Flood Forecasting and Warning System: A Survey of Models and Their Applications in West Africa

Flood events occurrences and frequencies in the world are of immense worry for the stability of the economy and life safety. Africa continent is the third continent the most negatively affected by the flood events after Asia and Europe. Eastern Africa is the most hit in Africa. However, Africa continent is at the early stage in term of flood forecasting models development and implementation. Very few hydrological models for flood forecasting are available and implemented in Africa for the flood mitigation. And for the majority of the cases, they need to be improved because of the time evolution. Flash flood in Bamako (Mali) has been putting both human life and the economy in jeopardy. Studying this phenomenon, as to propose applicable solutions for its alleviation in Bamako is a great concern. Therefore, it is of upmost importance to know the existing scientific works related to this situation in Mali and elsewhere. The main aim was to point out the various solutions implemented by various local and international institutions, in order to fight against the flood events. Two types of methods are used for the flood events adaptation: the structural and non-structural methods. The structural methods are essentially based on the implementation of the structures like the dams, dykes, levees, etc. The problem of these methods is that they may reduce the volume of water that will inundate the area but are not efficient for the prediction of the coming floods and cannot alert the population with any lead time in advance. The non-structural methods are the one allowing to perform the prediction with acceptable lead time. They used the hydrological rainfall-runoff models and are the widely methods used for the flood adaptation. This review is more accentuated on the various types non-structural methods and their application in African countries in general and West African countries in particular with their strengths and weaknesses. Hydrologiska Byråns Vattenbalansavdelning (HBV), Hydrologic Engineer Center Hydrologic Model System (HEC-HMS) and Soil and Water Assessment Tool (SWAT) are the hydrological models that are the most widely used in West Africa for the purpose of flood forecasting. The easily way of calibration and the weak number of input data make these models appropriate for the West Africa region where the data are scarce and often with bad quality. These models when implemented and applied, can predict the coming floods, allow the population to adapt and mitigate the flood events and reduce considerably the impacts of floods especially in terms of loss of life.

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.

Skilful Forecasts of Summer Rainfall in the Yangtze River Basin from November

Variability in the East Asian summer monsoon(EASM)brings the risk of heavy flooding or drought to the Yangtze River basin,with potentially devastating impacts.Early forecasts of the likelihood of enhanced or reduced monsoon rainfall can enable better management of water and hydropower resources by decision-makers,supporting livelihoods and major economic and population centres across eastern China.This paper demonstrates that the EASM is predictable in a dynamical forecast model from the preceding November,and that this allows skilful forecasts of summer mean rainfall in the Yangtze River basin at a lead time of six months.The skill for May–June–July rainfall is of a similar magnitude to seasonal forecasts initialised in spring,although the skill in June–July–August is much weaker and not consistently significant.However,there is some evidence for enhanced skill following El Niño events.The potential for decadal-scale variability in forecast skill is also examined,although we find no evidence for significant variation.

Evaluation of Tianji and ECMWF high-resolution precipitation forecasts for extreme rainfall event in Henan in July 2021

The extreme rainfall event of July 17 to 22, 2021 in Henan Province, China, led to severe urban waterlogging and flood disasters. This study investigated the performance of high-resolution weather forecasts in predicting this extreme event and the feasibility of weather forecast-based hydrological forecasts. To achieve this goal, high-resolution precipitation forecasts from the Tianji weather system and the forecast system of the European Centre for Medium-Range Weather Forecasts (ECMWF) were evaluated with the spatial verification metrics of structure, amplitude, and location. The results showed that Tianji weather forecasts accurately predicted the amplitude of 12-h accumulated precipitation with a lead time of 12 h. The location and structure of the rainfall areas in Tianji forecasts were closer to the observations than ECMWF forecasts. Tianji hourly precipitation forecasts were also more accurate than ECMWF hourly forecasts, especially at lead times shorter than 8 h. The precipitation forecasts were used as the inputs to a hydrological model to evaluate their hydrological applications. The results showed that the runoff forecasts driven by Tianji weather forecasts could effectively predict the extreme flood event. The runoff forecasts driven by Tianji forecasts were more accurate than those driven by ECMWF forecasts in terms of amplitude and location. This study demonstrates that high-resolution weather forecasts and corresponding hydrological forecasts can provide valuable information in advance for disaster warnings and leave time for people to act on the event. The results encourage further hydrological applications of high-resolution weather forecasts, such as Tianji weather forecasts, in the future.

Flood Forecasting of Malaysia Kelantan River using Support Vector Regression Technique

The rainstorm is believed to contribute flood disasters in upstream catchments,resulting in further consequences in downstream area due to rise of river water levels.Forecasting for flood water level has been challenging,present-ing complex task due to its nonlinearities and dependencies.This study proposes a support vector machine regression model,regarded as a powerful machine learning-based technique to forecast flood water levels in downstream area for different lead times.As a case study,Kelantan River in Malaysia has been selected to validate the proposed model.Four water level stations in river basin upstream were identified as input variables.A river water level in downstream area was selected as output of flood forecasting model.A comparison with several bench-marking models,including radial basis function(RBF)and nonlinear autoregres-sive with exogenous input(NARX)neural network was performed.The results demonstrated that in terms of RMSE error,NARX model was better for the proposed models.However,support vector regression(SVR)demonstrated a more consistent performance,indicated by the highest coefficient of determination value in twelve-hour period ahead of forecasting time.The findings of this study signified that SVR was more capable of addressing the long-term flood forecasting problems.

Test Study on Flood Forecast by Merging Multi Precipitation Data

Shuibuya control basin in upper reaches of Qingjiang River,Hubei Province was taken as the case. By combining grouping Z-I relation with ground meteorological rainfall station,rainfall estimation by radar was calibrated,and actual average surface rainfall in the basin was calculated.By combining genetic algorithm with neural network,the corrected AREM rainfall forecast model was established,to improve rainfall forecast accuracy by AREM. Finally,AREM rainfall forecast models before and after correction were input in Xin＇an River hydrologic model for flood forecast test. The results showed that the corrected AREM rainfall forecast model could significantly improve forecast accuracy of accumulative rainfall,and decrease range of average relative error was more than 60%. Hourly rainfall forecast accuracy was improved somewhat,but there was certain difference from actual situation. Average deterministic coefficient of AREM flood forest test before and after correction was improved from -32. 60% to 64. 38%,and relative error of flood peak decreased from 39. 00% to 25. 04%. The improved effect of deterministic coefficient was better than relative error of flood peak,and whole flood forecast accuracy was improved somewhat.

The Operational Forecasting of Total Precipitation in Flood Seasons (April to September) of 5 Years (1983-1987)

Ⅰ.INTRODUCTION We have discovered that there exists a good corresponding relationship between theanomalous axes of soil temperature at a depth of 1.6m in winter (December to February) andprecipitations in following flood season (Tang et al., 1982a). We have also designed a simplethermodynamical model and applied it to the forecasting of precipitations in the flood season(Tang et al., 1982 b,c). The practical forecast started from 1975. Before 1980, however, therewere only 40-50 stations in China for measuring the soil temperature at a 1.6m depth. Since1980, the stations have been increased to a total of about 180, but no available mean valueshad been obtained from newly added stations before 1982. Therefore the analysis and map-ping of anomalies of soil temperature was not performed until 1983, and from then on theprecision of analysis has been greatly improved. The following is the actual situation of forecast in five years from 1983 to 1987.

NUMERICAL EXPERIMENTS FOR THE EFFECTS OF TWO MODEL INITIALIZATION SCHEMES ON RAINFALL FORECAST IN THE 2008 FLOODING SEASON

In this paper, based on heavy rain numerical forecast model AREM(Advanced Regional Eta Model), two different initialization schemes, LAPS and GRAPES-3DVAR, are used to run assimilation experiments of AREM-LAPS and AREM-3DVAR with the same data source(NCEP forecast field, surface data and radio-soundings) during the period from 21 May to 30 July 2008 to investigate the effect of the two initialization schemes on the rainfall simulation. The result suggests that:(1) the forecast TS score by the AREM-LAPS is higher than that by the AREM-3DVAR for rainfall in different areas, at different valid time and with different intensity, especially for the heavy rain, rainstorm and extremely heavy rain;(2) the AREM-3DVAR can generally simulate the average rainfall distribution, but the forecast area is smaller and rainfall intensity is weaker than the observation, while the AREM-LAPS significantly improves the forecast;(3) the AREM-LAPS gives a better forecast for the south-north shift of rainfall bands and the rainfall intensity variation than the AREM-3DVAR;(4) the AREM-LAPS can give a better reproduction for the daily change in the mean-rainfall-rate of the main rain band, and rainfall intensity changes in the eastern part of Southwest China, the coastal area in South China, the middle-lower valleys of Yangtze river, the Valleys of Huaihe river, and Shandong peninsula, with the rainfall intensity roughly close to the observation, while the rainfall intensity simulated by the AREM-3DVAR is clearly weaker than the observation, especially in the eastern part of Southwest China; and(5) the comparison verification between the AREM-LAPS and AREM-3DVAR for more than 10 typical rainfall processes in the summer of 2008 indicates that the AREM-LAPS gives a much better forecast than AREM-3DVAR in rain-band area, rainfall location and intensity, and in particular, the rainfall intensity forecast is improved obviously.

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。

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。

InfoWorksRS、FloodWorks软件及应用

InfoWorks RS和FloodWorks软件是英国Wallingford软件公司开发的用于实时洪水预报、预警和水资源管理软件.软件包含有关河道、漫滩、堤防以及水工建筑所有的水力学模拟计算,同时也提供了基于事件和概念性水文模型的流域降水-径流模拟,如溶雪模型、降雨径流模型、汇流模型等.软件具有图形化操作及动态显示运行结果等先进管理理念与附属工具,通过地理平面视图、剖面视图、总断面、表格及逐时变化图表等进行所有模型数据的交互,与MapInfo Professional和ArcView GIS无缝连接,可容易地将GIS软件生成的地理数据转化为InfoWorks RS和FloodWorks模型数据.本文详细介绍了软件功能及实时洪水预报配置.通过山西省汾河水库实时洪水预报系统的应用,说明了预报点、模型点的配置;河道断面、旁侧入流点和上、下游边界点的配置及预报方案的构建.最后综述了该软件的优点及有待改进之处.

GIS技术在洪水灾害中的应用研究综述

地理信息系统(GIS)是一种专用于处理地理和空间信息的计算机系统。当前,随着计算机技术的逐渐成熟,GIS技术的空间分析等特点对于水资源的规划与管理有着重要的研究意义,其中在洪水灾害中的应用是近年来研究发展的一个重点。通过GIS技术建立洪水模拟模型,进行预测预警,以此为防洪抗涝提出更加科学的决策建议,降低频发的洪涝灾害事件所带来的损失。该文针对其在洪水灾害中的预测、风险图绘制、模型建立及避险路线进行评述,提出当前存在的问题,并对今后GIS在洪水灾害中的应用进行展望。

新安江模型在金城江水文站洪水预报中的应用

本文依托中国洪水预报系统构建龙江金城江水文站洪水预报方案,基于遗传算法对新安江水文模型参数进行寻优率定,结果表明,该预报方案模型参数最优拟合效果好,方案确定性系数为0.877,分析的洪峰流量、峰现时间、洪量合格率均达乙级标准以上。利用该系统模型可在洪水作业预报中自动获取降雨及实时水文数据并进行快速分析计算,得出精准洪水预报结果,可助力龙江流域“四预”工作,为各级防汛部门提高“四情”精准防御工作提供数据支撑。

应对极端暴雨洪水与提升防洪安全韧性研究——以太湖流域为例

为应对类似于河南省郑州市“7·20”特大暴雨可能带来的灾害风险,保障极端暴雨条件下的太湖流域防洪安全,提升流域防洪韧性,将郑州“7·20”特大暴雨移植到太湖流域,采用水文水动力学耦合模型推演现状骨干防洪工程体系(太浦河、望虞河、江南运河、沿长江、沿杭州湾等)3种洪水调度条件下,太湖流域、水利分区与城市代表站应对极端暴雨洪水的响应情况。研究表明:将暴雨中心移植至太湖流域武澄锡虞区,3天累计降雨量达398.0 mm;现状调度下北部分区代表站最高水位分别较有记录以来的历史最高水位偏高0.15~0.88 m,外圈全力排水调度下北部分区水位较历史最高水位偏高0.07~0.76 m,内外圈全力排水调度下北部分区水位较历史最高水位偏高0.03~0.28 m,无锡(大)站水位峰值将会降至5.78 m,低于江南运河无锡段现状堤顶高程。研究成果有助于提升流域、区域与城市应对超标准暴雨洪水的应急处置能力,为强化太湖流域洪涝灾害风险感知、预报预警、风险研判、调度决策、响应联动等关键环节提供技术支撑和科学依据。

Seasonal Forecasts of the Summer 2016 Yangtze River Basin Rainfall

The Yangtze River has been subject to heavy flooding throughout history, and in recent times severe floods such as those in 1998 have resulted in heavy loss of life and livelihoods. Dams along the river help to manage flood waters, and are important sources of electricity for the region. Being able to forecast high-impact events at long lead times therefore has enormous potential benefit. Recent improvements in seasonal forecasting mean that dynamical climate models can start to be used directly for operational services. The teleconnection from E1 Nifio to Yangtze River basin rainfall meant that the strong E1 Nifio in winter 2015/16 provided a valuable opportunity to test the application of a dynamical forecast system. This paper therefore presents a case study of a real-time seasonal forecast for the Yangtze River basin, building on previous work demonstrating the retrospective skill of such a forecast. A simple forecasting methodology is presented, in which the forecast probabilities are derived from the historical relationship between hindcast and observations. Its performance for 2016 is discussed. The heavy rainfall in the May-June-July period was correctly forecast well in advance. August saw anomalously low rainfall, and the forecasts for the June-July-August period correctly showed closer to average levels. The forecasts contributed to the confidence of decision-makers across the Yangtze River basin. Trials of climate services such as this help to promote appropriate use of seasonal forecasts, and highlight areas for future improvements.

Seasonal Rainfall Forecasts for the Yangtze River Basin in the Extreme Summer of 2020

Seasonal forecasts for Yangtze River basin rainfall in June,May–June–July(MJJ),and June–July–August(JJA)2020 are presented,based on the Met Office GloSea5 system.The three-month forecasts are based on dynamical predictions of an East Asian Summer Monsoon(EASM)index,which is transformed into regional-mean rainfall through linear regression.The June rainfall forecasts for the middle/lower Yangtze River basin are based on linear regression of precipitation.The forecasts verify well in terms of giving strong,consistent predictions of above-average rainfall at lead times of at least three months.However,the Yangtze region was subject to exceptionally heavy rainfall throughout the summer period,leading to observed values that lie outside the 95%prediction intervals of the three-month forecasts.The forecasts presented here are consistent with other studies of the 2020 EASM rainfall,whereby the enhanced mei-yu front in early summer is skillfully forecast,but the impact of midlatitude drivers enhancing the rainfall in later summer is not captured.This case study demonstrates both the utility of probabilistic seasonal forecasts for the Yangtze region and the potential limitations in anticipating complex extreme events driven by a combination of coincident factors.

Assessment of Socio-Economic Impacts of the December 2011 Flood Event in Dar es Salaam, Tanzania

Floods are the most devastating hazards that have significant adverse impacts on people and their livelihoods. Their impacts can be reduced by investing on: 1) improving the forecasting skills of extreme and heavy rainfall events, 2) development of Impacts Based Flood Early Warning System (IBFEWS) and 3) effective communication of impacts from anticipated extreme or heavy rainfall event. The development of IBFEWS however, requires a complete understanding of factors that relates to the formation of extreme or heavy rainfall events and their associated socio-economic impacts. This information is crucial in the development of Impacts Based Flood Forecasting Models (IBFFMs). In this study, we assess the socio-economic impacts of the December 2011 flood event in Dar es Salaam as the preliminary stage in the development of IBFFMs for the City of Dar es Salaam. Data from household survey collected using systematic random sampling techniques and structured questionnaires are used. The survey was conducted to acquire respondent’s views on the causes of floods impacts, adaptive capacity to extreme or heavy rainfall events and adaptation options to minimize flood impact. It is found that the main causes of floods were river overflow due to heavy rainfall and blocked drainage system. Poor infrastructure such as drainage and sewage systems, and ocean surge were identified to be the causes of observed impacts of the December 2011 flood event in Dar es Salaam. Death cases analysis showed that 43 people were reported dead. The flood event damaged properties worth of 7.5 million Tanzania shillings. Furthermore, the Tanzania Government spent a total amount of 1.83 billion Tanzanian shillings to rescue and relocate vulnerable communities that lived-in low-lying areas of Jagwani to high ground areas of Mabwepande in Kinondoni district.

考虑产流模式空间分布的流域-城市复合系统洪水预报模型

为解决流域-城市复合系统洪水协同预报问题,本文将流域和城市纳入统一空间范畴,在提出易发生产流模式辨析框架的基础上,耦合网格产流计算模型(GRGM)和长短时记忆神经网络(LSTM)构建了GRGM-LSTM洪水预报混合模型。以贾鲁河中牟站控制流域为例,基于18场实测洪水进行模型检验,同时将预报结果与暴雨洪水管理模型(SWMM)、GRGM-SWMM模型进行对比分析。研究表明:①GRGM模型模拟产流量相对误差、决定性系数平均值分别为8.41%、0.976,考虑产流模式空间分布的产流计算更为准确;②预见期小于6 h时,GRGM-LSTM混合预报模型纳什效率系数大于0.8,比GRGM-SWMM、SWMM等物理机制模型具有更好的模拟性能;③预见期大于6 h时,GRGM-LSTM混合模型出现一定的精度损失,预见期增至12 h时,GRGM-SWMM模拟精度高于GRGMLSTM模型。研究成果可为流域-城市防洪减灾协同管理提供科学依据。

太湖流域超标特大洪水风险预警系统建设及应用

为做好超标洪水调度、保障流域防洪安全,开发建设了由基于水文水动力学耦合的超警超保风险区域预警模型与基于水文学法的洪水淹涝风险快速评估模型共同组成的超标特大洪水风险预警系统,该系统在太湖流域预报调度一体化系统中通过智能交互方式进行模型与系统的紧密集成,实现了预报调度成果的可视化。基于水利一张图的超警超保与淹涝动态展示,实现了预报产品从点到面、从常规预报到影响预测的突破,以及洪水风险由静态评估向实时快速动态分析的转变。应用结果表明:系统在2020年太湖流域性大洪水中累计发布超警超保风险提示39期、洪水淹涝风险评估4期,预测结果与实际基本一致,为科学调度防御超标洪水提供了技术支撑,避免了江苏省苏州市3万多的人员转移,有力保障了太湖流域防洪安全。