共查询到18条相似文献，搜索用时 375 毫秒
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与传统确定性预报相比，洪水概率预报能够为防洪调度决策提供更为丰富的信息。以大渡河猴子岩水库以上流域为研究区，建立新安江次洪模型，并采用动态系统响应曲线进行实时洪水预报校正。在确定性预报校正基础上，建立基于水文不确定性处理器（HUP）的次洪概率预报模型，定量分析预报不确定性，实现入库洪水概率预报。结果表明：(1)利用猴子岩流域2009 2019年水文气象资料，建立的新安江次洪模型整体精度较高，率定期和验证期的洪量和洪峰相对误差均在±20%以内，平均确定性系数分别为0.69和0.72；经动态系统响应曲线校正后，洪峰和洪量误差均有降低，率定期和验证期的确定性系数分别提高0.13和0.09。(2)以2020年3场洪水未来48 h预报降雨为输入，新安江模型预报精度不高，且随着预见期增长而降低，但经动态系统响应曲线校正后，整体预报精度有所提高，洪量相对误差减小幅度超50%，确定性系数提高幅度超60%。(3)HUP次洪概率预报模型提供的分布函数中位数Q50的预报精度在一定程度上优于校正后的确定性预报；提供的90%置信区间覆盖率均在90%左右，离散度均在0.40以下，能以相对较窄的区间覆盖大部分实测值... 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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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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本文旨在将实时监测得到的土壤墒情转化为流域水文模型可以直接使用的土壤含水量,论证将实时土壤墒情资料用于实时预报的可行性;利用实时监测土壤墒情,改进传统的模型结构,设计基于实测土壤墒情的降雨径流水文预报模型.采用土壤含水量误差抗差估计技术以抵御观测资料粗差的影响,提高系统的稳定性;并在此基础上提出了土壤含水量系统响应修正方法,以提高模型计算精度.将该模型应用于实验流域——宝盖洞流域进行应用检验,洪水模拟合格率达到92.3%,整体模拟精度达到甲级. 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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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
8.
为考虑洪水预报误差的空间变化,提出一种基于微分响应的流域产流分单元修正方法.该方法建立了各单元流域产流与流域出口流量之间的微分响应关系,采用正则化最小二乘法结合逐步迫近进行反演求解,将产流误差估计量分配给相应单元流域实现流域产流分单元修正.将构建的方法应用于大坡岭流域和七里街流域进行新安江模型产流修正,比较分析了流域产流分单元修正、流域面平均产流修正和自回归修正的效果.结果表明：流域产流分单元修正效果优于流域面平均产流修正;随着预见期的增大,产流微分响应修正效果优于自回归修正.该方法通过汇流系统将流域出口断面流量信息进行分解用于修正各单元流域产流,有利于提高实时洪水预报精度. 相似文献
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我国东南沿海多为独流入海的中小流域,河流短小,流域调节能力弱.该区洪水历时较短,但危害较大,加之近年来区内经济的迅速发展,洪水造成损失日趋加剧,因此开展此区洪水特性和防洪减灾研究意义重大.本文以中国东南沿海曹娥江流域为典型,根据中小流域洪水的特点,在初步分析流域降雨径流的成因和洪水演进规律的基础上,开展了流域洪水模拟研究, 选择建立了流域降雨径流模型以及洪水演进模型,重点探讨了利用遥感信息和GIS相结合确定水文模型参数的方法和途径,经实验流域资料检验分析,其模拟结果计算精度满足要求.该研究将有助于该区流域降雨径流特性及洪水演变规律的深入研究,同时为东南沿海中小流域洪水模拟预测和防洪减灾研究提供了经验和模式. 相似文献
10.
阿克苏河(中吉国际河流)现已成为塔里木河的主河源,它对塔里木河干流的形成、发展和演变过程起着决定性作用.随着国家西部开发战略--塔里木河流域综合治理的深入开展和实施,阿克苏河流域的水文特征、水文预报等研究成为热点.特别是在干旱区中纬度高海拔流域的河流中,阿克苏河是以冰雪融水补充为主河流的典型代表,对阿克苏河流域径流进行预报研究具有理论和现实意义.鉴于此:(i)结合干旱区无资料或少资料的现状,利用现有的水文气象资料,尝试并构建日尺度水文预报方法;(ii)采用高空气温代替地面实测气温与日径流相关关系法、AR(p)预报模型、气温降雨修正的AR(p)预报模型和NAM降雨径流模型,对阿克苏流域的两大支流进行日径流模拟和预报;(iii)对4种方法模拟结果进行对比分析,表明利用气温和降雨修正后的AR(p)模型所用水文气象资料少、应用简便、预报精度较高、比较适用于资料较缺乏的阿克苏流域的短期径流预报.该研究以日尺度进行水文预报,在该流域尚属首次,不仅为阿克苏河、塔里木河的水文预报、洪水防治和全流域的水量调度等提供基础,也为干旱区其他流域的水文预报提供了参考方法. 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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Classified real-time flood forecasting by coupling fuzzy clustering and neural network 总被引：2，自引：0，他引：2
Minglei REN Bende WANG Qiuhua LIANG Guangtao FU 《国际泥沙研究》2010,25(2):134-148
This paper presented a new classified real-time flood forecasting framework by integrating a fuzzy clustering model and neural network with a conceptual hydrological model. A fuzzy clustering model was used to classify historical floods in terms of flood peak and runoff depth, and the conceptual hydrological model was calibrated for each class of floods. A back-propagation (BP) neural network was trained by using real-time rainfall data and outputs from the fuzzy clustering model. BP neural network provided a rapid on-line classification for real-time flood events. Based on the on-line classification, an appropriate parameter set of hydrological model was automatically chosen to produce real-time flood forecasting. Different parameter sets was continuously used in the flood forecasting process because of the changes of real-time rainfall data and on-line classification results. The proposed methodology was applied to a large catchment in Liaoning province, China. Results show that the classified framework provided a more accurate prediction than the traditional non-classified method. Furthermore, the effects of different index weights in fuzzy clustering were also discussed. 相似文献
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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
14.
《水文科学杂志》2013,58(2)
Abstract Artificial neural network (ANN) models provide huge potential for simulating nonlinear behaviour of hydrological systems. However, the potential of ANN is yet to be fully exploited due to the problems associated with improving the model generalization performance. Generalization refers to the ability of a neural network to correctly process input data that have not been used for calibrating the neural network model. In the hydrological context, better generalization performance implies higher precision of forecasting. The primary objectives of this study are to explore new measures for improving the generalization performance of an ANN-based rainfall–runoff model, and to evaluate the applicability of the new measures. A modified neural network model (entitled goal programming (GP) neural network) for modelling the rainfall–runoff process has been developed, in which three enhancements are made as compared to the widely-used backpropagation (BP) network. The three enhancements are (a) explicit integration of hydrological prior knowledge into the neural network learning; (b) incorporation of a modified training objective function; and (c) reduction of network sensitivity to input errors. Seven watersheds across a range of climatic conditions and watershed areas in China were selected for examining the alternative networks. The results demonstrate that the GP consistently outperformed the BP both in the calibration and verification periods and three proposed measures yielded improvement of performance. 相似文献
15.
Evaluation of four hydrological models for operational flood forecasting in a Canadian Prairie watershed 总被引：2，自引：2，他引：0
The complexities of the Prairie watersheds, including potholes, drainage interconnectivities, changing land-use patterns, dynamic watershed boundaries and hydro-meteorological factors, have made hydrological modelling on Prairie watersheds one of the most complex task for hydrologists and operational hydrological forecasters. In this study, four hydrological models (WATFLOOD, HBV-EC, HSPF and HEC-HMS) were developed, calibrated and tested for their efficiency and accuracy to be used as operational flood forecasting tools. The Upper Assiniboine River, which flows into the Shellmouth Reservoir, Canada, was selected for the analysis. The performance of the models was evaluated by the standard statistical methods: the Nash-Sutcliffe efficiency coefficient, correlation coefficient, root mean squared error, mean absolute relative error and deviation of runoff volumes. The models were evaluated on their accuracy in simulating the observed runoff for calibration and verification periods (2005–2015 and 1994–2004, respectively) and also their use in operational forecasting of the 2016 and 2017 runoff. 相似文献
16.
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
17.
A methodology is proposed for constructing a flood forecast model using the adaptive neuro‐fuzzy inference system (ANFIS). This is based on a self‐organizing rule‐base generator, a feedforward network, and fuzzy control arithmetic. Given the rainfall‐runoff patterns, ANFIS could systematically and effectively construct flood forecast models. The precipitation and flow data sets of the Choshui River in central Taiwan are analysed to identify the useful input variables and then the forecasting model can be self‐constructed through ANFIS. The analysis results suggest that the persistent effect and upstream flow information are the key effects for modelling the flood forecast, and the watershed's average rainfall provides further information and enhances the accuracy of the model performance. For the purpose of comparison, the commonly used back‐propagation neural network (BPNN) is also examined. The forecast results demonstrate that ANFIS is superior to the BPNN, and ANFIS can effectively and reliably construct an accurate flood forecast model. Copyright © 2005 John Wiley & Sons, Ltd. 相似文献
18.
George Papaioannou George Varlas Anastasios Papadopoulos Athanasios Loukas Petros Katsafados Elias Dimitriou 《水文研究》2021,35(4):e14151
A common source of uncertainty in flood inundation forecasting is the hydrograph used. Given the role of sea-air-hydro-land chain processes on the water cycle, flood hydrographs in coastal areas can be indirectly affected by sea state. This study investigates sea-state effects on precipitation, discharge, and flood inundation forecasting implementing atmospheric, ocean wave, hydrological, and hydraulic-hydrodynamic coupled models. The Chemical Hydrological Atmospheric Ocean wave System (CHAOS) was used for coupled hydro-meteorological-wave simulations ‘accounting’ or ‘not accounting’ the impact of sea state on precipitation and, subsequently, on flood hydrograph. CHAOS includes the WRF-Hydro hydrological model and the WRF-ARW meteorological model two-way coupled with the WAM wave model through the OASIS3-MCT coupler. Subsequently, the 2D HEC-RAS hydraulic-hydrodynamic model was forced by the flood hydrographs and map the inundated areas. A flash flood event occurred on 15 November 2017 in Mandra, Attica, Greece, causing 24 fatalities, and damages was selected as case study. The calibration of models was performed exploiting historical flood records and previous studies. Human interventions such as hydraulic works and the urban areas were included in the hydraulic modelling geometry domain. The representation of the resistance caused by buildings was based on Unmanned Aerial System (UAS) data while the local elevation rise method was used in the urban-flood simulation. The flood extent results were assessed using the Critical Success Index (CSI), and CSI penalize. Integrating sea-state affected the forecast of precipitation and discharge peaks, causing up to +24% and from −8% to +36% differences, respectively, improving inundation forecast by 4.5% and flooding additional approximately 70 building blocks. The precipitation forcing time step was also highlighted as significant factor in such a small-scale flash flood. The integrated multidisciplinary methodological approach could be adopted in operational forecasting for civil protection applications facilitating the protection of socio-economic activities and human lives during similar future events. 相似文献