英国实时洪水预报研究

河流洪水会引起生命损失、灾难性的财产破坏以及对经济和环境的负面影响。虽然不能完全消除洪水的风险,但是,实时洪水预报模型作为洪水警报服务的重要和不可分割的组成部分,可给及时洪水警报提供足够的超前时间,以尽量减少洪水损失。介绍了英国开发的2种实时洪水警报模型：简单触发洪水警报模型和流域综合水文和水动力学模型。     

通用实时洪水预报系统设计与开发

针对实时洪水预报系统需求,设计和开发了一套完整的通用实时洪水预报系统,包括预报方案配置率定、实时人工交互预报以及实时自动预报3个模块。重点介绍了系统结构和数字流域拓扑关系构建、模型库、预报方案构建、模型参数自我修正、人机交互和实时校正等相关关键技术。该系统将洪水预报方案的制作、预报作业的流程合理分解,便于预报系统实施和调试,在实践应用中取得了较好的效果。     

实时洪水预报系统

实时洪水预报系统主题词实时洪水预报，预报系统，数据处理，计算机应用，英国通过采用英国水文研究所、Ｌｏｇｉｃａ公司和国家河流管理局设计的一种系统，现在可以在全国范围内根据需要的周期按１５ｍｉｎ、每天或每周的时段进行实时洪水预报。这套河流流量预报系统（Ｒ...     

青尼罗河实时洪水预报

苏丹靠近埃寒俄比边的埃尔代姆水文站使用ＳＭＡＲ－ＡＲ模型进行了青尼罗河的实时流量预报。该模型用１９９２￣１９９４年资料较准并用１９９５年资料检验，将ＳＭＡＲ－ＡＲ模型结果与苏丹灌溉与水利部（ＭＯＩＷＲ）使用的荷兰ＳＡＭＦＩＬ模型结果进行了对比分析。对１９９２￣１９９５年４个汛期的预报结果进行了比较，ＳＡＭＦＩＬ模型的流量预报结果取自ＭＯＩＷＲ的档案资料。比较成果表明，用ＳＭＡＲ＿ＡＲＳ民得的流量预     

洪水预报技术研究进展与展望

随着城市化进程加快,地面不渗透面积的增加,洪水发生频率的增大,极端气候的加剧,城市排水管网的老化等原因造成城市雨涝情况的突出,使得传统排涝减排方法不得不进行更新,迫使我们采用新的洪水预报技术进行洪水预报及洪涝控制。     

我国洪水预报技术研究进展与展望

洪水预报是防灾减灾的一项重要的非工程措施。在多年的科研和实际防汛工作中,我国的洪水预报在基础理论、技术方法和实际经验上取得了巨大成绩,特别是‘98大水以后,洪水预报在预报方法、预报系统建设、预报作业管理、新技术应用等方面有了新的进展。本文简要回顾了我国洪水预报技术的最新进展,并就国内外洪水预报发展情况进行了对比分析。最后,展望了洪水预报研究前沿的一些新技术和新方法的应用。     

北江“94.6”洪水特点和数学模型实时预报应用

本文通过介绍北江“９４．６”特大洪水的特点。侧重论述了北江洪水预报调度数学模型的方法，结构，及其在实时预报中的应用，同时对预报结果进行了实事求是的评价，剖析了存在问题的原因，以利今后进一步完善这一方面的工作。     

实时洪水预报中的雨量短期预报

为提高实时洪水预报的精度,延长实时预报预见期,针对目前预见期内降雨不作处理的状况,提出了对预见期内的雨量进行短期预报的思路．经分析比较发现,采用相邻时段雨量相关关系预报预见期内的雨量,对延长洪水预报预见期有一定的作用．在针对亭下水库流域进行试验时,经对不同预见期的预报结果分析后证明,此方法相对于不预报雨量的方法有更高的精度．且延长了预报的预见期．     

洪水预报实时校正技术研究进展

回顾国内外洪水预报实时校正的产生背景,评述其理论与方法的发展历程。在此基础上,将实时校正方法归纳为终端误差校正和过程误差校正两类,并梳理出各自的典型校正方法以及联合校正方法,概述不同方法的研究成果及进展。重点介绍其中的反馈模拟技术、误差自回归算法(AR)、递推最小二乘算法(RLS)、卡尔曼滤波技术(KF)和动态系统响应曲线算法(DSRC)等5种代表性的实时校正技术,阐述其计算过程,并分析其特点与适用性。对洪水预报实时校正的未来发展方向及研究热点进行了展望。     

实时洪水预报中基于岭估计的AR修正模型研究

为提高实时洪水预报精度,经常将水文模型与误差修正模型相结合,AR模型因其结构简单广泛应用于实时洪水预报误差修正。然而,实际应用显示,AR模型时常出现修正结果不稳定现象,表现为流量修正幅度过大,甚至出现“震荡”现象,严重影响修正效果。鉴于此,本文从矩阵特征值角度解释了AR模型出现不稳定现象的原因,并引入岭估计方法选择性利用流量信息更新自回归系数,使其更满足真实流量的涨落过程,增强该模型的稳健性。将新方法应用于蔺河口流域,结果显示岭估计方法显著提高了AR模型的稳健性,从而改善了模型修正效果,进一步提高了洪水预报精度。     

A coupled rainfall-runoff and runoff-routing model for adaptive real-time flood forecasting

This paper describes an adaptive hydrologic modelling technique for real-time flood forecasting. The modelling approach is based on a linear stochastic time-varying representation of the rainfall-runoff process and on the Muskingum routing method formulated as an optimal linear filtering problem. The most general stochastic rainfall-runoff model used for linear forecasting is known as the transfer function noise model. An on-line identification procedure based on an extension of the recursive Instrumental Variable estimator is discussed. The routing procedure, based on the Muskingum method, is written in a state-space representation. This allows real-time updating of the state and the system parameters by means of Kalman filtering. The described method is used to forecast extreme flood events for the River Ourthe (drainage basin: approx 3626 km²), one of the main tributaries of the River Meuse, Belgium. The method is compared with stationary modelling procedures and its superiority based on objective forecasting criteria is demonstrated.     

线性动态系统模型在实时洪水预报中的应用

应用一般的线性动态系统模型理论，并结合衰减记忆最小二乘递推识别的实时校正技术，建立了鄱阳湖吴城站水位过程的实时预报方案。模型结构采用AIC准则法和逐步回归筛选法综合确定，模型参数采用最小二乘法估计。应用结果表明，该预报模型适用于湖泊水位这类缓变型水文变量的预报，可以用于实时作业预报。     

基于最大Lyapunov指数的混沌预测在洪水实时预报中的应用

为提高洪水预报精度,从而提高水库防洪调度可信度,采用混沌预测模型,研究了复杂洪水动力特征和非线性特征并分析洪水实时预报问题。考虑到时间延迟与嵌入维数的相关性,利用C-C法计算相空间重构参数,判别了洪水混沌特性。为避免由嵌入维数m的选取引起的最大Lyapunov指数的明显波动,使用了改进的小数据量方法计算最大Lyapunov指数。构造了基于最大Lyapunov指数的混沌洪水实时预报模型,并将其应用到湖南五强溪水库的洪水预报,计算结果表明该模型具有较高的预报精度。     

The performance of some real-time statistical flood forecasting models seen through multicriteria analysis

In the present work, seven statistical-routing models have been developed and applied to the Medjerdah River (Tunisia) for forecasting the extreme flood events at Jendouba for different forecasting horizons. The performance of the models are characterized by statistical measures of precision, peak error and peak delay between the measured and forecast flow and their variations. Due to the important number of criteria, a multi-criteria analysis is used to rank the models according to four forecasting horizons. The mixed models seem to be the best ones for short (2–4 h) as well as long (6–8 h) forecasting horizons.     

Development and application of a real-time flood forecasting system in the Veneto region of Italy

The paper describes three flood forecasting models used in the framework of the integrated hydrological forecasting system of the Veneto Region. The three models are based on the unit hydrograph theory, even though the computation of the effective rainfall and the real-time correction procedure are carried out in different ways. A comparison of the model performances is also made.     

抗旱减灾学科研究进展与展望

从回溯水利部防洪抗旱减灾工程技术研究中心抗旱减灾研究团队2005年成立的背景入手,结合初步构建的以旱灾学、防旱学和应急抗旱技术为主体的抗旱减灾学科体系,系统梳理了研究团队在基础理论研究、旱情监测预警技术、旱情预测预报技术、旱情旱灾影响评估技术、旱灾风险评估技术、抗旱规划编制、抗旱预案编制、应急抗旱设备等方面的研究及应用...     

河流系统实时洪水预报误差多点联合校正方法研究

实时校正是改善洪水实时预报精度的重要手段。河流系统中多个站点之间具有高度的水力联系,各个站点之间的误差也具有空间关联性。依据河道水流演进的基本方程和动态自适应的自回归方法,建立了考虑误差空间演化的河流系统实时洪水预报误差多点联合校正方法。利用洪峰段洪量误差、洪峰流量误差、纳什效率系数(NSE)和峰滞时间等指标分别对多点联合校正和不考虑误差空间关联性的单点校正开展校正效果评估。以淮河王家坝断面以上为背景开展实证研究,结果表明:考虑河流系统误差空间关联性的多点联合校正效果优于单点校正,洪峰段效果更为显著,能够更有效地提高河流系统洪水预报的精度。     

A real-time flood forecasting model based on maximum-entropy spectral analysis: II. Application

The MESA-based model, developed in the first paper, for real-time flood forecasting was verified on five watersheds from different regions of the world. The sampling time interval and forecast lead time varied from several minutes to one day. The model was found to be superior to a state-space model for all events where it was difficult to obtain prior information about model parameters. The mathematical form of the model was found to be similar to a bivariate autoregressive (AR) model, and under certain conditions, these two models became equivalent.Notation A _k parameter matrix of the bivariate AR model - B backshift operator in time series analysis - eT forecast error (vector) at timet = T - _t uncorrelated random series (white noise) - F _k forward extension matrix of the entropy model forkth lag - I identity matrix - m order of the entropy model - N number of observations - P order of the AR model - Q _p peak of the direct runoff hydrograph - R correlation matrix - t _p time to peak of the direct runoff hydrograph - ₁ coefficient of variation - ₂ ratio of absolute error to the mean - forecasted runoff - x _i observed runoff - mean of the observed runoff - X ^–1 inverse ofX matrix - X* transpose of theX matrix Abbreviations AIC Akaike information criterion - AR autoregressive (model) - AR(p) autoregressive process of thepth order - ARIMA autoregressive integrated moving average (model) - acf autocorrelation function - ccf cross-correlation function - FLT forecast lead time - MESA maximum entropy spectral analysis - MSE mean square error - STI sampling time interval