长江实时洪水预报系统

长江实时洪水预报系统
黄艳
长江水利委员会水文局
摘要长江水利委员会1951年成立以来，长江实时洪水预报系统不断得到发展。

系统采
取气象模型（提供定量降雨预报）与水文、水力模型相结合的方法，并结合预报员经验分析，为长江上主要站点提供实时洪水预报。

采用的模型包括气象模型MM5，水文模型有新安江模型，API模型，水力模型有马斯京根方法，统计学模型如相关关系法和“大湖演算法”以及MIKE11模型。

本文介绍了长江水利委员会水文局实施的洪水预报系统的框架和概要。

简述了使用模型的原理，预报范围与流程，并给出了一些预报结果。

文章最后讨论了目前需要给予特别关注的重要问题。

关键字实时洪水预报，长江，定量降雨预报
1 引言
长江洪水历来带来巨大破坏。

1877年以来，至少有25次洪水超过了河道泄洪能力。

1998年洪水是最近发生的一次大洪水事件。

由于流域范围的大洪水会影响了成千上万人民的生命与财产安全，所以，对洪水预报的精度，时效性都提出了非常高的要求。

另外，随着社会经济的不断发展，对防洪的要求也在不断提高。

同时，由于人类活动的加剧，使得洪水预报变得更为复杂。

为了促进流域洪水管理，长江委水文局通过不断努力，逐步建立了一套实时洪水预报系统。

经过几十年的不断发展，目前使用的洪水预报系统是1990年代中期开发的。

系统包括1400多个水位雨量测站，其中118个中央报汛站实现自动报汛。

系统应用了多个水文水力学模型。

本文简要描述了当前业务实时洪水预报系统使用的方法及其使用效果，并讨论了即将面临的挑战。

2 长江实时洪水预报系统
2.1 概述
长江流域位于中国中部，面积108万平方公里，河流干支流全长6300余公里。

从青藏高原长江源头至宜昌为上游，集水面积100平方公里，从宜昌至武汉为中游，集水面积68万平方公里，从武汉至河口上海市为下游，集水面积12万平方公里。

针对不同区域，目前的洪水预报系统预报方法各不相同，详细见图1。

图1 长江实时洪水预报系统
如图1所示，当前的洪水预报系统由多个部分构成。

根据信息流程，系统组成可分为如下几个部分：
1、气象预报：使用气象模型提供定量降雨预报；
2、水文模拟：使用实时雨量与定量降雨预报值预测径流量。

使用的模型主要有API 方法[1]，单位线(UH) [2]演算和新安江模型(XAJ) [3]；
3、洪水演算：使用模型预测径流和实测径流作为边界条件，应用多个水力学模型和统计模型洪水演算程序计算不同站点的水位和流量。

使用的模型有马斯京根[4]，MIKE11，相关关系线法和由长江委水文局为复杂的洞庭湖区开发的所谓“大湖演算法”；
4、后验分析：人工对模型结果进行分析的过程。

结合不同模型给出的预报结果，考虑到人类活动或天气动力带来的不确定性，给出最终预报成果，予以发布。

这一步没有在图1中明显表现出来，但它是当前洪水预报系统中非常重要的一环。

2.2 系统组成
如上所述，当前的洪水预报系统由四部分组成，即定量降雨预报，降雨-径流模拟，洪水演算和对模型结果进行的后验分析。

使用的大多数模型在理论上是成熟的，模型的复杂性是可行的，大多数教科书中均有介绍。

惟一的例外是所谓的“大湖演算法”，它是专门为模拟洞庭湖区的江湖关系而研发的。

2.2.1 定量降雨预报(QPF)
为了增加预报预见期，提高预报精度，当前的洪水预报系统中进行了气象模拟，为水文模型提供QPF。

目前，使用两种预报方法来进行短期降雨准定量预报。

第一种方法是，通过分析各种气象信息，包括常规的地面天气信息，卫星信息，卫星图像，雷达遥感信息等等，估算降雨范围与趋势。

第二种方法是使用MM5模型来预报流域范围降雨。

该模型是一个中
期预报气象模型，由美国国家天气局和加州大学联合开发，起初是一个中尺度模型。

关于该模型的更多信息可以访问该网站：/mm5/mm5-home.html. 两种方法都用来为洪水预报系统提供QPF。

QPF计算单独进行，计算程序没有与水文模型自动对接。

2.2.2 水文模拟
长期长江洪水预报实践表明，有两个水文模型可以可靠地和准确地计算长江流域大多数地区径流量。

这两个模型是API模型和新安江模型(Zhao, 1984).。

·API 模型：API模型通常与单位线演算一起用来计算集水区的降雨-径流。

·新安江模型：该模型是一个概念性降雨-径流模型，在长江流域广泛采用。

其它水文模型如由澳大利亚研制的URBS，RAFTS模型，源自美国的HEC-HMS模型以及源自丹麦的NAM模型也在系统中应用，它们与其它模型并行测试运行。

2.2.3 洪水演算
在当前的洪水预报系统中，洪水演算程序包括有水力学模型和统计学方法。

在长江上游地区，河道较窄，稳定，马斯京根方法合适应用；在下游以及洞庭湖区，统计方法如相关关系线法和大湖演算法表现相对好些；MIKE11表现位于两者之间，比较稳定与准确。

马斯京根演算：作为广泛使用的一种1维洪水演算程序，该方法是长江主河道洪水演算的主要水力学模型，在长江上中游河道稳定的区域使用。

[5].
MIKE11:由丹麦水力研究所研制，是一个1维水力学模型，用于简单和复杂河流和河道系统模拟分析，设计，管理和调度。

水力学模块是MIKE11模型的核心。

相关关系曲线法：使用历史数据，建立了测站之间的水位之间和/或流量之间相关关系曲线。

在这中方法中，洪水波传播时间与洪峰之间建立相关关系。

这种关系曲线反映了地理上相互联系的两个或多个测站之间的统计特性和对应关系。

大湖演算法–洞庭湖区：最为复杂同样也是最为重要的洪水演算就是所谓的“大湖演算法”，用于洞庭湖区预测螺山站水文与流量。

将螺山看成一个大的蓄水池，根据质量守恒原理，考虑来自上游的所有入流，包括四口入流，长江主干宜昌入流，清江长阳入流以及集水区径流，通过水位～流量曲线来计算螺山站水位。

2.2.4 后验分析
模型是洪水预报系统的主要部分，但不是最后的一步。

后验分析成为预报过程的一部分，包含了专家经验和对当前形式的理解判断，是目前洪水预报系统最后的和重要的一步。

尽管模拟结果总的来说是好的，但在重要洪水事件期间，这还是不够的。

为了获得可靠的洪水预报，需要预报员考虑各种不确定因素对模型结果进行综合分析，最终确定预报值。

后验分析一般有这几项工作：
1、精度分析：结合由不同模型给出的预报值，确定可能最准确的预报；
2、历史洪水相似性研究：与历史洪水进行比较，找出相似之处，如果必要，寻求合适的管理措施；
3、气象假想案例研究：计算可能的暴雨时空组合产生的洪水；
4、社会经济评估：在洪水期结束之后，对洪水预报系统的表现进行社会经济影响评估，找出当年系统中的不足之处，为下一个汛期改进提供借鉴。

2.3 总结
如上所述，目前的洪水预报系统由多种模型和方法组成，结合气象预报，水文模拟和水力学洪水演算，并且还有预报员经验分析。

该系统提供3-5天长江干流和主要支流重要测站的洪水预报。

下面一节给出了最近的一些预报结果。

图2 长江流域6小时累积降雨预报
3 结果
3.1 定量降雨预报
在目前的洪水预报系统中，气象模拟为水文模型提供定量降雨预报（QPF）输入。

图2显示了使用MM5模型给出的长江流域6小时累积降雨预报。

统计分析表明，QPF精度是不够的。

目前正在努力改进MM5模型QPF预报精度。

3.2 洪水预报
当前洪水预报系统提供长江干流及支流主要测站水文和流量预报。

预见期一般为枝城以上测站为3天，螺山以下为5天。

图3显示了2005年汉口站汛期1-5天水文预报。

1-5天的平均预报误差分别为，0.05m，0.09m，0.14m，0.20m and 0.27m。

模拟系统总的来说是稳健的，准确的。

4 结论与讨论
实践表明，长江水利委员水文局开发与实施的长江洪水预报系统为长江流域的洪水管理和防洪提供了准确和有效的洪水预报，取得了显著的社会经济效益。

目前的洪水预报系统有几个值得关注的特性：
1、经验方法如相关关系线法被广泛采用。

物理基础水力学模型的应用具有局限性，主要是因为物理基础的确定性水力学模型需要大量的信息，而这些信息很难保持不断更新。

经
验方法相对简单，容易，可以使用最新的水文信息进行调整，因此更加实用。

2、在实时预报中，使用模型模拟只是第一步，考虑更多信息和不确定性因素是长江实时洪水预报非常重要的一步。

这需要借鉴预报员的经验。

这些经验很难用数学方程和其它清楚的规则来表达，但这些隐性经验的使用是长江洪水预报过程中非常重要的一环。

尽管当前的洪水预报系统是可靠有效的，但需要进行不断改进。

需要关注的几个问题如下：
1、应当加强和改进监测能力。

随着河流特性的改变，流域社会经济的快速发展，有必要加强监测与测绘，掌握河流特性的变化规律。

随着大型水利工程如三峡工程投入使用，河道冲淤以及水流特性也会发生变化。

有必要增加泥沙淤积观测。

为了更有效地进行信息管理，有必要改进自动监测和数据传输技术以及改进分析方法和模型。

2、有必要更新当前的预报/模拟方法和技术。

由于人类活动，目前的洪水预报方法变得不再准确。

由于三峡水库投入运行，河道淤积情势发生变化，下游河道变化显著。

过去的相关关系曲线可能不再合适，需要研究并建立新的关系曲线。

3、在目前洪水预报系统中建立实时更新程序是重要的。

数据同化技术如卡尔曼滤波或基因编程法对建立这种程序具有相当大的价值。

4、需要进一步跟踪和分析人类活动带来的影响。

在过去20年间，长江流域兴建了大量的水利灌溉或其他供水工程，长江干流和支流特性发生了重大改变，水流特性受到影响。

收集这些水利工程实时调度信息对洪水预报来说是重要的，尤其是对上游地区小流域来说更是如此。

为了获得准确和有效的洪水预报，不断收集和跟踪人类活动是十分有意义的。

[1] Kohler, M.A., and Linsley, R.K., 1951. Predicting runoff from storm rainfall. Res. Paper 34,
U.S. Weather Bureau, Washington, D.C.
[2] Sherman, L.K., 1942. ‘The unit hydrograph method’, Chapter X1E of Hydrology. ed. O.E.
Meinzer, pp. 514-525.
[3] Zhao, R.J., 1984. Hydrological modelling. Water Conservancy and hydropower publication,
1984 [M] Beijing. (赵人俊，1984，流域水文模拟[M]。

北京：水利电力出版社。

) [4] Cunge, J.A., 1969. On the subject of a flood propagation method. Journal of Hydraulics
Research. IAHR, 7, pp.205-230.
[5] Anthes, R. A., and Warner, T. T., 1978. Development of hydrodynamic models suitable for air
pollution and other mesometeorological studies. Mon. Wea. Rev., 106, 1045-1078.
作者简介：黄艳，PhD，长江委水文局总工助理，高工Contact
Address: 湖北武汉解放大道1863号，
邮编：430010
Tel: +86 27 8292 6230
Fax: +86 27 8282 9605
Email: yhuang@
Changjiang Real Time Flood Forecasting System
Yan Huang
Bureau of Hydrology, Changjiang Water Resources Commission
Abstract The Changjiang real time Flood Forecasting System (FFS) has been progressively developed throughout history since Changjiang Water Resources Commission (CWRC) was established in 1951. As an integrated approach, this system combines meteorological models which provides Quantitative Precipitation Forecasting (QPF) with various hydrological and hydraulic models, incorporates forecaster’s posterior analysis on the modeling outcomes, to provide real time Flood Forecasting (FF) for major gauge stations on Changjiang River. Models employed are: meteorological model MM5, hydrological model Xinanjiang and API model, and hydraulic model including Muskingum method, stochastic methods such as co-relationship curves between stations and the so-called ‘big-lake-routing’ approach near DongTing lake area, and fully hydrodynamic models such as MIKE 11.
This paper presents the framework and outline of the current FFS implemented in the Bureau of Hydrology (BOH) of CWRC. Theoretical background of each model is briefly given, as well as the forecasting scope and procedures, followed by some forecasting results. Discussions are given in the end of the paper on the key issues that require special attention and efforts to improve the current system to satisfy critical requirements from the changes of river nature as a result of human activities, and the flood prevention pressure due to the rapid economic and social development of the river basin.
Key word Real time flood forecasting; Changjiang; Quantitative Precipitation Forecasting (QPF)
1 Introduction
Flooding has caused enormous damage through its history on Changjiang River in China. Since 1877, at least 25 floods have exceeded the channel capacity of the river. The most recent of these major floods occurred in 1998. Since serious flooding at basin scale may involve millions of people living aside the river, requirement for Flood Forecasting (FF) in terms of accuracy, efficiency and sufficiency in scenario analysis is very high. In addition, the rapid economical development has induced progressively increasing pressure on the flood defense situation, and thence presents a continuous challenge to the FF technology. Moreover, increasing human activities within the basin also complicate the difficulties for FF. To support flood management at Changjiang River in the basin scale, a real time Flood Forecasting System (FFS) has been progressively developed in the FF department of the Bureau Of Hydrology (BOH), Changjiang Water Resources Commission (CWRC) since 1951 when CWRC was first established. It becomes the key system that provides regular basin-scale flood forecasting for flood management at Chagnjiang River.
Through three generations of system evolvement, the current Flood Forecasting System (FFS) was developed since the middle of 1990s. The system is supported by basin-wide data collecting and transferring system involving 1400 gauging stations, amongst there are 118 automatic hydrological monitoring stations. Various hydrological and hydraulic models are coupled in the system. This paper presents briefly the methodologies used in the current operational real time flood forecasting system, which is used at a very high frequency and updated at a regular period at
FF department, BOH CWRC (section 2). Some results are showed (section 3). Future challenges are discussed at the end of this paper (section 4).
2 Changjiang real time flood forecasting system
2.1 Overview
Located at the central-south China, the Changjiang River (also named Yangtze river) basin has 1.8 million km2 in area and over 6300 km in length. The upper catchment, which runs from the source of the river in the Qinghai-Tibet plateau to the city of Yichang, has a catchment area of 1.0 million km2. The middle catchment, which lies between Yichang and the City of Wuhan, has a catchment area of 0.68 million km2. The lower catchment, which runs from Wuhan down to the city of Shanghai at the estuary, has a catchment area of 0.12 million km2. To accord appropriate forecasting time and human resources input, the current FFS has been developed differently for the three areas accordingly (Figure 1).
Figure 1Current real time Flood Forecasting System (FFS) of Changjiang River, CWRC
As illustrated in Figure 1, several system components are involved in the current real time FFS. Follow the information flow, the system components can be distinguished as:
1. Metrological forecasting: providing Quantitative Precipitation Forecast (QPF) using meteorological models;
2. Hydrological modeling: using real time rainfall and QPF to predict runoff. Models used are mainly Antecedent Precipitation Index (API) method [1] using Unit Hydrograph (UH) [2] routing, and the Xinanjiang (XAJ) model [3];
3. Flood routing: using runoff from model estimations and actual measurements as the boundary
conditions, the flood routing procedures calculate water level and discharge at different stations using various hydraulic and statistic models; Models are Muskingum method [4], MIKE11 developed by DHI Water & Environment (/), co-relationship curves, and the so-called complex “big lake routing” at Dongting lake developed by BOH CWRC;
4. Posteriori analysis: combining results obtained from different models, integrating possible uncertainties in terms of human activities or weather dynamics, to make final decision on flood forecasts for issuing and management purpose. This part is not explicitly indicated in Figure 1 however it is a vital step in current FFS.
The following section explains the basic theory for each components and its functionality and role in the current real time FFS.
2.2 ystem components
As indicated above, the system components included in the current FFS are QPF, Rainfall-runoff modeling using hydrological models, flood routing using hydraulic approaches (models), and posterior analysis on model outcomes. Most models and approaches used in the FFS are mature in terms of theory, and more practical in terms of model complexity, and can be found in many text books. The only exception is the so-called “big lake routing” which is an approach developed specifically for the Dongting area for the main river channel especially Luosan station as one of the major gauging station at downstream of Changjiang River.
2.2.1 Quantitative Precipitation Forecast (QPF)
To increase FF lead time and to improve the accuracy, meteorological modelling is carried out in the current FFS to provide QPF for hydrological models. Currently two forecast methods are used to quasi-quantify short-term precipitation forecast. In the first method, based on the analysis of various meteorological information such as regular ground weather information, satellite aviation weather materials, satellite images, and radar remote sensing information, precipitation forecast range and tendency are estimated. The second method uses the MM5 model to forecast basin-wide precipitation. MM5 is a meteorological model for mid-term forecast, developed jointly by USA National Weather Service and University of California, which was first developed from a mesoscale model used by Anthes at Penn State in the early ‘70’s [5]. General information regarding the MM5 modeling system can be found on Web page /mm5/mm5-home.html. Both methods are used to provide the QPF in the current FFS. The QPF is carried out separately and not linked with the hydrological model automatically in terms of computer programming structure.
2.2.2 Hydrological modeling
Through the history of Changjiang flood forecasting practice, two typical hydrological models have been found reliable and accurate to calculate the runoff in most parts of the river basin. These two models are: the API model [1] and the Xinanjiang model [3] (Zhao, 1984).
API model: API model is usually combined with UH routing to calculate rainfall ~ runoff through the catchment. Introduced by the American engineer Sherman in 1932, the concept of the unit hydrograph ahs been widely accepted for its efficiency and effectiveness in rainfall ~ runoff computation. The UH is defined as the hydrograph of surface runoff resulting from effective
rainfall falling in a unit of time such as 1 hour or 1 day, and distributed uniformly in space and time over the total catchment area [2]. For most sub-catchments in Changjiang river basin, in general a 6 hour UH which is derived from historical data, is used to calculate runoff using the effective rainfall obtained from the API model. API is calculated on a daily basis and assumes that soil moisture declines exponentially when there is no rainfall, while the coefficient K differs for each month in a year.
Xinanjiang model: The Xinanjiang model is a conceptual rainfall–runoff model [3] (Zhao, 1984), and was developed for catchments with agricultural, forested and pastoral coverage. It has been widely used in Changjiang river basin. The model calculates runoff formation on the repletion of storage, i.e. to calculate the runoff after the soil has reached field capacity. In the model a basin is divided into sub-basins, the runoff is calculated based on non-uniform distribution effects of the tension water capacity. The runoff is separated and calculated in three parts: runoff production, runoff separation and runoff concentration; the outflow for each sub-basin is calculated and then routed through channels to the main basin outlet using the linear reservoir method.
Other hydrological models such as URBS, RAFTS which are of Australian origin and HEC-HMS which is of the United States origin, and NAM which is of Denmark origin, are also recently employed. These models are in testing phases in parallel to the current implemented models.
2.2.3 Flood routing
In current FFS, flood routing procedures consist of hydraulic models and statistic approaches. Depending on the river nature the procedures are different. At upstream areas where the river channel is relative narrow and stable, the Muskingum routing is well accepted; at downstream near Dongting lake area, due to the complex river-lake network, the statistic approach, i.e. the co-relationship curve and the ‘big-lake-routing’ procedure, performs better; in between MIKE11 performs relative stable and accurate.
Muskingum routing: as a widely applied 1-dimensional (1D) flood routing procedure, the Muskingum routing is the major hydraulic model in Changjiang main river channel flood routing, in particular at the up and middle stream areas where the river channel is geographically stable [5]. MIKE11: Developed by DHI Water & Environment, based on the conservation laws, MIKE 11 is a 1-dimensional fully hydrodynamic modeling package for the detailed analysis, design, management and operation of both simple and complex river and channel systems. The hydrodynamic module is the core of the MIKE 11 and forms the basis for its FF module.
Co-relationship curves: Using historical data, a set of water level ~ water level and/or discharge ~ discharge co-relationship curves between stations have been established by FF department BOH. In this method, flood wave transportation time is associated with peak values. This type of co-relationship curves express statistical characteristics and correspondence between two (or more) stations which are geographically linked. For example, important gauging stations such as Shashi at Jingjiang reach, Hankou station at Wuhan city, are all predicted using co-relationship curves to route the flood wave from its upstream stations of Yichang and Luosan, respectively.
Big Lake Routing – DongTing area: The most complex part and also important flood routing is the so-called “big-lake-routing method”, applied near DongTing area to predict water level and discharge at Luosan station. Regarding Luosan as a big storage, following mass balance principle, taking into all inflows from its upstream including four tributaries to DongTing lake, Yichang at main channel, Changyan from Qing river, and the catchment runoff, the method calculates water level at Luosan station through the water level ~ volume curve.
2.2.4 Posterior analysis
Modeling is the major but not the final step of the current FFS. The posterior analysis forms flood forecasting process with experts’ understanding to the situation and experiences, which is the final and important step of the current FFS. Although modeling results are in general good but during big flooding it is not sufficient. In big flooding period, situation is in general more complicated than what models can present. Therefore, to obtain reliable flood forecast, comprehensive analysis on the model outcomes combining uncertainties from all kinds of possible sources, are carried out by the forecasters to finally determine the flood forecast results. The posterior analysis in general involves the following tasks:
1. Accuracy analysis: combine forecasts from different models to determine the possible most accurate forecast;
2. Historical flood similarity study: compare with historical flooding event to find similarities, and to search for appropriate management measure when necessary;
3. Meteorological scenarios study: calculate floods based on scenarios of possible temporal and spatial combination of storms;
4. Social-economical assessment: by the end of the flood season a complete statistics will be applied to estimate the performance of the FFS, and to assess social-economical impact. It also provides an overall review to the inadequacies in system for the year and tackles improvement before the next flood season forecasting.
2.3 Summary
As presented above, consisting of various methods and models, combining meteorological forecasting, hydrological modeling and hydraulic flood routing, especially the posterior analysis by forecasters, the current FFS implemented in BOH_CWRC is operated on a daily base to provide 3-5 days flood forecasting for the major gauging stations on the main channel or the major tributaries. The following section presents some recent forecasting results.
Figure 26-hour cumulative precipitation forecasts on Yangtze River basin
3 Results
Some recent flood predictions have been chosen to demonstrate the products of QPF and flood forecasting within flood forecasting department work scope. The accuracy in terms of amplitude error for the peaks is assessed.
3.1 Quantitative precipitation forecasting
In the current FFS, meteorological simulation has provided QPF as the input for the hydrological model. Figure 2 shows the forecasts for 6 hour cumulative precipitation at Yangtze River Basin using MM5.
This quantitative result of the precipitation covering 6-48 hours (with 6 hour time step) is then provided in mm as ASCII file for hydrological models. However statistics analysis shows that the accuracy of the QPF using MM5 remains insufficient. Current efforts are being made on improving MM5 modelling accuracy for QPF.
3.2 Flood forecasting
Using the current system and QPF provided by the meteorological model, using the system presented in section 2, flood forecasting are made for gauging stations from Cuntan on discharge near Chongqing city, down to Yichang, Shashi, Zhicheng, Chenglingji (the mouth of DongTing lake), Luosan, Hankou and Datong on both discharges and water levels. Associated with the location of the station, lead time is in general 3 days for stations upstream of Zhicheng, and 5 days for downstream of Luosan (including Luosan). Figure 3 shows the flood forecasting for the flooding season in 2005 at Hankou station.
Figure 3 shows the 1-5 days water level forecast for the flood season in 2005 for Hankou station at Changjiang River. The average errors for 1-5 days are: 0.05m, 0.09m, 0.14m, 0.20m and 0.27m, respectively. The modeling system shows a robust and accurate flood forecast for Changjiang River in general.
4 Conclusions and discussions
Throughout the practice of its history, the current Changjiang flood forecasting system developed and implemented in BOH CWRC has provided accurate and efficient flood forecasting for flood management and control in Yangtze River basin, which gains significant social-economical benefit every year. Several characteristics that should be noted in the current FFS are:
1. Empirical method such as the co-relationship curves is widely used. The implementation of physically-based hydraulic model remains limited. The reason is: the physically-based deterministic hydrodynamic models require large amount of information, which is difficult to keep
it updated. For example it is difficult to keep very accurate cross section data due to the survey difficulties and the rapid change of the river (in particular the downstream area of the Three Gorges area). Meanwhile, it is impossible to keep updating the latest physical data as model calibration is time consuming task and it cannot satisfy the forecasting requirement. The empirical method is relatively simpler and easier to be adjusted using the latest hydrological data, and therefore are more practical.
2. In real time forecasting, simulations using models are just the first step, considering more information and uncertainties is a very important step for real time FF on Changjiang This type of capability works through the forecaster’s experience. They are difficult to express in mathematical equations neither explicit rule. The application of such implicit knowledge is a vital step in the flood forecasting process.
the with the
condition and flow characteristics have also changed. In addition, to maintain the current monitoring capability, increasing the frequency and density of sedimentation monitoring becomes more important. Meanwhile, to achieve more efficient information management, improvement on technology of automatic monitoring and data transferring, as well as analysis approach/models are becoming as well an important task.
2. It is urgent to update the current forecasting/modeling methods and technology. As the result of the human activities, current FF methods are no longer accurate. Due to the operation of Three Gorges Reservoir which has changed in both temporal and spatial distribution the sedimentation condition, change to the river channel at downstream areas is found significant. The old co-relationship curves established using historical data before the reservoir will be soon out of date. It is important to study and establish the new co-relationships following the nature changes.
3. It is important to develop a real time updating procedure in current FFS. There is an advantage in using the hydrodynamic models as it can be quickly modified to represent the updated river properties. However, due to the time consuming (relative to its changing frequency) for calibration, a real time updating procedure is needed to memorize and minimizes the model errors. Data assimilation technology such as Kalman Filtering or Genetic programming is of considerable value for such a development.
4. Further tackling and analyzing impact of human activities is needed. In the last two decades, for economical development, many hydro-constructions for hydropower, irrigation or other water supply purposes have been built on both the main channel and tributaries of Yangtze River basin. These human activities have largely changed the river nature and affected its flow characteristic. Gathering real time operation information of these projects is important for flood forecasting, especially at small catchments at upstream areas. For example according to the model simulations and weather condition, there should apparently be a big flood. However due to the storing of water at an upstream reservoir which is not included in the current system, the water is not balanced in the FFS, and this would create large uncertainty to the forecasting results. Not to mention is the operation of the big projects such as Three Gorges Dam which has changed the river nature and therefore affected flow combination at its downstream. For example, due to the lowering of the river bed at the main channel because of the change of sedimentation condition, the ratio outflow to DongTing lake is also changing. Therefore, to obtain an accurate and efficient flood forecasting, it is of great significance to collecting and tracking the operations of human activities frequently.
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About the author: Yan Huang (Ms.), born in September 1971 in Guizhou, China. Rewarded Doctorate degree (PhD) in October 2005 from University Twente, The Netherlands. Currently she is the assistant chief engineer (senior engineer) of Bureau of Hydrology, Changjiang Water Resources Commission. Contact
Address: 1863 Jianfang Avenue, Wuhan, 430010, China Tel: +86 27 8292 7530
Fax: +86 27 8282 9605
Email: yhuang@。