PDP和VAX系列机实时水情信息处理和洪水预报系统

本系统把实时水情信息处理和水文预报模型的实际应用结合起来,是实现从实时水情信息接收、处理、检索、应用,直到联机洪水预报通用的自动化系统。三个多功能的汉字、英文、绘图检索程序包,可为多用户监视实时水情、统计打印各种水情报表、绘制雨量图和水位流量过程线等提供各种服务。使用简便、具有同一输入输出文件格式、可自动共享历史和实时数据库资料、分块率定整体组装式的SCLS、XAJA、NSAC和NTAK等预报模型通用程序包,可实现从模型率定到联机洪水预报的全过程。     

基于神经网络理论的河道水情预报模型

河道水流运动过程特别是洪水演进过程是一个复杂的非线性动力学过程,鉴于神经网络具有很强的处理大规模复杂非线性动力学系统的能力,本文将神经网络理论用于河道水情预报的研究,以期识别水流运动变化过程与其影响因子之间的复杂非线性关系,为河道水情预报提供了一条新的途径。在此基础上建立了螺山站洪水预报的非线性动力学模型,通过分析研究得出近年来特别是1998年长江中游出现的小流量高水位现象与螺山汉口河段累计淤积有关并得到螺山站水位变化与河床淤积之间的定量关系。     

水利部水文局业务交流和技术讲座圆满结束

2006年6月5日至16日,水利部水文局举办了为期二周的业务交流与技术培训。内容侧重于流域水情、气象、网络业务知识、防汛抗旱业务系统以及实用洪水预报模型技术、中长期水资源预报、枯季径流预报、突发性洪水预报、水质预报模型等。长江委水文局洪水预报专家葛守西教授、清华大学水利水电工程系杨大文教授应邀分别做了“现代洪水预报预测技术与经验”、“大尺度分布式水     

双牌水库洪水预报及调度自动化系统研究

根据双牌水库水情自动测报系统要求,结合该水库的流域和工程特性,应用新安江模型、ＡＰＩ模型及动态规划等,对该水库洪水预报及调度进行了系统研究。     

VSAT卫星在柳州市洪水预警预报系统中的应用

柳州市洪水灾害频繁,为了防洪减灾在柳江上建设洪水预警预报系统。系统由水情遥测、计算机广域网和洪水预报模型等子系统组成。水情遥测子系统的通信采用卫星和超短波组网,并使用卫星作为超短波中继的热备份,提高系统的可靠性。系统经过１９９９年主汛期的考核,运行正常,使用效果良好,ＶＳＡＴ工作频段为Ｋｕ波段,存在雨衰问题,在建设中采取了一些措施,使该问题不致于影响系统的正常运行。实践证明,ＶＡＳＴ卫星在柳州市洪     

考虑区间正负入流量的自适应洪水预报模型

根据甘肃省气候条件和地理环境十分复杂，河流站网密度低，面对上下游区间有正负入流量且没有观测资料，以水量平衡原理为基础的河道洪水演算无法进行的实际情况，立足于利用现有上下游水站的水情信息，在传统系统水模型和河道汇流理论有关原理、方法的基础上，提出了考虑区间正负入流量的自适应洪水预报模型，改进了现行系统输入～输出水量不平衡关系，解决了甘肃省河流洪水过程无法预报的问题。具有十分广阔的应用前景。     

淮河干流洪水预报系统及其在1991年大洪水的应用

综述了淮河流域概况，淮河干流洪水预报系统物采用的预报方法。１９９１年淮河干流洪水预报采用了降雨径流预报与上，下游站相应流量预防方法相配合，并注重实时水情分析；在行洪区多，行洪后水面宽广而比降又极小的河段的汇流计算，采用了以实测洪水资料绘制的经验蓄曲线为充分发挥的湖泊洪水演算方法。     

水情自动测报系统在水资源系统管理中的应用

水情自动测报系统在水资源系统管理中发挥着十分重要的作用。针对该系统在盘石头水库管理中站网布设、系统硬件组成、数据采集系统、洪水预报系统、洪水调度系统等应用技术进行详实分析,运行演示软件系统进行实例分析,达到了较高的预报精度。水情自动测报系统不仅能在汛期起到防洪减灾作用,而且在也可用于日常的水资源管理,可以起到增加蓄水和发电量,达到优化调度的目的,从而提高水利工程的社会效益和经济效益。     

陕西省水情信息系统的研究与开发

介绍了陕西省水情信息系统及其功能和特点，对水情信息系统开发的总体目标、功能模块、数据库设计和预报模型集成化结构进行了探讨。该系统经过2001年、2002年2个汛期的应用，系统运行稳定，功能齐全，操作简单，使用方便，流程合理，预报精度优良，标准化与先进程度较高，满足国家防汛抗旱指挥系统总体要求，提高了水情工作现代化的整体水平，并在实际使用中取得了显著的社会效益和经济效益。     

中国洪水预报系统设计建设研究

分析了我国洪水预报系统的现状,阐述了中国洪水预报系统的建设的必要性、目标及开发原则,介绍了中国洪水预报系统的软硬件环境、预报模型和方法、预报方案、模型率定、实时作业预报、实用模块、系统管理7部分的设计建设。     

通用洪水预报模型库设计建设研究

在介绍洪水预报模型库的基本概念和性质的基础上，分析了各类洪水预报模型的输入、输出数据类型，规定了各类数据文件格式，设计了洪水预报模型库的通用数据接口，并在中国洪水预报系统中实现了通用洪水预报模型库的建设。     

陕西省地质灾害-气象预报预警系统研制及应用

为了提高地质灾害-气象预报预警工作的自动化程度和产品的质量,文章从计算机系统制作的角度探讨了地质灾害-气象预报预警的方法,论述了陕西省地质灾害-气象预报预警系统的运行环境、软件功能、空间数据库、模型方法等内容,并介绍了陕西省汛期地质灾害-气象预报预警的应用实例。     

A Probability-Based Renewal Rainfall Model for Flow Forecasting

In real-time flood warning systems, sufficient lead-time is important for people to take suitable actions. Rainfall forecasting is one of the ways commonly used to extend the lead-time for catchments with short response time. However, an accurate forecast of rainfall is still difficult for hydrologists using the present deterministic model. Therefore, a probability-based rainfall forecasting model, based on Markov chain, was proposed in this study. The rainfall can be forecast one to three hours in advance for a specified nonexceeding probability using the transition probability matrix of rainfall state. In this study, the nonexceeding probability, which was hourly updated on the basis of development or decay of rainfall processes, was taken as a dominant variable parameter. The accuracy of rainfall forecasting one to three hours in advance is concluded from the application of this model to four recording rain gauges. A lumped rainfall-runoff forecasting model derived from a transfer function was further applied in unison with this rainfall forecasting model to forecast flows one to four hours in advance. The results of combination of these two models show good performance with agreement between the observed and forecast hydrographs.     

洪水预报模型输入与输出的误差分布及相关性分析

降雨预报信息作为洪水预报模型的输入,该信息的准确性直接影响洪水预报模型输出的准确性.为探究模型输入(降雨预报)误差与输出(洪水预报)误差之间的关系,以英那河流域为例,分析了不同雨量等级下,预报模型的输入误差与输出误差的分布规律,并定性分析了两种误差的相关关系.结果表明,降雨量等级若为无雨及小雨时,两种误差不相关;若为中...     

An extended STIRPAT model-based methodology for evaluating the driving forces affecting carbon emissions in existing public building sector: evidence from China in 2000–2015

In recent decades, population growth associated with unplanned urban occupation has increased the vulnerability of the Brazilian population to natural disasters. In susceptible regions, early flood forecasting is essential for risk management. Still, in Brazil, most flood forecast and warning systems are based either on simplified models of flood wave propagation through the drainage network or on stochastic models. This paper presents a methodology for flood forecasting aiming to an operational warning system that proposes to increase the lead time of a warning through the use of an ensemble of meteorological forecasts. The chosen configuration was chosen so it would be feasible for an operational flood forecast and risk management. The methodology was applied to the flood forecast for the Itajaí-Açu River basin, a region which comprises a drainage area of approximately 15,500 km² in the state of Santa Catarina, Brazil, historically affected by floods. Ensemble weather forecasts were used as input to the MHD-INPE hydrological model, and the performance of the methodology was assessed through statistical indicators. Results suggest that flood warnings can be issued up to 48 h in advance, with a low rate of false warnings. Streamflow forecasting through the use of hydrological ensemble prediction systems is still scarce in Brazil. To the best of our knowledge, this is the first time this methodology aiming to an operational flood risk management system has been tested in Brazil.     

Forecasting for flood warning

Advances in flood forecasting have been constrained by the difficulty of estimating rainfall continuously over space, for catchment-, national- and continental-scale areas. This has had a concomitant impact on the choice of appropriate model formulations for given flood-forecasting applications. Whilst weather radar used in combination with raingauges – and extended to utilise satellite remote-sensing and numerical weather prediction models – have offered the prospect of progress, there have been significant problems to be overcome. These problems have curtailed the development and adoption of more complete distributed model formulations that aim to increase forecast accuracy. Advanced systems for weather radar display and processing, and for flood forecast construction, are now available to ease the task of implementation. Applications requiring complex networks of models to make forecasts at many locations can be undertaken without new code development and be readily revised to take account of changing requirements. These systems make use of forecast-updating procedures that assimilate data from telemetry networks to improve flood forecast performance, at the same time coping with the possibility of data loss. Flood forecasting systems that integrate rainfall monitoring and forecasting with flood forecasting and warning are now operational in many areas. Present practice in flood modelling and forecast updating is outlined from a UK perspective. Challenges for improvement are identified, particularly against a background of greater access to spatial datasets on terrain, soils, geology, land-cover, and weather variables. Representing the effective runoff production and translation processes operating at a given grid or catchment scale may prove key to improved flood simulation, and robust application to ungauged basins through physics-based linkages with these spatial datasets. The need to embrace uncertainty in flood-warning decision-making is seen as a major challenge for the future. To cite this article: R.J. Moore et al., C. R. Geoscience 337 (2005).     

Flood forecasts based on multi-model ensemble precipitation forecasting using a coupled atmospheric-hydrological modeling system

The recent improvement of numerical weather prediction (NWP) models has a strong potential for extending the lead time of precipitation and subsequent flooding. However, uncertainties inherent in precipitation outputs from NWP models are propagated into hydrological forecasts and can also be magnified by the scaling process, contributing considerable uncertainties to flood forecasts. In order to address uncertainties in flood forecasting based on single-model precipitation forecasting, a coupled atmospheric-hydrological modeling system based on multi-model ensemble precipitation forecasting is implemented in a configuration for two episodes of intense precipitation affecting the Wangjiaba sub-region in Huaihe River Basin, China. The present study aimed at comparing high-resolution limited-area meteorological model Canadian regional mesoscale compressible community model (MC2) with the multiple linear regression integrated forecast (MLRF), covering short and medium range. The former is a single-model approach; while the latter one is based on NWP models [(MC2, global environmental multiscale model (GEM), T213L31 global spectral model (T213)] integrating by a multiple linear regression method. Both MC2 and MLRF are coupled with Chinese National Flood Forecasting System (NFFS), MC2-NFFS and MLRF-NFFS, to simulate the discharge of the Wangjiaba sub-basin. The evaluation of the flood forecasts is performed both from a meteorological perspective and in terms of discharge prediction. The encouraging results obtained in this study demonstrate that the coupled system based on multi-model ensemble precipitation forecasting has a promising potential of increasing discharge accuracy and modeling stability in terms of precipitation amount and timing, along with reducing uncertainties in flood forecasts and models. Moreover, the precipitation distribution of MC2 is more problematic in finer temporal and spatial scales, even for the high resolution simulation, which requests further research on storm-scale data assimilation, sub-grid-scale parameterization of clouds and other small-scale atmospheric dynamics.     

基于单位线反演的产流误差修正

为提高实时洪水预报的精度,将单位线引入实时洪水预报修正中,建立一种向信息源头追溯的反馈修正模型。用最小二乘估计原理,通过推求产流量误差,用理想模型对误差修正模型进行了验证,并对不同范围的产流量误差修正效果进行对比。在浙江长潭流域对11场历史洪水进行修正验证,效果明显,对预报精度有一定的提高。该方法结构简单,且不增加参数,物理概念清晰,又不损失预见期,可以在实际流域洪水预报中推广应用。     

基于人工神经网络的多泥沙洪水预报

阐述了人工神经网络方法对多泥沙洪水演进的辨识机理,建立神经网络预报模型,采用最小二乘快速收敛法,通过模型联想实现水沙演进预报.研究结果与实测结果吻合良好,体现了人工神经网络的应用价值和发展前景.