Real-time Flood Forecasting System Research Articles

Multiscale and multi event evaluation of short-range real-time flood forecasting in large metropolitan areas

Many large metropolitan areas are especially susceptible to floods generated by heavy, short-duration rainfall. The high density of people, buildings and infrastructure in these areas underline the importance of developing flood resilient cities and communities. An accurate real-time flood forecasting system can support decision making for launching preparedness and response actions in short-range (hours to days), and assist mitigating the disturbances caused by floods. This study explores the short-range predictive capability of a real-time flood forecast system by coupling the High-Resolution Rapid Refresh (HRRR) meteorological forecasted variables with a fully distributed hydrological model (WRF-Hydro). We provide a comprehensive analysis of short-range (36 h) forecasts for 19flood events generated by heavy rainfall in small urban and suburban watersheds (<200 km2) in the National Capital Region of the United States. Flood forecast performance are then assessed using different metrics based on observed data from U.S. Geological Survey stream gauges and reanalysis simulations using the StageIV quantitative precipitation estimates. Results show that despite the high variability of the HRRR cycle-to-cycle precipitation forecasts, the real-time flood forecast system can produce skillful forecasts with similar overall performance as the reanalysis simulations, achieving 65 % of flood detection rate. This variability had more impact on forecast skill in smaller subbasins, and flood forecasts were more consistent for longer duration and larger spatial extent rainfall. Hourly flood forecasts performed well even in smaller watersheds, correctly detecting flood occurrence with up to 34 h lead time and resulting in low peak flow magnitude and timing errors.

Real-Time Flood Warning System Application

The reliability of weather radar data in real-time flood forecasting and early warning system remain ambivalent due to high uncertainty in Quantitative Precipitation Forecasts (QPF). In this study, a methodology is presented with the objective to improve the flood forecasting results with the application of radar rainfall calculated in three different ways. The QPF radar rainfall forecast data of four typhoon events in Fèngshān River Basin, Taiwan, were simulated using the WASH123D numerical model. The simulated results were corrected using a physical real-time correction technique and compared with direct simulation without correction for all three QPF calculation methods. According to model performance evaluation criteria, in the third method of QPF calculation, flood peak error was the lowest in all three methods, indicating better results for flood forecasting and can be used for flood early warning systems. The impact of the real-time correction technique was assessed using mass balance analysis. It was found that flow change is between 16% and 42% from direct simulation, indicating being on the safe side in case of a flood warning. However, the impact of the real-time physical correction on the water level itself is in a reasonable range. Still, QPF rainfall correction/calculation is more important to obtain accurate results for flood forecasting. Therefore, the application of real-time correction to correct the model water level has a certain degree of credibility, which is the mass balance of the model. This approach is recommended for flood forecasting early warning systems.

Development and Application of a Real-Time Flood Forecasting System (RTFlood System) in a Tropical Urban Area: A Case Study of Ramkhamhaeng Polder, Bangkok, Thailand

In urban areas of Thailand, and especially in Bangkok, recent flash floods have caused severe damage and prompted a renewed focus to manage their impacts. The development of a real-time warning system could provide timely information to initiate flood management protocols, thereby reducing impacts. Therefore, we developed an innovative real-time flood forecasting system (RTFlood system) and applied it to the Ramkhamhaeng polder in Bangkok, which is particularly vulnerable to flash floods. The RTFlood system consists of three modules. The first module prepared rainfall input data for subsequent use by a hydraulic model. This module used radar rainfall data measured by the Bangkok Metropolitan Administration and developed forecasts using the TITAN (Thunderstorm Identification, Tracking, Analysis, and Nowcasting) rainfall model. The second module provided a real-time task management system that controlled all processes in the RTFlood system, i.e., input data preparation, hydraulic simulation timing, and post-processing of the output data for presentation. The third module provided a model simulation applying the input data from the first and second modules to simulate flash floods. It used a dynamic, conceptual model (PCSWMM, Personal Computer version of the Stormwater Management Model) to represent the drainage systems of the target urban area and predict the inundation areas. The RTFlood system was applied to the Ramkhamhaeng polder to evaluate the system’s accuracy for 116 recent flash floods. The result showed that 61.2% of the flash floods were successfully predicted with accuracy high enough for appropriate pre-warning. Moreover, it indicated that the RTFlood system alerted inundation potential 20 min earlier than separate flood modeling using radar and local rain stations individually. The earlier alert made it possible to decide on explicit flood controls, including pump and canal gate operations.

A novel stochastic modelling approach for operational real-time ice-jam flood forecasting

Forecasting ice jams and their consequential flooding is more challenging than predicting open water flood conditions. This is due to the chaotic nature of ice jam formation since slight changes in water and ice flows, location of the ice jam toe along the river and initial water levels at the time of jam formation can lead to marked differences in the outcome of backwater level elevations and flood severity. In this paper, we introduce a novel, operational real-time flood forecasting system that captures this stochastic nature of ice-jam floods and places the forecasts in a probabilistic context in the form of flood hazard maps (probability of flood extents and depths). This novel system was tested successfully for the ice-cover breakup period in the spring of 2018 along the Athabasca River at the Town of Fort McMurray, Canada.

A Robust Skewed Boxplot for Detecting Outliers in Rainfall Observations in Real-Time Flood Forecasting

The standard boxplot is one of the most popular nonparametric tools for detecting outliers in univariate datasets. For Gaussian or symmetric distributions, the chance of data occurring outside of the standard boxplot fence is only 0.7%. However, for skewed data, such as telemetric rain observations in a real-time flood forecasting system, the probability is significantly higher. To overcome this problem, a medcouple (MC) that is robust to resisting outliers and sensitive to detecting skewness was introduced to construct a new robust skewed boxplot fence. Three types of boxplot fences related to MC were analyzed and compared, and the exponential function boxplot fence was selected. Operating on uncontaminated as well as simulated contaminated data, the results showed that the proposed method could produce a lower swamping rate and higher accuracy than the standard boxplot and semi-interquartile range boxplot. The outcomes of this study demonstrated that it is reasonable to use the new robust skewed boxplot method to detect outliers in skewed rain distributions.

Automated procedure of real-time flood forecasting in Vu Gia – Thu Bon river basin, Vietnam by integrating SWAT and HEC-RAS models

Abstract The precise and reliable simulation of hydrologic and hydraulic processes is important for efficient flood forecasting and warning. The study proposes a real-time flood forecasting system which integrates a coupled hydrological-hydraulic modeling system, weather station network, and stream gauges in a web-based visualization environment. An automated procedure was developed for linking dynamically terrestrial rainfall-runoff processes and river hydraulics by coupling the SWAT hydrological model and the HEC-RAS hydraulic model. The flood forecasting system was trialed in the Vu Gia – Thu Bon river basin, Quang Nam province, Vietnam. The results showed good statistical correlation between predicted and measured stream flow for a 10-year calibration period (R² = 0.95, NSI = 0.95, PBIAS = −1.54) and during the following 10-year validation period as well (R² = 0.93, NSI = 0.93, PBIAS = 6.18). A close-up analysis of individual storm events indicated that the magnitude and timing of peak floods were accurately predicted in 2015 (R² = 0.88, NSI = 0.69, PBIAS = 4.50) and 2016 (R² = 0.80, NSI = 0.93, PBIAS = 6.18). In addition, the automated procedure was demonstrated to be reliable with dependable computational efficiency of less than 5 minutes' processing time.

A Case Study for the Application of an Operational Two-Dimensional Real-Time Flooding Forecasting System and Smart Water Level Gauges on Roads in Tainan City, Taiwan

An operational two-dimensional real-time flood forecasting system has been developed to prevent urban inundation in Taiwan, and it uses the Delft-FEWS (Flood Early Warning System) platform to integrate the SOBEK models to forecast flooding. A new generation of smart water level gauges, integrating power charging, recording, transmission, and sensing into one, can monitor the inundation on roads in real-time. This study took Annan District in Tainan City, Taiwan, as the study object and presented the application of the flood forecasting system and smart water level gauges in the urban environment. The collected data from Storm 0611 and Typhoon Megi in 2016 were used to assess the accuracy of the flood forecasting system. The analysis of three water level stations in the drainage showed that the one-dimensional simulation results were fairly accurate. According to the 18 observed and simulated inundation depths on the roads, the true positive, false positive, false negative, and true negative values were 8, 0, 2, and 8, respectively. The accuracy, sensitivity, and precision of the flood forecasting system were 0.889, 0.800, and 1.000, respectively, indicating that the two-dimensional simulation results were highly accurate.

Improving the Computational Performance of an Operational Two-Dimensional Real-Time Flooding Forecasting System by Active-Cell and Multi-Grid Methods in Taichung City, Taiwan

An operational two-dimensional real-time flood forecasting system has been developed in Taiwan to prevent urban inundation. This system takes an hour to come up with forecasts for the next three hours, and the resolution of the forecasts is 40 × 40 m. This study used a large urban area of 126 km2 in downtown Taichung City for the case study and adopted the active-cell and multi-grid methods to meet the target by computing from data of a 12-h rainfall within one hour at 20 m × 20 m spatial resolution to provide faster forecasting and more hours for flood preparation. With the active-cell method, the Central Processing Unit (CPU) time was reduced by 65.04% from 659 m 29 s to 230 m 33 s under the 200-year return period storm. Further, with multi-grid methods, the CUP time was reduced by 73.98% from 230 m 33 s to 60 m 0 s. In general, the computing time of this model has been reduced 11-fold. The error validation coefficients of inundation areas were between 89.39~97.45% with an average error of depth between 1.06~3.22 cm.

Real-Time Flood Forecasting and Information System for the State of Iowa

Abstract The Iowa Flood Center (IFC), established following the 2008 record floods, has developed a real-time flood forecasting and information dissemination system for use by all Iowans. The system complements the operational forecasting issued by the National Weather Service, is based on sound scientific principles of flood genesis and spatial organization, and includes many technological advances. At its core is a continuous rainfall–runoff model based on landscape decomposition into hillslopes and channel links. Rainfall conversion to runoff is modeled through soil moisture accounting at hillslopes. Channel routing is based on a nonlinear representation of water velocity that considers the discharge amount as well as the upstream drainage area. Mathematically, the model represents a large system of ordinary differential equations organized to follow river network topology. The IFC also developed an efficient numerical solver suitable for high-performance computing architecture. The solver allows the IFC to update forecasts every 15 min for over 1,000 Iowa communities. The input to the system comes from a radar-rainfall algorithm, developed in-house, that maps rainfall every 5 min with high spatial resolution. The algorithm uses Level II radar reflectivity and other polarimetric data from the Weather Surveillance Radar-1988 Dual-Polarimetric (WSR-88DP) radar network. A large library of flood inundation maps and real-time river stage data from over 200 IFC “stream-stage sensors” complement the IFC information system. The system communicates all this information to the general public through a comprehensive browser-based and interactive platform. Streamflow forecasts and observations from Iowa can provide support for a similar system being developed at the National Water Center through model intercomparisons, diagnostic analyses, and product evaluations.

Real-Time Flood Forecasting System: Case Study of Hsia-Yun Watershed, Taiwan

AbstractTyphoons and heavy storms frequently cause severe inundation in Taiwan. For early flood warning purposes, a real-time flood forecasting system was acknowledged as a pivotal tool and developed in this study. The system integrates four parts: (1) a gray-based rainfall prediction model, (2) the antecedent hydrological condition estimation method, (3) a deterministic runoff model, and (4) a runoff updating algorithm. The developed system was applied to the Hsia-Yun watershed in Taiwan. The time-invariant parameters, which characterize terrain features, were calibrated in advance. Additionally the time-variant parameters were modeled to change with temporal hydrological condition of the watershed. The gray theory was performed to predict the rainfall intensity in the next 1–3 h, which thus enables the runoff model to forecast incoming runoff in the next 1–3 h. By consulting the real-time feedback flow observation, a runoff updating algorithm was adopted and was helpful in improving the performance of t...

A real-time flood forecasting system with dual updating of the NWP rainfall and the river flow

Numerical weather prediction (NWP) models are gaining more and more attention in providing high-resolution rainfall forecasts for real-time flood forecasting. In this study, the weather research and forecasting (WRF) model is integrated with the probability distribution model (PDM) to make real-time flow forecasts in a small catchment located in Southwest England. In order to improve the accuracy of the NWP rainfall and flow forecasts, dual real-time updating is carried out in the forecasting system through data assimilation. The three-dimensional variational data assimilation technique is coupled with the WRF model to assimilate radar reflectivity and traditional meteorological data; meanwhile, the autoregressive moving average model works with the rainfall–runoff model PDM to assimilate real-time flow observations. Four 24-h storm events with different characteristics of rainfall–runoff responses are selected from the study catchment to test the performance of the constructed forecasting system. The flood forecasting accuracy is found to be largely improved by incorporating the NWP forecasted rainfall when the lead time is beyond the catchment concentration time. The assimilation of radar and meteorological data also shows great advantage in improving the NWP rainfall forecasts.

남한강 지류 합류부의 실시간 홍수위 예측

상대적으로 홍수량의 규모가 작은 지류 하천에서는 합류부 배수영향으로 제내지 침수 및 제방 범람에 의한 홍수피해의 위험이 가중되고 있다. 특히 지류 합류부에서는 본류와 지류의 홍수유하 조건에 따라 수위가 급격히 증가하므로 인명피해의 가능성 또한 높다. 따라서 본 연구에서는 비구조적 홍수피해저감대책의 일환으로 지류 합류부의 실시간 홍수위 예측기술을 개발하고자 한다. 이를 위하여 지류 합류부 수위의 주요 영향인자를 검토하였고, 잘 구축된 수리학적 모형으로부터 계산된 본류 및 지류의 유량과 합류부 수위자료를 이용하여 홍수위 예측을 위한 경험식을 개발하였다. 개발된 식에 의한 예측결과는 최대 1.0m의 수위오차를 포함하고 있었으나, 평균 0.2~0.3m의 절대오차를 나타내었고, 발생시각은 0~5 hr 앞서 예측 가능한 것으로 나타났다. 본 연구결과로부터 홍수예측 시스템이 구축되지 않은 지류 합류부에서도 쉽게 실시간 홍수예측이 가능하며, 구축된 시스템은 지류의 홍수범람 및 침수피해 예방에 활용될 수 있을 것으로 기대된다. The backwater effect at a tributary junction increases the risk of flood damage such as inundation and levee overflow. In particular, the rapid increase in water level may cause injury to persons. The purpose of this research is the development of the real-time flood forecasting technique as a part of the non-structural flood damage reduction measures. To this end, the factors causing a water level rising at a junction were examined, and the empirical formula for predicting flood level at a junction was developed using the calculated discharge and water level data from the well-constructed hydraulic model. The water level predictions show that average absolute error is about 0.2~0.3m with the maximum error of 1.0m and peak time can be captured prior to 0~5 hr. From the results of this study, the real-time flood forecasting system of a tributary junction can be easily constructed, and this system is expected to be utilized for reduction of flood inundation damage.

Real-time flood forecasting system for complicated river channels: A case study from Wangjiaba to Xiaoliuxiang section in the Huaihe River basin

随着社会经济的快速发展,洪水灾害造成的损失日益严重.洪水预报作为一项重要的防洪非工程措施,对防洪、抗洪工作起着至关重要的作用.淮河洪水危害的严重性和洪水演进过程的复杂性使得淮河洪水预报系统的研究长期以来受到高度重视.本文以王家坝至小柳巷区间流域为例,以河道洪水演算为主线,采用新安江三水源模型进行子流域降雨径流预报,概化具有行蓄洪区的干流河道,进行支流与干流、行蓄洪区与干流的洪水汇流耦合计算,采用实时更新的基于多元回归的方法确定水位流量关系,并以上游站点降雨径流预报模型提供的流量作为上边界条件、以下游站点的水位流量关系作为下边界条件,结合行蓄洪调度模型,建立具有行蓄洪区的河道洪水预报系统,再与基于K-最近邻(KNN)的非参数实时校正模型耦合,建立淮河中游河道洪水预报系统.采用多年资料模拟取得了较好的预报效果,并以2003和2007年大洪水为例进行检验,模拟结果精度较高,也证明了所建预报系统的合理性和适用性.;The losses caused by flood disasters are becoming increasingly serious with the rapid development of social economy.As an important non-engineering flood protection measure,real-time flood forecasting plays an important role in flood control.Due to the severity of flood disasters and the complexity of flood routing process in the Huaihe River basin,the research on flood forecasting system has been a focus for a long period.A case study from Wangjiaba to Xiaoliuxiang section is discussed and a research on flood forecasting system is presented in this paper.On the basis of the hydrologic characteristics in the study area,this paper takes flood flow routing as mainline,uses the Xin'anjiang model for runoff yield prediction,generalizes flood diversion and retarding regions, applies hydrologic and hydraulic methods to simulate flow routing in the main channels,and fits stage-discharge relationships by multiple regression method.A general system is established by integrating the above parts and then by coupling K-Nearest Neighbor method to attach real-time correction to forecasting results for the real-time forecasting system.Inspected in 2003 and 2007,the system achieved high prediction accuracy,which proves that the system is sound and can be applied to this basin.

A conceptual and neural network model for real-time flood forecasting of the Tiber River in Rome

Rome is at risk from flooding when extreme events with a return period of about 200 years occur. For this reason, an accurate real-time flood forecasting system may be a useful non-structural countermeasure. Two different approaches are considered to develop a real-time forecasting system capable of predicting hourly water levels at Ripetta stream gauging station in Rome. The first is an adaptive, conceptual model (TFF model), which consists of a rainfall-runoff model that simulates the contribution of 41 ungauged sub-basins (covering approximately 30% of the catchment area) of the Tiber River and a hydraulic model to route the flood through the hydrographic network. The rainfall-runoff model is calibrated online during each flood event at every time step via an adaptive procedure while the flood routing model parameters were calibrated offline and held constant during the forecast. The second approach used is a data-driven one through the application of an artificial neural network (TNN model). Feedforward networks trained with backpropagation and Bayesian regularization were developed using a continuous historical dataset. Both models were used to forecast the most recent significant floods that occurred in Rome (November 2005 and December 2008) with lead times of 12 and 18 h. The results show good performance using both models when compared with observations for a series of absolute and relative performance measures as well as a visual inspection of the hydrographs. At present both models are suitable for real-time forecasting and the power of an integrated approach is still to be investigated.

Fuzzy Rule Based Models Modification by New Data: Application to Flood Flow Forecasting

Reconstruction and/or modification of an already existing fuzzy model with new data may improve system performances. As new data become available, adjusting the existing fuzzy rule-based model may present a challenging alternative to full model reconstruction. In this paper a fuzzy rule-based control model using a Takagi–Sugeno fuzzy system is presented and a model modification algorithm is developed which improves the performance of the initial model as new data become available. Proposed approach is applied to a flood flow forecasting case example and the results are compared with those forecasted using initially available and reconstructed models. Results show that the modified model outperforms the initial FRB model. Reconstructed model performs slightly better than the modified model; however, the reconstruction may not be justified in a real time flood forecasting system, considering the limitations on the available lead time.

Uncertainty propagation in a London flood simulation

Following recent costly flood events in the United Kingdom, there is considerable societal and political pressure to reduce flooding and improve warnings. In response to this, the Flood Risk Management Research Consortium (FRMRC) has been created to investigate the potential of several areas of existing research to be brought into operational use. In this paper, the estimation of flood impact and probability is analysed and illustrated with examples from a simulated forecast of a Thames Estuary flood event carried out at a FRMRC workshop. The forecast modelling chain consisted of meteorology, storm surge, estuary hydrodynamics, defence failure and inundation. The workshop concluded that end-to-end propagation of probability was feasible in an integrated real-time flood forecasting system, and that the basis of such a system had been demonstrated.

Design and testing of a real-time flood forecasting system for urban catchments

In the late afternoon on Friday 9 March 2001, a severe storm occurred on the south side of Brisbane. Peak intensity reached more than 210 mm/h, and total rainfall up to 200 mm occurred over a period of 3 hours. Extensive flooding was recorded at a number of locations in the Norman Creek catchment. This and other localised storm events have highlighted the need for real-time flood forecasting to increase the awareness of and preparedness for flash floods in urbanised areas, and to minimise the damage and disruption as a result of flash floods in the tropical and sub-tropical environments. A real-time forecasting system is developed using ensemble probability-based rainfall forecasts coupled with an efficient raster-based hydrologic model to forecast water levels at critical locations for dissemination on the internet. Forecasting of flash flood involves interrogating ALERT/telemetry data on gauge rainfall and water levels in the catchment, processing and interpreting radar scans to determine the storm’s spatial structure and movement, numerical weather prediction up to 30–90 min into the future, generating multiple realisations of likely rain fields, predicting multiple hydrographs from forecast rain fields, and converting hydrographs into probability-based water level forecasts at critical locations in the catchment. The design is critically constrained by the requirement to update the forecast on the internet every 15 minutes. The paper reports a retrospective test using the gauge and radar data for the March 2001 event in the 28 km2 Norman Creek catchment.

Evaluation of hydrologic and hydraulic models for real-time flood forecasting use in the Yangtze River catchment

A key component of the Yangtze River Flood Control and Management Project is the development of an integrated real-time flood forecasting system to improve the reliability, accuracy and lead times of forecast flood discharges and flood levels along the upper and middle reaches of the Yangtze River. Central to the flood forecasting system is the use of appropriate hydrologic and hydraulic models. As part of the hydrologic and hydraulic model evaluation and selection process, a comprehensive comparative study was carried out for five well established hydrologic models and three well established hydraulic models to assess their performance and suitability for use in real-time forecasting in the Yangtze River catchment. The model evaluations were undertaken using common data sets and technical performance criteria specified in the Chinese National Standards for Flood Forecasting Models. This paper describes how the different hydrologic and hydraulic model performances were evaluated and presents the findings on their suitability for use in the Yangtze River flood forecasting system.

Operational hydro-meteorological warning and real-time flood forecasting: the Piemonte Region case study

Abstract. The development and implementation of a real-time flood forecasting system in the context of the Piemonte Region's hydro-meteorological operational alert procedure is described. The area of interest is the Upper Po River basin (north-west Italy) of approximately 37000 km2 and its river network of about 3000 km and three big lakes. FloodWatch, a GIS-based decision support system for real-time flood forecasting, has been developed and used operationally at the Piemonte Region's Room for the Situation of Natural Hazards in Torino, Italy, since January 2000. The system is linked directly to the telemetric gauges system, uses daily quantitative precipitation and temperature forecasts issued by the Regional Meteorological Service and automatically supplies operational forecasts of water-level and discharge at about 30 locations for up to 48 hours. Strengths and limits of the system and its link with operational flood alert and management are discussed. The case study presented is the October 2000 flood event, when the north-west of Italy experienced one of the largest floods on record. Results highlight how the uncertainty linked to the use of meteorological forecasts greatly influences the quality of the hydrological forecasts. The proposed alert procedure, based on coded risk levels, can help effectively in facing forecast uncertainties.

Assessing urban hydrologic prediction accuracy through event reconstruction

Quantifying hydrologic prediction error is of fundamental importance to feasible operation of a real-time flood forecasting system customized for specific locations. Reconstruction of a series of storm events using gauge-adjusted radar provides information on prediction accuracies that may be achievable in real-time operations. The subject of this paper is the examination of hydrologic prediction uncertainty for a system providing site-specific flood forecasts in a heavily urbanized area. Reconstruction of Tropical Storm Allison that occurred in June 2001 and other events are used to calibrate and validate a fully distributed hydrologic model that is used for real-time continuous forecasting operations. Hydrologic prediction accuracy expected during real-time forecasting of urban flooding is shown to be sensitive to radar rainfall bias. Controlling for bias in the radar input for five storms, achievable model accuracy is approximately 11.8% in peak discharge, 12 min in timing, and 11.1% in runoff volume for a 260 km2 urban catchment. High-accuracy rising limb agreement is achieved through differential adjustment of overland and channel hydraulic roughness. Verification during a recent real-time event illustrates the need for bias adjustment of radar in real-time to achieve acceptable accuracies. Once the radar is gauge-corrected, remaining random error does not correlate strongly with the error in streamflow predictions. This lack of correlation indicates that the random error measured by radar-gauge comparison is diminished, not amplified, when simulated runoff is compared with observed streamflow. Performance evaluation during operations will be accomplished in the future as sufficient events become available for reliable statistical evaluation.