Analysis of Forecast and Early Warning of Flood in Medium and Small Rivers

Flooding of small and medium rivers is caused by environmental factors like rainfall and soil loosening.With the development and application of technologies such as the Internet of Things and big data,the disaster supervision and management of large river basins in China has improved over the years.However,due to the frequent floods in small and medium-sized rivers in our country,the current prediction and early warning of small and medium-sized rivers is not accurate enough;it is difficult to realize real-time monitoring of small and medium-sized rivers,and it is also impossible to obtain corresponding data and information in time.Therefore,the construction and application of small and medium-sized river prediction and early warning systems should be further improved.This paper presents an analysis and discussion on flood forecasting and early warning systems for small and medium-sized rivers in detail,and corresponding strategies to improve the effect of forecasting and early warning systems are proposed.

Mechanism and Forecasting Methods for Severe Droughts and Floods in Songhua River Basin in China

The influence of various factors, mechanisms, and principles affecting runoff are summarized as periodic law, random law, and basin-wide law. Periodic law is restricted by astronomical factors, random law is restricted by atmospheric circulation, and basin-wide law is restricted by underlying surface. The commensurability method was used to identify the almost period law, the wave method was applied to deducing the random law, and the precursor method was applied in order to forecast runoff magnitude for the current year. These three methods can be used to assess each other and to forecast runoff. The system can also be applied to forecasting wet years, normal years and dry years for a particular year as well as forecasting years when floods with similar characteristics of previous floods, can be expected. Based on hydrological climate data of Baishan (1933-2009) and Nierji (1886-2009) in the Songhua River Basin, the forecasting results for 2010 show that it was a wet year in the Baishan Reservoir, similar to the year of 1995; it was a secondary dry year in the Nierji Reservoir, similar to the year of 1980. The actual water inflow into the Baishan Reservoir was 1.178 × 10 10 m 3 in 2010, which was markedly higher than average inflows, ranking as the second highest in history since records began. The actual water inflow at the Nierji station in 2010 was 9.96 × 10 9 m 3 , which was lower than the average over a period of many years. These results indicate a preliminary conclusion that the methods proposed in this paper have been proved to be reasonable and reliable, which will encourage the application of the chief reporter release system for each basin. This system was also used to forecast inflows for 2011, indicating a secondary wet year for the Baishan Reservoir in 2011, similar to that experienced in 1991. A secondary wet year was also forecast for the Nierji station in 2011, similar to that experienced during 1983. According to the nature of influencing factors, mechanisms and forecasting methods and the service objects, mid-to long-term hydrological forecasting can be divided into two classes:mid-to long-term runoff forecasting, and severe floods and droughts forecasting. The former can be applied to quantitative forecasting of runoff, which has important applications for water release schedules. The latter, i.e., qualitative disaster forecasting, is important for flood control and drought relief. Practical methods for forecasting severe droughts and floods are discussed in this paper.

Development and Application of an Atmospheric-HydrologicHydraulic Flood Forecasting Model Driven by TIGGE Ensemble Forecasts

A coupled atmospheric-hydrologic-hydraulic ensemble flood forecasting model, driven by The Observing System Research and Predictability Experiment （THORPEX） Interactive Grand Global Ensemble （TIGGE） data, has been developed for flood forecasting over the Huaihe River. The incorporation of numerical weather prediction （NWP） information into flood forecasting systems may increase forecast lead time from a few hours to a few days. A single NWP model forecast from a single forecast center, however, is insufficient as it involves considerable non-predictable uncertainties and leads to a high number of false alarms. The availability of global ensemble NWP systems through TIGGE offers a new opportunity for flood forecast. The Xinanjiang model used for hydrological rainfall-runoff modeling and the one-dimensional unsteady flow model applied to channel flood routing are coupled with ensemble weather predictions based on the TIGGE data from the Canadian Meteorological Centre （CMC）, the European Centre for Medium-Range Weather Forecasts （ECMWF）, the UK Met Office （UKMO）, and the US National Centers for Environmental Prediction （NCEP）. The developed ensemble flood forecasting model is applied to flood forecasting of the 2007 flood season as a test case. The test case is chosen over the upper reaches of the Huaihe River above Lutaizi station with flood diversion and retarding areas. The input flood discharge hydrograph from the main channel to the flood diversion area is estimated with the fixed split ratio of the main channel discharge. The flood flow inside the flood retarding area is calculated as a reservoir with the water balance method. The Muskingum method is used for flood routing in the flood diversion area. A probabilistic discharge and flood inundation forecast is provided as the end product to study the potential benefits of using the TIGGE ensemble forecasts. The results demonstrate satisfactory flood forecasting with clear signals of probability of floods up to a few days in advance, and show that TIGGE ensemble forecast data are a promising tool for forecasting of flood inundation, comparable with that driven by raingauge observations.

Ensemble flood predictions for River Thames under climate change

In this study,a Bayesian model averaging(BMA)-based ensemble modeling system is proposed to project future flood occurrences for the River Thames using downscaled high-resolution climate projections from the latest general circulation models(GCMs)in the Coupled Model Intercomparison Project Phase 6(CMIP6).The BMA-based ensemble modeling system integrates multiple hydrological models into the BMA framework to enhance the accuracy of hydrological forecasting,which has shown good performance in validation with the NSE higher 0.91,KGE approaching 0.80,and correlation coefficient higher than 0.96.Daily projections of precipitation and temperature under all four shared socioeconomic pathways were obtained from three GCM models and were further employed to project future potential evaporation.The BMA-based ensemble modeling system was then used to forecast annual maximum flood rates and associated 3-day maximum flood volumes in the future.Our results show that the three GCM models exhibit considerable differences in terms of future flood projections,but all indicate a general increase in flood occurrence and magnitude under future climate change scenarios.The future daily flood events under different climate scenarios are likely to become more severe,as indicated by higher mean,maximum,and 90th quantile values of the AMAX flood series.Meanwhile,the corresponding 3-day flood volumes show varying patterns in terms of mean and extreme flood volumes under different scenarios,but we would have more chances to experience severe 3-day flood volumes in future.The results of our study can provide important information for flood risk management and adaptation planning in the River Thames basin.

基于GPU并行计算和WebGIS的潖江蓄滞洪区洪水预报系统研究

洪水预报所采用的数值模拟涉及大量计算,模拟的结果需经多种专用软件处理后才能展示给用户,操作繁琐,无法满足蓄滞洪区防洪调度及应急抢险处置中迅速做出响应的需求。该文提出了基于GPU并行计算和WebGIS的洪水预报系统,旨在提高洪水计算的效率,延长预见期,并实现洪水演进的可视化。该系统基于最新的GPU加速的计算方法,利用GPU强大的浮点数运算能大幅提高洪水计算的效率,结合WebGIS技术,将水文-洪水演进模型的计算结果与水利底图无缝连接,以图表、图像和动画等形式直观展示洪水演变过程,使决策人员能直观地掌握蓄滞洪区洪水的演变过程,可为潖江蓄滞洪区的调度运用和防洪抢险提供帮助。

洪水预报在蓄滞洪区启用条件中的应用研究

针对蓄滞洪区的启用涉及到的蓄滞洪区内人员和财产转移等诸多问题,本文将洪水预报引入蓄滞洪区启用的条件中,通过构建一套河系预报,可提前8 d预报出江桥站的流量是否达到启用蓄滞洪区的标准,并通过分级启用蓄滞洪区的方式,为决策和人员财产提供了时间的保障。同时文中还分析了各主要站点预报方案的精度和拉林河洪水的影响,以确保预报的可靠性和蓄滞洪区启用标准的合理性。

红水河龙滩水电站区间河系洪水预报方案研究

龙滩水电站是珠江流域防洪控制性工程,但目前缺乏较完整的河系洪水预报方案。为充分发挥龙滩水电站的防洪调蓄作用,选取了雷公滩、沫阳、平里河、平湖和仙人桥等支流控制站点,通过重要子区间分析确定重点关注区域,利用新安江三水源产流模型、三水源滞后演算汇流模型和马斯京根汇流演算法构建龙滩水电站区间河系洪水预报方案。研究结果表明:龙滩水电站最大入库洪水洪量占比通常为无控区间,其次为贵州区支流(蒙江、坝王河、曹渡河、六硐河),两者是洪水预报的关注重点;本研究构建的河系洪水预报方案整体洪峰流量、场次洪量的平均相对误差均为10%,平均确定性系数均在0.75以上,总体结果较好。因此,构建的龙滩水电站区间河系洪水预报方案能够应用于实时洪水作业预报中,也为进一步提升“四预”能力筑牢基础。

基于MIKE FLOOD的中小河流溃堤洪水风险分析

近年来洪水引发的中小河流堤防溃决等洪水灾害风险问题凸显,因此进行溃堤洪水风险分析对于加强中小河流的洪水管理及减少溃堤洪水带来的损失具有十分重要的意义。以江西省罗塘河为例,借助MIKE软件中的MIKE 11、MIKE 21及其耦合模块对罗塘河遭遇10a一遇及20a一遇洪水进行溃堤洪水演进模拟。然后依据灾害系统理论从洪水的危险性和易损性两方面选择淹没水深、淹没流速、淹没历时等7个指标构建溃堤洪水风险评价指标体系。最后利用GIS技术与层次分析法对罗塘河洪水风险进行了评价。结果表明:洪水危险区面积为0.19 km 2,占研究区总面积的2.18%,主要分布在地势低洼的富港地区;重灾区和中灾区面积为1.25 km 2,占研究区总面积的14.37%,主要分布在重文和蒋元乐家;安全区为研究区域内洪水没有到达并且地物覆盖价值较低的地区,包括游家店、下胡、大塘杨家和马山等处。研究成果可为中小河流防洪规划、避洪转移等提供参考依据。

基于循环神经网络的山前平原型城市河道洪水预报研究

山前平原型城市拥有复杂的下垫面条件,其洪水兼具了山洪和城市洪水的特征,为水文模拟和洪水预报增加了难度。以小清河济南市黄台桥水文站以上流域为研究对象,构建了基于循环神经网络变体的洪水预报模型,并评估了模型的预测性能。研究结果表明,所构建的洪水预报模型既适用于对场次洪水的预报,又适用于对长系列洪水过程的连续预测,且能够灵活地输出流量、水位过程,在一定预测步长内拥有较高的预测精度,其中基于双向门控循环单元(Bidirectional Gate Recurrent Unit,BiGRU)网络构建的模型预测性能最佳,且随预测步长的延长,其性能衰减最弱,能够作为山前平原型城市河道洪水预报的新方法新手段。

河网地区洪水预报模型开发及在长江下游地区的应用

平原水网地区的洪水预报一直是水文学科的难点问题,尤其受人类活动影响剧烈。为了更好地模拟流域水文循环过程,解决工程实际应用中的挑战,提出了一种新颖的分布式架构水文模型。该模型具有分布式特征,能够根据流域特征和需求灵活选择最合适的水文特征单元进行组合,从而支持对不同水文特征单元精确化、理论化的研究。为了实现实时预报功能,还研发了数据库耦合技术、大型河网实时校正技术及水利工程调度技术。在此基础上,应用分布式架构思想建立了太湖流域模型,对其进行了概化处理,并选取2016年实况数据对模型参数进行了率定。通过对太湖水位1 d、2 d、3 d预见期的实时预报,计算成果基本能够反映平原水网地区太湖流域的水流运动实际状况。综合而言,该分布式架构水文模型方法不仅在科学性上得到验证,而且在实现技术上具有可行性,成功地解决了从理念到现实的应用实践问题,为平原水网地区洪水预报提供了新的思路和方法。

基于CNN-LSTM-Attention和自回归的混合水位预测模型

【目的】水位预测对交通运输、农业以及防洪措施具有重要影响。精确的水位值可用于提升水道运输的安全及效率、降低洪水风险,同时也是保障区域可持续发展的必要条件。【方法】提出一种CRANet的混合水位预测模型,以卷积神经网络(CNN)、长短期记忆网络(LSTM)、注意力机制以及自回归(AR)组件为基础,旨在应对时间序列数据中存在的线性与非线性问题,缓解自回归及ARIMA模型的缺陷。其应用不仅在于为航运调度提供决策支撑,加强导航安全效率,同样能提升防洪减灾的能力。其中,CNN和LSTM组件有效地针对数据集内的局部和全局关系进行捕捉,AR组件则能充分考虑数据的时间序列特性。同时,通过注意力机制,模型能够优先考虑相关特性,提高预测效果。【结果】研究成果所提出的模型已成功应用于中国西江梧州站的水位预测,在测试集上预测未来3 h级别水位的MAE、RMSE和R^(2)分别为0.086、0.114 5和0.950 8。【结论】结果表明所提出的CRANet模型在水位预测方面的高可用性、准确度与稳健性,相较于AR、SVR、CNN、LSTM等模型具有更优的MAE、RMSE和R^(2)。

概念性水文模型与智能模型在中小河流洪水模拟中的比较研究

相较于大江大河的流域水文预报研究,中小流域的研究相对匮乏。以沅江河溪水文站以上流域为例,研究LSTM(Long Short-Term Memory)模型和新安江模型在场次洪水中的模拟效果。通过对比,新安江模型的整体模拟精度较高,洪量、洪峰、峰现时间的平均相对误差分别为9.39%、9.55%、1.6h,确定性系数为0.73,综合合格率为100%,达到甲级精度标准;LSTM模型的模拟精度较低,洪量、洪峰、峰现时间的平均相对误差分别为11.76%、12.33%、2.3 h,确定性系数为0.60,综合合格率为75%,达到乙级精度标准。结果表明,新安江模型和LSTM模型是中小河流洪水预报的有效方法,均可用于河溪流域的正式预报,且对于河溪流域,新安江模型的模拟精度比LSTM模型更高。

考虑径流过程矢量化的机器学习洪水预报模型

准确的超前洪水预报有利于开展防洪减灾工作和优化水资源调度。本文提出一种针对场次洪水的径流过程矢量化方法(Runoff Process Vectorization, RPV),并耦合3种机器学习(Machine Learning, ML)模型构建了RPV-ML洪水预报系列模型。以黄河中上游孤山川、佳芦河和祖厉河3个典型流域为研究区,分别基于43、28、37场洪水的降雨径流数据,按照洪水场次7∶3的比例进行模型训练和验证。研究表明:(1)相同预见期条件下RPV-ML模型在孤山川、佳芦河和祖厉河流域洪水预报纳什效率系数更高、均方根误差和洪峰相对误差更低,RPV-ML模型比ML模型具有更好的预报性能,在预见期为4~6 h时优势更显著;(2) RPV-ML和ML模型预报精度会随着预见期增加逐渐下降,但RPV-ML预报精度呈现缓慢下降趋势,具有更好的鲁棒性;(3)基于RPV改进的时间卷积网络(Temporal Convolutional Network, TCN)可以更好地克服预报误差问题,RPV-TCN模型在3个流域预报性能最好。

浦阳江流域感潮河段洪水预报研究

浦阳江流域中上游属感潮河段,易受下游洪水顶托影响,导致预报站水位流量关系不稳定,给洪水预报作业带来难度。以诸暨站以上流域范围为研究对象,选用1990—2021年间具有代表性的20场洪水,建立三水源新安江模型,对诸暨站流量进行预报;基于BP神经网络优良的非线性映射能力,建立以湄池水位、时段降雨量、安华水库下泄流量、诸暨站预报流量等为输入因子的诸暨站水位预报模型,以探究该模型在诸暨洪峰水位预报中的适用性。结果证明2019—2021年的场次洪峰水位预报效果较优,场次水位相对误差均保持在2.5%以内。

基于水动力学和GIS空间分析的洪水预演模拟研究

洪水演进模拟的准确性是洪水“预演”有效性的关键。针对流域洪水预演模型计算精度低、普适性弱、缺实测资料等问题,提出了基于水动力学和GIS空间分析的洪水预演模型,模型采用一维水动力学并耦合GIS空间D8扩散算法进行洪水预演模拟,采用综合风险度模型进行风险度计算。以台兰河流域验证耦合模型性能。结果表明,提出的水动力学和GIS空间分析的洪水预演模型能够较好保证模拟精度,流域内极高风险地区面积为4.92 km~2。研究结果对指导洪水“预案”具有重要应用价值。

HEC-HMS模型在江西省山区中小河流洪水预报中的适用性研究

山区洪水暴涨陡落,常造成严重经济损失和人员伤亡,实现中小河流洪水预报在防洪减灾中十分重要。为探究HEC-HMS分布式水文模型在江西省山区中小河流的适用性,以蜀水流域为研究区,构建了基于HEC-HMS的蜀水流域分布式水文模型,选用2013-2019年间的12场降雨对模型进行参数率定、敏感性分析和洪水过程模拟。结果表明:模型参数中CN值为最敏感参数;模型预测值与实测值结果显示洪峰流量和峰现时间合格率均为91.67%,径流深合格率为100%,确定性系数均高于0.7;HEC-HMS模型在蜀水流域中有较好的模拟效果,可为江西省山区中小河流域洪水预报提供参考。

基于流溪河模型的水库洪水预报方案

中国是世界上洪水灾害发生最频繁的国家之一。建立准确可靠的洪水预报程序是应对洪涝灾害的关键。中国南方的某水库流域属于亚热带季风气候,春季雨水较多,夏季暴雨集中,秋季有台风。坝址洪水多为中小型洪水,具有典型的山洪上升陡、下降缓的特点。为了探索流溪河模型在某水库洪水预测中的适用性,基于DEM、土地利用和土壤类型数据,建立了水库流溪河预测模型,并选取1个典型的洪水事件,利用粒子群优化算法对参数进行优化,并通过模拟其他洪水验证了模型的准确性。

基于PSO算法的新安江模型在永宁河洪水预报中的应用研究

根据嘉陵江一级支流永宁河流域的气候特征,利用永宁镇水文站2002-2021年连续20年的逐日水文观测资料,基于粒子群算法(PSO)对新安江模型进行参数率定,基于此对永宁镇水文站日流量进行模拟,探讨该模型在永宁镇水文站洪水预报中的应用。结果表明:新安江模型在永宁镇水文站径流模拟中的成果较好,率定期确定性系数平均值为0.53,合格率为71%,验证期验证期确定性系数平均值为0.59,合格率为100%,模拟成果合格率达到乙级,确定性系数达到丙级预报精度,符合规范要求,可用于该站的洪水预报,为该站洪水预报提出了新的方向,并为新安江模型在嘉陵江上游应用积累了经验。

不同优化算法在新安江模型参数率定中的效果评估

针对水文模拟预报中需要寻求实用、稳健、高效的优化算法并据此确定参数的全局最优解的问题,以綦江流域典型控制断面五岔断面为研究对象,分别选取GA、SCE-UA、CMA-ES、PSO等4种优化算法进行分布式新安江模型汇流参数率定,并从有效性、稳定性、耗时和效率4个方面评估算法的参数率定性能。结果表明:CMA-ES算法优化效果最佳,相同迭代次数下耗时最少;SCE-UA算法稳定性和效率最高,但耗时随着迭代次数增大而显著增加;PSO算法有效性和效率相对较优,但稳定性最差;GA算法耗时相对较少,优化效果和效率最差;综合性能从优到劣依次为CMA-ES算法、SCE-UA算法、PSO算法、GA算法。