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1.
In many engineering problems, such as flood warning systems, accurate multistep‐ahead prediction is critically important. The main purpose of this study was to derive an algorithm for two‐step‐ahead forecasting based on a real‐time recurrent learning (RTRL) neural network that has been demonstrated as best suited for real‐time application in various problems. To evaluate the properties of the developed two‐step‐ahead RTRL algorithm, we first compared its predictive ability with least‐square estimated autoregressive moving average with exogenous inputs (ARMAX) models on several synthetic time‐series. Our results demonstrate that the developed two‐step‐ahead RTRL network has efficient ability to learn and has comparable accuracy for time‐series prediction as the refitted ARMAX models. We then investigated the two‐step‐ahead RTRL network by using the rainfall–runoff data of the Da‐Chia River in Taiwan. The results show that the developed algorithm can be successfully applied with high accuracy for two‐step‐ahead real‐time stream‐flow forecasting. Copyright © 2003 John Wiley & Sons, Ltd.  相似文献   

2.
Streamflow forecasting is very important for the management of water resources: high accuracy in flow prediction can lead to more effective use of water resources. Hydrological data can be classified as non‐steady and nonlinear, thus this study applied nonlinear time series models to model the changing characteristics of streamflows. Two‐stage genetic algorithms were used to construct nonlinear time series models of 10‐day streamflows of the Wu‐Shi River in Taiwan. Analysis verified that nonlinear time series are superior to traditional linear time series. It is hoped that these results will be useful for further applications. Copyright © 2008 John Wiley & Sons, Ltd.  相似文献   

3.
初值和海温强迫对延伸期可预报性时空分布的影响   总被引：1，自引：0，他引：1       下载免费PDF全文
利用全球谱模式T106L19和增长模繁殖法,分别在气候海温和预测海温强迫下,进行了动力延伸集合预报试验.基于方差分析思想,利用集合预报结果,定义和计算了初值影响指数、海温强迫影响指数、潜在可预报性指数以及波动活动指数.通过分析四个指数,揭示了初值和海温强迫对延伸期可预报性时空分布以及潜在可预报性的影响,并探讨了其影响机理.结果表明：初值影响指数分布具有地域和季节的差异,初值的影响在中高纬度地区大于热带地区;相同季节,海温强迫影响指数分布与初值影响指数分布相似;潜在可预报性指数呈带状分布,大值集中在热带地区,且在低纬度地区,高层的潜在可预报性大于低层;初值和海温强迫对延伸期可预报性时空分布的影响,依赖于大气环流形势,初值和海温强迫影响的显著区正是大气长波的活跃区和西风急流区,急流区的强风切变为长波活动提供了斜压不稳定能量,而长波的发展调控着初值和海温强迫的影响,这说明延伸期的可预报性具有明显的流依赖性,大气外强迫的作用也与大气内部的动力过程密切相关.  相似文献   

4.
In a water‐stressed region, such as the western United States, it is essential to have long lead times for streamflow forecasts used in reservoir operations and water resources management. Current water supply forecasts provide a 3‐month to 6‐month lead time, depending on the time of year. However, there is a growing demand from stakeholders to have forecasts that run lead times of 1 year or more. In this study, a data‐driven model, the support vector machine (SVM) based on the statistical learning theory, was used to predict annual streamflow volume with a 1‐year lead time. Annual average oceanic–atmospheric indices consisting of the Pacific decadal oscillation, North Atlantic oscillation (NAO), Atlantic multidecadal oscillation, El Niño southern oscillation (ENSO), and a new sea surface temperature (SST) data set for the ‘Hondo’ region for the period of 1906–2006 were used to generate annual streamflow volumes for multiple sites in the Gunnison River Basin and San Juan River Basin, both located in the Upper Colorado River Basin. Based on the performance measures, the model showed very good forecasts, and the forecasts were in good agreement with measured streamflow volumes. Inclusion of SST information from the Hondo region improved the model's forecasting ability; in addition, the combination of NAO and Hondo region SST data resulted in the best streamflow forecasts for a 1‐year lead time. The results of the SVM model were found to be better than the feed‐forward, back propagation artificial neural network and multiple linear regression. The results from this study have the potential of providing useful information for the planning and management of water resources within these basins. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

5.
Fluvial flood events have substantial impacts on humans, both socially and economically, as well as on ecosystems (e.g., hydroecology and pollutant transport). Concurrent with climate change, the seasonality of flooding in cold environments is expected to shift from a snowmelt‐dominated to a rainfall‐dominated flow regime. This would have profound impacts on water management strategies, that is, flood risk mitigation, drinking water supply, and hydro power. In addition, cold climate hydrological systems exhibit complex interactions with catchment properties and large‐scale climate fluctuations making the manifestation of changes difficult to detect and predict. Understanding a possible change in flood seasonality and defining related key drivers therefore is essential to mitigate risk and to keep management strategies viable under a changing climate. This study explores changes in flood seasonality across near‐natural catchments in Scandinavia using circular statistics and trend tests. Results indicate strong seasonality in flooding for snowmelt‐dominated catchments with a single peak occurring in spring and early summer (March through June), whereas flood peaks are more equally distributed throughout the year for catchments located close to the Atlantic coast and in the south of the study area. Flood seasonality has changed over the past century seen as decreasing trends in summer maximum daily flows and increasing winter and spring maximum daily flows with 5–35% of the catchments showing significant changes at the 5% significance level. Seasonal mean daily flows corroborate those findings with higher percentages (5–60%) of the catchments showing statistically significant changes. Alterations in annual flood occurrence also point towards a shift in flow regime from snowmelt‐dominated to rainfall‐dominated with consistent changes towards earlier timing of the flood peak (significant for 25% of the catchments). Regionally consistent patterns suggest a first‐order climate control as well as a local second‐order catchment control, which causes inter‐seasonal variability in the streamflow response.  相似文献   

6.
Raise Beck is a mountain torrent located in the central Lake District fells, northern England (drainage area of 1·27 km2). The torrent shows evidence of several major flood events, the most recent of which was in January 1995. This event caused a major channel avulsion at the fan apex diverting the main flood flow to the south, blocking the A591 trunk road and causing local flooding. The meteorological conditions associated with this event are described using local rainfall records and climatic data. Records show 164 mm of rainfall in the 24 hours preceding the flood. The peak flood discharge is reconstructed using palaeohydrological and rainfall–runoff methods, which provide discharge values of 27–74 m3 s?1, and 4–6 m3 s?1, respectively. The flood transported boulders with b‐axes up to 1400 mm. These results raise some important general questions about flood estimation in steep mountain catchments. The geomorphological impact of the event is evaluated by comparing aerial photographs from before and after the flood, along with direct field observations. Over the historical timescale the impact and occurrence of flooding is investigated using lichenometry, long‐term rainfall data, and documentary records. Two major historical floods events are identified in the middle of the nineteenth century. The deposits of the recent and historical flood events dominate the sedimentological evidence of flooding at Raise Beck, therefore the catchment is sensitive to high magnitude, low frequency events. Following the 1995 flood much of the lower catchment was channelized using rip‐rap bank protection, re‐establishing flow north towards Thirlmere. The likely success of this management strategy in containing future floods is considered, based on an analysis of channel capacities. It is concluded that the channelization scheme is only a short‐term solution, which would fail to contain the discharge of an event equivalent to the January 1995 flood. Copyright © 2002 John Wiley & Sons, Ltd.  相似文献   

7.
Various types of neural networks have been proposed in previous papers for applications in hydrological events. However, most of these applied neural networks are classified as static neural networks, which are based on batch processes that update action only after the whole training data set has been presented. The time variate characteristics in hydrological processes have not been modelled well. In this paper, we present an alternative approach using an artificial neural network, termed real‐time recurrent learning (RTRL) for stream‐flow forecasting. To define the properties of the RTRL algorithm, we first compare the predictive ability of RTRL with least‐square estimated autoregressive integrated moving average models on several synthetic time‐series. Our results demonstrate that the RTRL network has a learning capacity with high efficiency and is an adequate model for time‐series prediction. We also investigated the RTRL network by using the rainfall–runoff data of the Da‐Chia River in Taiwan. The results show that RTRL can be applied with high accuracy to the study of real‐time stream‐flow forecasting networks. Copyright © 2002 John Wiley & Sons, Ltd.  相似文献   

8.
In the Northern Great Plains, melting snow is a primary driver of spring flooding, but limited knowledge of the magnitude and spatial distribution of snow water equivalent (SWE) hampers flood forecasting. Passive microwave remote sensing has the potential to enhance operational river flow forecasting but is not routinely incorporated in operational flood forecasting. We compare satellite passive microwave estimates from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E) to the National Oceanic and Atmospheric Administration Office of Water Prediction (OWP) airborne gamma radiation snow survey and U.S. Army Corps of Engineers (USACE) ground snow survey SWE estimates in the Northern Great Plains from 2002 to 2011. AMSR‐E SWE estimates compare favourably with USACE SWE measurements in the low relief, low vegetation study area (mean difference = ?3.8 mm, root mean squared difference [RMSD] = 34.7 mm), but less so with OWP airborne gamma SWE estimates (mean difference = ?9.5 mm, RMSD = 42.7 mm). An error simulation suggests that up to half of the error in the former comparison is potentially due to subpixel scale SWE variability, limiting the maximum achievable RMSD between ground and satellite SWE to approximately 26–33 mm in the Northern Great Plains. The OWP gamma versus AMSR‐E SWE comparison yields larger error than the point‐scale USACE versus AMSR‐E comparison, despite a larger measurement footprint (5–7 km2 vs. a few square centimetres, respectively), suggesting that there are unshared errors between the USACE and OWP gamma SWE data.  相似文献   

9.
Satellite‐based and reanalysis quantitative precipitation estimates are attractive for hydrologic prediction or forecasting and reliable water resources management, especially for ungauged regions. This study evaluates three widely used global high‐resolution precipitation products [Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks‐Climate Data Record (PERSIANN‐CDR), Tropical Rainfall Measuring Mission 3B42 Version 7 (TRMM 3B42V7), and National Centers for Environment Prediction‐Climate Forecast System Reanalysis (NCEP‐CFSR)] against gauge observations with seven statistical indices over two humid regions in China. Furthermore, the study investigates whether the three precipitation products can be reliably utilized as inputs in Soil and Water Assessment Tool, a semi‐distributed hydrological model, to simulate streamflows. Results show that the precipitation estimates derived from TRMM 3B42V7 outperform the other two products with the smallest errors and bias, and highest correlation at monthly scale, which is followed by PERSIANN‐CDR and NCEP‐CFSR in this rank. However, the superiority of TRMM 3B42V7 in errors, bias, and correlations is not warranted at daily scale. PERSIANN‐CDR and 3B42V7 present encouraging potential for streamflow prediction at daily and monthly scale respectively over the two humid regions, whilst the performance of NCEP‐CFSR for hydrological applications varies from basin to basin. Simulations forced with 3B42V7 are the best among the three precipitation products in capturing daily measured streamflows, whilst PERSIANN‐CDR‐driven simulations underestimate high streamflows and high streamflow simulations driven by NCEP‐CFSR mostly are overestimated significantly. In terms of extreme events analysis, PERSIANN‐CDR often underestimates the extreme precipitation, so do extreme streamflow simulations forced with it. NCEP‐CFSR performs just the reverse, compared with PERSIANN‐CDR. The performance pattern of TRMM 3B42V7 on extremes is not certain, with coexisting underestimation and overestimation. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

10.
This paper describes the use of numerical weather and climate models for predicting severe rainfall anomalies over the Yangtze River Basin (YRB) from several days to several months in advance. Such predictions are extremely valuable, allowing time for proactive flood protection measures to be taken. Specifically, the dynamical climate prediction system (IAP DCP-II), developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP), is applied to YRB rainfall prediction and flood planning. IAP DCP-II employs ensemble prediction with dynamically conditioned perturbations to reduce the uncertainty associated with seasonal climate prediction. IAP DCP-II was shown to successfully predict seasonal YRB summer flooding events based on a 15-year (1980–1994) hindcast experiment and the real-time prediction of two summer flooding events (1999 and 2001). Finally, challenges and opportunities for applying seasonal dynamical forecasting to flood management problems in the YRB are discussed.  相似文献   

11.
Two models, one linear and one non‐linear, were employed for the prediction of flow discharge hydrographs at sites receiving significant lateral inflow. The linear model is based on a rating curve and permits a quick estimation of flow at a downstream site. The non‐linear model is based on a multilayer feed‐forward back propagation (FFBP) artificial neural network (ANN) and uses flow‐stage data measured at the upstream and downstream stations. ANN predicted the real‐time storm hydrographs satisfactorily and better than did the linear model. The results of sensitivity analysis indicated that when the lateral inflow contribution to the channel reach was insignificant, ANN, using only the flow‐stage data at the upstream station, satisfactorily predicted the hydrograph at the downstream station. The prediction error of ANN increases exponentially with the difference between the peak discharge used in training and that used in testing. ANN was also employed for flood forecasting and was compared with the modified Muskingum model (MMM). For a 4‐h lead time, MMM forecasts the floods reliably but could not be applied to reaches for lead times greater than the wave travel time. Although ANN and MMM had comparable performances for an 8‐h lead time, ANN is capable of forecasting floods with lead times longer than the wave travel time. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

12.
Robert E. Criss 《水文研究》2018,32(11):1607-1615
The rainfall–run‐off convolution integral is analytically solved for several models for the elementary hydrograph. These solutions can be combined with available rainfall frequency analyses to predict flood flows along streams for different recurrence intervals, using no free parameters for gauged streams and one estimable parameter for ungauged streams. Extreme discharge magnitudes at gauged sites can be typically estimated within a factor of two of actual records, using no historical data on extreme flows. The flow predictions reproduce several important characteristics of the flood phenomenon, such as the slope of the regression line between observed extreme flows and basin area on the conventional logQ versus logA plot. Importantly, for the models and data sets investigated, the storm duration of greatest significance to flooding was found to approximate the intrinsic transport timescale of the particular watershed, which increases with basin size. Thus, storms that deliver extraordinary amounts of rainfall over a particular time interval will most greatly activate basins whose time constants approximately equal that interval. This theoretical finding is supported by examination of the regional hydrological response to the massive storms of September 14, 2008, and April 28–30, 2017, which caused extraordinary record flooding of basins of about 5–100 km2 and 500–4,000 km2, respectively, but produced few records in basins that were larger or smaller than those ranges.  相似文献   

13.
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   

14.
Cellular‐based approaches for flood inundation modelling have been extensively calibrated and evaluated for the prediction of flood flows on rural river reaches. However, there has only been limited application of these approaches to urban environments, where the need for flood management is greatest. Practical application of two‐dimensional (2D) flood inundation models is often limited by computation time and processing power on standard desktop PCs when attempting to resolve flows on the high‐resolution grids necessary to replicate urban features. Consequently, it is necessary to evaluate the effectiveness of coarse grids to represent flood flows through urban environments. To examine these effects, LISFLOOD‐FP, a 2D storage cell model, is applied to hypothetical flooding scenarios in Greenfields, Glasgow. Grid resampling techniques in GIS software packages are evaluated and a bilinear gridding technique appears to provide the most accurate and physically intuitive results. A gridding method maintaining sharp elevation changes at building interfaces and neighbouring land is presented and estimates of the discretization noise associated with the coarse resolution grids suggest little improvement over current gridding methods. The variation in model results from the friction sensitivity analysis suggests a non‐stationary response to Manning's n with changing model resolution. Model results suggests that a coarse resolution model for urban applications is limited by the representation of urban media in coarse model grids. Furthermore, critical length scales related to building dimensions and building separation distances exist in urban areas that determine maximum possible grid resolutions for hydraulic models of urban flooding. Copyright ©, 2008 John Wiley & Sons, Ltd.  相似文献   

15.
M. Rahman  M. Sulis  S. J. Kollet 《水文研究》2016,30(10):1563-1573
Subsurface and land surface processes (e.g. groundwater flow, evapotranspiration) of the hydrological cycle are connected via complex feedback mechanisms, which are difficult to analyze and quantify. In this study, the dual‐boundary forcing concept that reveals space–time coherence between groundwater dynamics and land surface processes is evaluated. The underlying hypothesis is that a simplified representation of groundwater dynamics may alter the variability of land surface processes, which may eventually affect the prognostic capability of a numerical model. A coupled subsurface–land surface model ParFlow.CLM is applied over the Rur catchment, Germany, and the mass and energy fluxes of the coupled water and energy cycles are simulated over three consecutive years considering three different lower boundary conditions (dynamic, constant, and free‐drainage) based on groundwater dynamics to substantiate the aforementioned hypothesis. Continuous wavelet transform technique is applied to analyze scale‐dependent variability of the simulated mass and energy fluxes. The results show clear differences in temporal variability of latent heat flux simulated by the model configurations with different lower boundary conditions at monthly to multi‐month time scales (~32–91 days) especially under soil moisture limited conditions. The results also suggest that temporal variability of latent heat flux is affected at even smaller time scales (~1–3 days) if a simple gravity drainage lower boundary condition is considered in the coupled model. This study demonstrates the importance of a physically consistent representation of groundwater dynamics in a numerical model, which may be important to consider in local weather prediction models and water resources assessments, e.g. drought prediction. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   

16.
Because of high spatial heterogeneity and the degree of uncertainty about hydrological processes in large‐scale catchments of semiarid mountain areas, satisfactory forecasting of daily discharge is seldom available using a single model in many practical cases. In this paper the Takagi–Sugeno fuzzy system (TS) and the simple average method (SAM) are applied to combine forecasts of three individual models, namely, the simple linear model (SLM), the seasonally based linear perturbation model (LPM) and the nearest neighbour linear perturbation model (NNLPM) for modelling daily discharge, and the performance of modelling results is compared in five catchments of semiarid areas. It is found that the TS combination model gives good predictions. The results confirm that better prediction accuracy can be obtained by combining the forecasts of different models with the Takagi–Sugeno fuzzy system in semi‐arid mountain areas. Copyright © 2009 John Wiley & Sons, Ltd.  相似文献   

17.
During typhoons or storms, accurate forecasts of hourly streamflow are necessary for flood warning and mitigation. However, hourly streamflow is difficult to forecast because of the complex physical process and the high variability in time. Furthermore, under the global warming scenario, events with extreme streamflow may occur that leads to more difficulties in forecasting streamflows. Hence, to obtain more accurate hourly streamflow forecasts, an improved streamflow forecasting model is proposed in this paper. The computational kernel of the proposed model is developed on the basis of support vector machine (SVM). Additionally, self‐organizing map (SOM) is used to analyse observed data to extract data with specific properties, which are capable of providing valuable information for streamflow forecasting. After reprocessing, these extracted data and the observed data are used to construct the SVM‐based model. An application is conducted to clearly demonstrate the advantage of the proposed model. The comparison between the proposed model and the conventional SVM model, which is constructed without SOM, is performed. The results indicate that the proposed model is better performed than the conventional SVM model. Moreover, as regards the extreme events, the result shows that the proposed model reduces the forecasting error, especially the error of peak streamflow. It is confirmed that because of the use of data extracted by SOM, the improved forecasting performance is obtained. The proposed model, which can produce accurate forecasts, is expected to be useful to support flood warning systems. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

18.
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   

19.
In a real climate system there are multiple time-space scale atmosphere-ocean interactions, ranging from the planetary scale and basin scale to local air-sea interactions. The Zebiak-Cane (ZC) model with one-level atmosphere described only local air-sea interaction process. Thus the planetary scale Hadley cell and Walker cell anomalies should be introduced in the model. Including the planetary scale Hadley cell anomaly in the model improved the prediction skill. It showed that the improved model provided satisfactory prediction of the equatorial eastern Pacific SST anomaly with lead time of 9–10 months not only for 1970–1991 hut also for 1992–1995.  相似文献   

20.
This paper presents an analytical method for establishing a stage–fall–discharge rating using hydraulic performance graphs (HPG). The rating curves derived from the HPG are used as the basis to establish the functional relation of stage, fall and discharge through regression analysis following the USGS procedure. In doing so, the conventional trial‐and‐error process can be avoided and the associated uncertainties involved may be reduced. For illustration, the proposed analytical method is applied to establish stage–fall–discharge relations for the Keelung River in northern Taiwan to examine its accuracy and applicability in an actual river. Based on the data extracted from the HPG for the Keelung River, one can establish a stage–fall–discharge relation that is more accurate than the one obtained by the conventionally used relation. Furthermore, the discharges obtained from the proposed rating method are verified through backwater analysis for measured high water level events. The results indicate that the analytical stage–fall–discharge rating method is capable of circumventing the shortcomings of those based on single‐station data and, consequently, enhancing the reliability of flood estimation and forecasting. Copyright © 2008 John Wiley & Sons, Ltd.  相似文献   

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