–48 h prediction) forecasts using all three CPSs overpredicted
monsoon rainfall over the Indian landmass, with the larger overprediction seen in the day 2 forecasts. Among the CPSs, the
rainfall distribution over the Indian landmass was better simulated in forecasts using the KF scheme. The KF scheme showed
better skill in predicting the area of rainfall for most of the rainfall thresholds. The root mean square error (RMSE) in
day 1 and day 2 rainfall forecasts using different CPSs showed that rainfall simulated using the KF scheme agreed better with
the observed rainfall. As compared to other CPSs, simulation using the GR scheme showed larger RMSE in wind speed prediction
at 850 and 200 hPa over the Indian landmass. MM5 24-h temperature forecasts at 850 hPa with all the CPSs showed a warm bias
of the order of 1 K over the Indian landmass and the bias doubled in 48-h model forecasts. The mean error in temperature prediction
at 850 hPa over the Indian region using the KF scheme was comparatively smaller for all the forecast intervals. The model
with all the CPSs overpredicted humidity at 850 hPa. The improved prediction by MM5 with the KF scheme is well complemented
by the smaller error shown by the KF scheme in vertical distribution of heat and mean moist static energy in the lower troposphere.
In this study, the KF scheme which explicitly resolve the downdrafts in the cloud column tended to produce more realistic
precipitation forecasts as compared to other schemes which did not explicitly incorporate downdraft effects. This is an important
result especially given that the area covered by monsoon-precipitating systems is largely from stratiform-type clouds which
are associated with strong downdrafts in the lower levels. This result is useful for improving the treatment of cumulus convection
in numerical models over the ISM region. 相似文献
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This study evaluates two (of the many) modelling approaches to flood forecasting for an upland catchment (the River South Tyne at Haydon Bridge, England). The first modelling approach utilizes ‘traditional’ hydrological models. It consists of a rainfall–runoff model (the probability distributed model, or PDM) for flow simulation in the upper catchment. Those flows are then routed to the lower catchment using two kinematic wave (KW) routing models. When run in forecast‐mode, the PDM and KW models utilize model updating procedures. The second modelling approach uses neural network models, which use a ‘pattern‐matching’ process to produce model forecasts.Following calibration, the models are evaluated in terms of their fit to continuous stage data and flood event magnitudes and timings within a validation period. Forecast times of 1 h, 2 h and 4 h are selected (the catchment has a response time of approximately 4 h). The ‘traditional’ models generally perform adequately at all three forecast times. The neural networks produce reasonable forecasts of small‐ to medium‐sized flood events but have difficulty in forecasting the magnitude of the larger flood events in the validation period. Possible modifications to the latter approach are discussed. © Crown copyright 2002. Reproduced with the permission of Her Majesty's stationery office. Published by John Wiley & Sons, Ltd. 相似文献
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The major purpose of this study is to effectively construct artificial neural networks‐based multistep ahead flood forecasting by using hydrometeorological and numerical weather prediction (NWP) information. To achieve this goal, we first compare three mean areal precipitation forecasts: radar/NWP multisource‐derived forecasts (Pr), NWP precipitation forecasts (Pn), and improved precipitation forecasts (Pm) by merging Pr and Pn. The analysis shows that the accuracy of Pm is higher than that of Pr and Pn. The analysis also indicates that the NWP precipitation forecasts do provide relative effectiveness to the merging procedure, particularly for forecast lead time of 4–6 h. In sum, the merged products performed well and captured the main tendency of rainfall pattern. Subsequently, a recurrent neural network (RNN)‐based multistep ahead flood forecasting techniques is produced by feeding in the merged precipitation. The evaluation of 1–6‐h flood forecasting schemes strongly shows that the proposed hydrological model provides accurate and stable flood forecasts in comparison with a conventional case, and significantly improves the peak flow forecasts and the time‐lag problem. An important finding is the hydrologic model responses which do not seem to be sensitive to precipitation predictions in lead times of 1–3 h, whereas the runoff forecasts are highly dependent on predicted precipitation information for longer lead times (4–6 h). Overall, the results demonstrate that accurate and consistent multistep ahead flood forecasting can be obtained by integrating predicted precipitation information into ANNs modelling. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献
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Two lumped conceptual hydrological models, namely tank and NAM and a neural network model are applied to flood forecasting in two river basins in Thailand, the Wichianburi on the Pasak River and the Tha Wang Pha on the Nan River using the flood forecasting procedure developed in this study. The tank and NAM models were calibrated and verified and found to give similar results. The results were found to improve significantly by coupling stochastic and deterministic models (tank and NAM) for updating forecast output. The neural network (NN) model was compared with the tank and NAM models. The NN model does not require knowledge of catchment characteristics and internal hydrological processes. The training process or calibration is relatively simple and less time consuming compared with the extensive calibration effort required by the tank and NAM models. The NN model gives good forecasts based on available rainfall, evaporation and runoff data. The black‐box nature of the NN model and the need for selecting parameters based on trial and error or rule‐of‐thumb, however, characterizes its inherent weakness. The performance of the three models was evaluated statistically. Copyright © 2000 John Wiley & Sons, Ltd. 相似文献
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Accurate water level forecasts are essential for flood warning. This study adopts a data‐driven approach based on the adaptive network–based fuzzy inference system (ANFIS) to forecast the daily water levels of the Lower Mekong River at Pakse, Lao People's Democratic Republic. ANFIS is a hybrid system combining fuzzy inference system and artificial neural networks. Five ANFIS models were developed to provide water level forecasts from 1 to 5 days ahead, respectively. The results show that although ANFIS forecasts of water levels up to three lead days satisfied the benchmark, four‐ and five‐lead‐day forecasts were only slightly better in performance compared with the currently adopted operational model. This limitation is imposed by the auto‐ and cross‐correlations of the water level time series. Output updating procedures based on the autoregressive (AR) and recursive AR (RAR) models were used to enhance ANFIS model outputs. The RAR model performed better than the AR model. In addition, a partial recursive procedure that reduced the number of recursive steps when applying the AR or the RAR model for multi‐step‐ahead error prediction was superior to the fully recursive procedure. The RAR‐based partial recursive updating procedure significantly improved three‐, four‐ and five‐lead‐day forecasts. Our study further shows that for long lead times, ANFIS model errors are dominated by lag time errors. Although the ANFIS model with the RAR‐based partial recursive updating procedure provided the best results, this method was able to reduce the lag time errors significantly for the falling limbs only. Improvements for the rising limbs were modest. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
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