River channel flood forecasting method of coupling wavelet neural network with autoregressive model

Based on analyzing the limitations of the commonly used back-propagation neural network （BPNN）, a wavelet neural network （WNN） is adopted as the nonlinear river channel flood forecasting method replacing the BPNN. The WNN has the characteristics of fast convergence and improved capability of nonlinear approximation. For the purpose of adapting the timevarying characteristics of flood routing, the WNN is coupled with an AR real-time correction model. The AR model is utilized to calculate the forecast error. The coefficients of the AR real-time correction model are dynamically updated by an adaptive fading factor recursive least square（RLS） method. The application of the flood forecasting method in the cross section of Xijiang River at Gaoyao shows its effectiveness.

Real-time flood forecasting of Huai River with flood diversion and retarding areas

A combination of the rainfall-runoff module of the Xin’anjiang model, the Muskingum routing method, the water stage simulating hydrologic method, the diffusion wave nonlinear water stage method, and the real-time error correction method is applied to the real-time flood forecasting and regulation of the Huai River with flood diversion and retarding areas. The Xin’anjiang model is used to forecast the flood discharge hydrograph of the upstream and tributary. The flood routing of the main channel and flood diversion areas is based on the Muskingum method. The water stage of the downstream boundary condition is calculated with the water stage simulating hydrologic method and the water stages of each cross section are calculated from downstream to upstream with the diffusion wave nonlinear water stage method. 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 faded-memory forgetting factor least square of error series is used as the real-time error correction method for forecasting discharge and water stage. As an example, the combined models were applied to flood forecasting and regulation of the upper reaches of the Huai River above Lutaizi during the 2007 flood season. The forecast achieves a high accuracy and the results show that the combined models provide a scientific way of flood forecasting and regulation for a complex watershed with flood diversion and retarding areas.

Forecast of Flood in Chaohu Lake Basin of China Based on Grey-Markov Theory

Flood is one kind of unexpected and the most common natural disasters, which is affected by many factors and has complex mechanism. At home and abroad, there is still no mature theory and method used for the long-term forecast of natural precipitation at present. In the present paper the disadvantages of grey GM (1, 1) and Markov chain are ana- lyzed, and Grey-Markov forecast theory about flood is put forward and then the modifying model is developed by making prediction of Chaohu Lake basin. Hydrological law was conducted based on the theoretical forecasts by grey system GM (1, 1) forecast model with improved Markov chain. The above method contained Stat-analysis, embodying scientific approach, precise forecast and its reliable results.

Rainfall-runoff simulation and flood forecasting for Huaihe Basin

The main purpose of this study was to forecast the inflow to Hongze Lake using the Xin＇anjiang rainfall-runoff model. The upper area of Hongze Lake in the Huaihe Basin was divided into 23 sub-basins, including the surface of Hongze Lake. The influence of reservoirs and gates on flood forecasting was considered in a practical and simple way. With a one-day time step, the linear and non-linear Muskingum method was used for channel flood routing, and the least-square regression model was used for real-time correction in flood forecasting. Representative historical data were collected for the model calibration. The hydrological model parameters for each sub-basin were calibrated individually, so the parameters of the Xin＇anjiang model were different for different sub-basins. This flood forecasting system was used in the real-time simulation of the large flood in 2005 and the results are satisfactory when compared with measured data from the flood.

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.

Nash Model Parameter Uncertainty Analysis by AM-MCMC Based on BFS and Probabilistic Flood Forecasting

A hydrologic model consists of several parameters which are usually calibrated based on observed hy-drologic processes. Due to the uncertainty of the hydrologic processes, model parameters are also uncertain, which further leads to the uncertainty of forecast results of a hydrologic model. Working with the Bayesian Forecasting System (BFS), Markov Chain Monte Carlo simulation based Adaptive Metropolis method (AM-MCMC) was used to study parameter uncertainty of Nash model, while the probabilistic flood forecasting was made with the simu-lated samples of parameters of Nash model. The results of a case study shows that the AM-MCMC based on BFS proposed in this paper is suitable to obtain the posterior distribution of the parameters of Nash model according to the known information of the parameters. The use of Nash model and AM-MCMC based on BFS was able to make the probabilistic flood forecast as well as to find the mean and variance of flood discharge, which may be useful to estimate the risk of flood control decision.

Development of Flood Forecasting System Using Statistical and ANN Techniques in the Downstream Catchment of Mahanadi Basin, India

The floods in river Mahanadi delta are due to either dam release of Hirakud or due to contribution of intercepted catchment between Hirakud dam and delta. It is seen from post-Hirakud periods (1958) that out of 19 floods 14 are due to intercepted catchment contribution. The existing flood forecasting systems are mostly for upstream catchment, forecasting the inflow to reservoir, whereas the downstream catchment is devoid of a sound flood forecasting system. Therefore, in this study an attempt has been made to develop a workable forecasting system for downstream catchment. Instead of taking the flow time series concurrent flood peaks of 12 years of base and forecasting stations with its corresponding travel time are considered for analysis. Both statistical method and ANN based approach are considered for finding the peak to reach at delta head with its corresponding travel time. The travel time has been finalized adopting clustering techniques, there by differentiating high, medium and low peaks. The method is simple and it does not take into consideration the rainfall and other factors in the intercepted catchment. A comparison between both methods are tested and it is found that the ANN methods are better beyond the calibration range over statistical method and the efficiency of either methods reduces as the prediction reach is extended. However, it is able to give the peak discharge at delta head before 24 hour to 37 hour for high to low peaks.

Flood Forecasting GIS Water-Flow Visualization Enhancement (WaVE): A Case Study

Riverine flood event situation awareness and emergency management decision support systems require accurate and scalable geoanalytic data at the local level. This paper introduces the Water-flow Visualization Enhancement (WaVE), a new framework and toolset that integrates enhanced geospatial analytics visualization (common operating picture) and decision support modular tools. WaVE enables users to: 1) dynamically generate on-the-fly, highly granular and interactive geovisual real-time and predictive flood maps that can be scaled down to show discharge, inundation, water velocity, and ancillary geomorphology and hydrology data from the national level to regional and local level;2) integrate data and model analysis results from multiple sources;3) utilize machine learning correlation indexing to interpolate streamflow proxy estimates for non-functioning streamgages and extrapolate discharge estimates for ungaged streams;and 4) have time-scaled drill-down visualization of real-time and forecasted flood events. Four case studies were conducted to test and validate WaVE under diverse conditions at national, regional and local levels. Results from these case studies highlight some of WaVE’s inherent strengths, limitations, and the need for further development. WaVE has the potential for being utilized on a wider basis at the local level as data become available and models are validated for converting satellite images and data records from remote sensing technologies into accurate streamflow estimates and higher resolution digital elevation models.

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.

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.

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.

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.

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.

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。

THE PRELIMINARY STUDY ON POSSIBLE SCENARIOS OF FLOOD AND DROUGHT IN CHINA IN THE CASE OF GLOBAL WARMING

According to Prof. Zhu Kezhen’s(Chu K.C.)historical climatic division,the last 500 years in China can be divided into several alternately cold and warm periods.The periods of 1470-1520,1620-1720,1840-1890 had cold winters,while those of 1550-1600,1770-1830 had warm winters.Based on such division,in four kinds of periods,i.e.cold, warm,cold-warm,and warmcold (transition period),the differences between flood/drought degree in 120 stations in China and average of flood/drought degree in the last 500 years have been calculated. Positive anomaly indicates drought-prone area,while negative anomaly indicates flood-prone area. This historical experience provides a background to analyze the possible scenarios in the case of global warming in the future.The final results suggest that in the case of global warming the hazards of flood probably increase in many parts of China,such as southeast coast area,southwest,northwest, some parts of northeast and inner Mongolia while the hazards of drought probably decrease in the North China Plain,the middle reaches of the Huanghe River and its southern adjacent area. This distribution is basically consistent with that of precipitation in warming periods in this century and that resulted from climatic model in the case of CO2 doubling.

A New Material Balance Equation Model for Analyzing Dynamic Performance of C_2 Flooding

Implementing a CO2 flooding scheme successfully requires the capacity to get accurate information of reservoir dynamic performance and fluids injected. Despite some numerical simulation studies, the complicated drive mechanisms and actual reservoir performance have not been fully understood. There is a strong need to develop models from different perspectives to complement current simulators and provide valuable insights into the reservoir performance during CO2 flooding. The aim of this study is to develop a model by using an improved material balance equation （MBE） to analyze quickly the performance of CO2 flooding. After matching the historical field data the proposed model can be used to evaluate, monitor and predict the overall reservoir dynamic performance during CO2 flooding. In order to account accurately for the complex displacement process involving compositional effect and multiphase flow, the PVT properties and flowability of reservoir fluids are incorporated in the model. This study investigates the effects of a number of factors, such as reservoir pressure, the amount of CO2 injected, the CO2 partition ratios in reservoir fluids, the possibility of the existence of a free CO2 gas cap, the proportion of reservoir fluids contacted with CO2, the starting time of CO2 flooding, oil swelling, and oil flowability improvement by mixing with CO2. The model was used to analyze the CO2 flooding project in Weyburn oil field, Saskatchewan, Canada. This study shows that the proposed model is an effective complementary tool to analyze and monitor the overall reservoir performance during CO2 flooding.

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.

Modeling the Effect of Irrigation Practices in Flash Floods: A Case Study for the US Southwest

Conventional streamflow forecasting does not generally take into account the effects of irrigation practice on the magnitude of floods and flash floods. In this paper, we report the results of a study in which we modeled the impacts of an irrigated area in the US Southwest on streamflow. A calibrated version of the Variable Infiltration Capacity model (VIC), coupled with a routing algorithm, was used to investigate two strategies for irrigating alfalfa in the Beaver Creek watershed (Arizona, USA), for the period January to March of 2010, at a resolution of 1.8 km and hourly time step. By incorporating the effects of irrigation in artificially maintaining soil moisture, model performance is improved without requiring changes in the resolution or quality of input data. Peak flows in the watershed were found to increase by 10 to 500 times, depending on the irrigation scenario, as a function of the strategy and the intensity of rainfall. The study suggests that both flood control and irrigation efficiency could be enhanced by applying improved irrigation techniques.

Deep learning for cross-region streamflow and flood forecasting at a global scale

Streamflow and flood forecasting remains one of the long-standing challenges in hydrology.Traditional physically based models are hampered by sparse parameters and complex calibration procedures particularly in ungauged catchments.We propose a novel hybrid deep learning model termed encoder-decoder double-layer long short-term memory(ED-DLSTM)to address streamflow forecasting at global scale for all(gauged and ungauged)catchments.Using historical datasets,ED-DLSTM yields a mean Nash-Sutcliffe efficiency coefficient(NSE)of 0.75 across more than 2,000 catchments from the United States,Canada,Central Europe,and the United Kingdom,highlighting improvements by the state-of-the-art machine learning over traditional hydrologic models.Moreover,ED-DLSTM is applied to 160 ungauged catchments in Chile and 76.9%of catchments obtain NSE>0 in the best situation.The interpretability of cross-region capacities of ED-DLSTM are established through the cell state induced by adding a spatial attribute encoding module,which can spontaneously form hydrological regionalization effects after performing spatial coding for different catchments.The study demonstrates the potential of deep leaning methods to overcome the ubiquitous lack of hydrologic information and deficiencies in physical model structure and parameterization.