基于分布式水文模拟的三峡区间洪水预报(Ⅰ)——模型构建及验证

长江三峡区间因暴雨形成的洪水峰高量大,对三峡水库的防洪安全和运行调度的影响很大.本论文依据三峡地区的地形地貌特征,采用基于GIS的机理性分布式水文模型,来模拟三峡区间入库洪水,以尽量减少洪水预报中的不确定性.利用近期建成的78个自动雨量站网监测的小时降雨信息作为模型的输入,对模型参数进行了率定和验证,结果表明:大多数洪水过程的模拟精度较好,但也有的模拟结果较差,其中降雨信息缺失是洪水预报不确定性的主要来源.     

三峡库区陆气耦合研究及应用

以延长洪水预见期、提高预报精度为目标,研究气象水文耦合机制,利用数值天气预报模式WRF(Weather Research and Forecasting)驱动分布式VIC(Variable Infiltration Capacity)水文模型,构建三峡库区陆气耦合洪水预报系统,并对2007~2008年期间四场暴雨洪水进行日滚动预报试验。结果表明,WRF模式在三峡库区内有着良好的短期降水预报精度,基于数值天气预报模式和分布式水文模型的陆气耦合洪水预报系统能有效延长三峡入库洪水预见期、提高洪水预报精度,具有较大的应用潜力。     

三峡库区洪峰的来水组成及传播特征

三峡库区的洪峰流量受入库支流洪水及水库运行方式的影响并表现出显著的空间变异性, 开展基于来水组成的入库洪峰及其在库区传播特征的研究, 有助于进一步认识河道型水库的水动力机理并支撑三峡水库优化调度实践。利用水文统计学及河流动力学理论, 分析三峡水库2011—2020年实测的逐日流量数据, 结果表明: ①提出的阈值方法, 能够有效统计任一场次洪水对应库区各站的洪峰流量; ②当入库洪峰流量大于40000 m3/s时, 各支流洪水峰现时刻的异步特性对入库洪峰流量的影响相对较小; ③清溪场、万县的洪峰流量主要取决于入库洪峰流量, 庙河、黄陵庙的洪峰流量还取决于三峡水库的调度运行并随入库洪峰流量呈分段变化的趋势; ④针对不同类型的洪峰, 建立了清溪场、万县洪峰流量的计算公式。     

水库流域入库洪水预报误差分析

由于受水库枢纽工程的影响，使得反推计算的入库流量本身就存在较大的误差，从而增加了入库洪水预报模型研制的难度，影响了这时预报精度的考核。通过对全国诸多水电厂水库流域实时洪水预报的实践，对其入库洪水预报中存在的误差原因及解决方法进行了科学的分析和阐述，以指导实时预报。     

基于动库容曲线的水库调洪高水位查算方法研究

河道型水库因动库容特性显著,传统的静库容调洪方法难以适用,研究动库容调洪方法是亟待解决的问题。当坝前水位爬升到最高点时,库区内的洪水演进到达一种临界状态,库区内的水文、水力条件相对稳定,在出库流量和入库流量一定的情况下,可望获得最高坝前水位与同时刻库容之间良好的对应关系。依托三峡库区水文水动力耦合预报模型,建立了一组以出库流量、入库流量为参数的动库容曲线,以供调洪时快速查算最高坝前水位。采用2009年以来三峡水库16场场次洪水资料,检验所建动库容曲线的合理性。结果表明,各场洪水的最高库水位查算值与实况平均偏差仅0.20m,证明建立的基于动库容曲线的三峡水库最高库水位查算方法具有较好的实践价值。     

考虑水文预报误差的三峡水库防洪调度图

分析三峡水库入库流量短期预报结果,采用非参数方法估算预报相对误差的概率密度函数。以防洪为目标,同时兼顾发电、通航等要求,建立了防洪调度模型,采用差分进化算法求解,得到了汛期防洪优化调度图。通过比较不考虑预报、现有预报方案、准确预报3种情况的调度结果,分析了水文预报误差对防洪调度的影响。结果表明,无论考虑预报与否,与现有设计方案相比,汛期防洪优化调度图的发电量都增加,弃水减少,削峰率有所提高;而考虑预报信息有助于调控洪水,提高预报精度有助于增加水库抵御大洪水风险的能力;基于模拟现行作业预报方案,汛期防洪优化调度图比设计调度方案发电量增加5.3%,弃水减少5.9%,削峰率提高1.7个百分点,防御1000年一遇设计洪水的能力大大提高。     

新安江模型与水箱模型在柘溪流域适用性研究

为提高柘溪流域洪水预报精度,充分合理利用洪水资源,缓解该流域下游的防洪压力,同时为水库防洪调度以及经济运行提供科学合理的决策依据,研究了集总式概念性水文模型新安江模型及水箱模型在该湿润流域的适用性。选取该流域2004~2015年实测数据,应用三目标MOSCDE算法分别优选三水源新安江模型以及水箱模型参数,从而对该流域划分的多个子流域单元进行了场次洪水模拟计算,并对比分析不同流域单元两种模型的模拟结果,探究不同模型结构在柘溪流域场次洪水模拟中的差异,分析总结这两种模型在该流域的适用性。结果表明:两种模型模拟效果比较接近,均可以达到《水文情报预报规范》规定的作业预报精度要求,且对于流域内的大洪水的模拟效果也比较理想。从洪量以及洪水过程方面分析,两个模型的模拟效果比较接近;从洪峰拟合角度分析,新安江模型比水箱模型更适合该流域。     

基于分布式水文模拟的三峡区间洪水预报(Ⅱ)——雷达测雨应用

降雨时空分布信息缺失是影响洪水预报精度的一个重要因素,尤其是雨量站点稀疏的山区,站点观测的降雨信息难以充分地表征山区降雨的空间差异.本文针对长江三峡区间,利用雨量站点观测值来校准雷达测雨数据,并作为分布式水文模型的降雨输入,借助水文模拟结果综合评价雷达测雨数据的适用性.两个示例小流域的研究结果表明:相对于雨量站而言,雷达估测的降雨信息更能体现降雨的空间变化,它与分布式水文模型相结合,是提高洪水预报精度的有效途径.     

基于深度学习的雷达降雨临近预报及洪水预报

为探究深度学习的雷达降雨临近预报在流域洪水预报中的适用性,采用U-Net、嵌入注意力门的Attention-Unet和添加转换器的多级注意力TransAtt-Unet开展雷达降雨临近预报,将预报降雨作为HEC-HMS水文模型的输入,对柳林实验流域进行洪水预报。结果表明：1 h预见期时,Attention-Unet对短时强降雨预报结果较好,TransAtt-Unet预报降雨模拟的洪峰流量和径流量相对误差小于20%,各深度学习模型对量级较大的降雨和洪水预报精度较高;2 h预见期的预报降雨强度、降雨总量、洪峰流量和径流量存在显著低估,U-Net能取得相对较好的降雨预报结果。基于深度学习的1 h预见期雷达降雨临近预报及洪水预报可为流域防洪减灾提供科学依据。     

基于深度学习的流域洪水预报模型研究

随着水文信息系统的普及,水文数据的采集越来越方便。传统上依靠各类水文模型及参数率定以刻画水文数据和预报成果间的复杂关联,如何实现水文数据驱动的水文自动预报是当前面临的问题。近年来,人工智能技术蓬勃发展,深度学习技术开始应用于水文学领域,试图解决这一问题。本文以沮漳河西支峡口至远安区间流域为研究对象,结合分布式水文模型的原理及深度学习理论方法,基于流域多维度数据对流域产汇流特性进行提取,利用特征网络进行流域径流预报,构建基于卷积神经网络（CNN,Convolutional Neural Networks）的流域降雨径流预报模型。研究将模型模拟结果与前期影响雨量模型（API, Antecedent Precipitation Index）结果进行对比分析,结果表明,基于卷积神经网络的流域降雨径流预报模型模拟精度为90%,模型可靠,能满足大部分的降雨洪水预报,为流域降雨径流预报提供一种新的方法。     

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...     

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).     

DEM支持下的梅雨区雨量站网规划研究

雨量站网是水文站网整体功能中极其重要的一个组成部分,雨量资料的准确性和代表性,将直接影响洪水预报的精度,科学地规划和优化雨量站网,使雨量资料最大程度地反映流域的实际降雨情况,可以大大提高洪水预报系统的精度。根据水文技术发展的现状,采用抽站法,基于GIS技术,加入地形、气象等下垫面因素综合考虑,对梅雨地区雨量站网进行优化研究。     

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.     

Coupling typhoon rainfall forecasting with overland-flow modeling for early warning of inundation

Taiwan suffers from an average of three or four typhoons annually, and the inundation caused by the heavy precipitation that is associated with typhoons frequently occurs in lowlands and floodplains. Potential inundation maps have been widely used as references to set up non-structural strategies for mitigating flood hazards. However, spatiotemporal rainfall distributions must be addressed to improve the accuracy of inundation forecasting for emergency response operations. This study presents a system for 24-h-ahead early warning of inundation, by coupling the forecasting of typhoon rainfall with the modeling of overland flow. A typhoon rainfall climatology model (TRCM) is introduced to forecast dynamically the spatiotemporal rainfall distribution based on typhoon tracks. The systematic scheme for early warning of inundation based on the spatiotemporal downscaling of rainfall and 2D overland-flow modeling yields not only the extent of inundation, but also the time to maximum inundation depth. The scheme is superior to traditional early warning method referring to the maximum extent and depth of inundation determined from conditional uniform rainfall. Analytical results show that coupling TRCM with an overland-flow model yields satisfactory inundation hydrographs for warning of the extent and peak time of inundation. This study also shows that the accuracy of forecasting spatiotemporal rainfall patterns determines the performance of inundation forecasting, which is critical to emergency response operations.     

概化的Tank模型及其在龙羊峡水库汛期旬平均入库流量预报中的应用

Tank模型（又称为水箱模型），是一种用于流域径流预报的确定性水文模型，根据龙羊峡水库入库主要产流区－－黄河上游唐乃亥水文站以上流域下垫面条件下产汇流特性，将其概化为以降雨量为输入，径流量为输出的单孔出流的线性水箱，工用于该水库汛期旬平均入库流量的预报，经对历史资料进行拟合和试验预报的 结果表明，该模型具有较高的预报精度，现已应用于黄河上游龙羊峡水库汛期旬平均入库来水量的中期预报中，取得了十分显著的经济效益。     

基于水库防洪预报调度图的洪水资源化方法

为有效调节利用洪水资源,探讨以水库防洪预报调度为途径的洪水资源化方法,在分析水库调洪过程和洪水过程特性的基础上,拟定了基于3 h预报信息的水库防洪预报调度图,并以安康水库为例,采用模拟结合遗传算法进行优化计算,绘制了安康水库以洪水资源化为目标的防洪预报调度图,通过对其应用并与常规洪水调度过程进行对比分析表明,预报调度的调洪结果在消减洪峰和抬高调洪末水位两个目标上明显优于常规调度。     

An operational flood warning system for poorly gauged basins: demonstration in the Guadalhorce basin (Spain)

This paper deals with the presentation of a flood warning system (GFWS) developed for the specific characteristics of the Guadalhorce basin (3,200 km², SE of Spain), which is poorly gauged and often affected by flash and plain floods. Its complementarity with the European flood alert system (EFAS) has also been studied. At a lower resolution, EFAS is able to provide a flood forecast several days in advance. The GFWS is adapted to the use of distributed rainfall maps (such as radar rainfall estimates), and discharge forecasts are computed using a distributed rainfall–runoff model. Due to the lack of flow measurements, the model parameters calibrated on a small watershed have been transferred in most of the basin area. The system is oriented to provide distributed warnings and fulfills the requirements of ungauged basins. This work reports on the performance of the system on two recent rainfall events that caused several inundations. These results show how the GFWS performed well and was able to forecast the location and timing of flooding. It demonstrates that despite its limitations, a simple rainfall–runoff model and a relatively simple calibration could be useful for event risk management. Moreover, with low resolution and long anticipation, EFAS appears as a good complement tool to improve flood forecasting and compensate for the short lead times of the GFWS.     

宁镇地区地质灾害预报模型——以镇江润州区为例

宁镇地区是长江中下游地质灾害最严重的地区之一。镇江润州区虽然仅是宁镇地区的一个局部区域,但其气候和地质环境特征具有典型意义,所提出的地质灾害气象预警预报模型同样适用于整个宁镇地区,对长江中下游地区亦有借鉴作用。气候环境、降雨尤其是连续降雨或强降雨是诱发地质灾害的重要因素。润州区地质灾害主要与梅雨期总降雨量有关,其次与台汛期台风带来的降雨量有关,而与台汛期总降雨量无关。地质灾害预测预警方程应针对不同时期采用不同的预警模型：非梅雨期的预警方程采用预报日降雨量结合前5日降水之和的综合模型,梅雨期的预警方程采用梅雨期降雨总量模型。提出地质灾害气象预报预警等级应根据《国家突发公共事件总体应急预案》将等级统一划分为4级。该模型可作为完善我国现有地质灾害气象预警预报系统的参考。     

Evaluation of real-time flash flood forecasts for Haiti during the passage of Hurricane Tomas, November 4–6, 2010

The January 2010 earthquake that devastated Haiti left its population ever more vulnerable to rainfall-induced flash floods. A flash flood guidance system has been implemented to provide real-time information on the potential of small (~70 km²) basins for flash flooding throughout Haiti. This system has components for satellite rainfall ingest and adjustment on the basis of rain gauge information, dynamic soil water deficit estimation, ingest of operational mesoscale model quantitative precipitation forecasts, and estimation of the times of channel flow at bankfull. The result of the system integration is the estimation of the flash flood guidance (FFG) for a given basin and for a given duration. FFG is the amount of rain of a given duration over a small basin that causes minor flooding in the outlet of the basin. Amounts predicted or nowcasted that are higher than the FFG indicate basins with potential for flash flooding. In preparation for Hurricane Tomas’ landfall in early November 2010, the FFG system was used to generate 36-h forecasts of flash flood occurrence based on rainfall forecasts of the nested high-resolution North American Model of the National Centers for Environmental Prediction. Assessment of the forecast flood maps and forecast precipitation indicates the utility and value of the forecasts in understanding the spatial distribution of the expected flooding for mitigation and disaster management. It also highlights the need for explicit uncertainty characterization of forecast risk products due to large uncertainties in quantitative precipitation forecasts on hydrologic basin scales.