scholarly journals Real-time flood stage forecasting by Variable Parameter Muskingum Stage hydrograph routing method

2011 ◽  
Vol 42 (2-3) ◽  
pp. 150-161 ◽  
Author(s):  
Muthiah Perumal ◽  
Tommaso Moramarco ◽  
Silvia Barbetta ◽  
Florisa Melone ◽  
Bhabagrahi Sahoo
Keyword(s):  
Real Time ◽  
Forecast Error ◽  
Central Italy ◽  
Variable Parameter ◽  
Warning System ◽  
Forecast Model ◽  
Flood Forecasting ◽  
Time Interval ◽  
Tiber River ◽  
Routing Method

The application of a Variable Parameter Muskingum Stage (VPMS) hydrograph routing method for real-time flood forecasting at a river gauging site is demonstrated in this study. The forecast error is estimated using a two-parameter linear autoregressive model with its parameters updated at every routing time interval of 30 minutes at which the stage observations are made. This hydrometric data-based forecast model is applied for forecasting floods at the downstream end of a 15 km reach of the Tiber River in Central Italy. The study reveals that the proposed approach is able to provide reliable forecast of flood estimate for different lead times subject to a maximum lead time nearly equal to the travel time of the flood wave within the selected routing reach. Moreover, a comparative study of the VPMS method for real-time forecasting and the simple stage forecasting model (STAFOM), currently in operation as the Flood Forecasting and Warning System in the Upper-Middle Tiber River basin of Italy, demonstrates the capability of the VPMS model for its field use.

scholarly journals Flood Forecasting and Warning System Structures: Procedure and Application to a Small Urban Stream in South Korea

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1571 ◽  
Author(s):  
Song ◽  
Park ◽  
Lee ◽  
Park ◽  
Song
Keyword(s):  
Decision Making ◽  
Water Level ◽  
Real Time ◽  
Lead Time ◽  
Flash Flood ◽  
Warning System ◽  
Flood Forecasting ◽  
Urban Stream ◽  
Forecasting And Warning ◽  
Flood Warnings

The runoff from heavy rainfall reaches urban streams quickly, causing them to rise rapidly. It is therefore of great importance to provide sufficient lead time for evacuation planning and decision making. An efficient flood forecasting and warning method is crucial for ensuring adequate lead time. With this objective, this paper proposes an analysis method for a flood forecasting and warning system, and establishes the criteria for issuing urban-stream flash flood warnings based on the amount of rainfall to allow sufficient lead time. The proposed methodology is a nonstructural approach to flood prediction and risk reduction. It considers water level fluctuations during a rainfall event and estimates the upstream (alert point) and downstream (confluence) water levels for water level analysis based on the rainfall intensity and duration. We also investigate the rainfall/runoff and flow rate/water level relationships using the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) and the HEC’s River Analysis System (HEC-RAS) models, respectively, and estimate the rainfall threshold for issuing flash flood warnings depending on the backwater state based on actual watershed conditions. We present a methodology for issuing flash flood warnings at a critical point by considering the effects of fluctuations in various backwater conditions in real time, which will provide practical support for decision making by disaster protection workers. The results are compared with real-time water level observations of the Dorim Stream. Finally, we verify the validity of the flash flood warning criteria by comparing the predicted values with the observed values and performing validity analysis.

scholarly journals Improving probabilistic flood forecasting through a data assimilation scheme based on genetic programming

2012 ◽  
Vol 12 (12) ◽  
pp. 3719-3732 ◽  
Author(s):  
L. Mediero ◽  
L. Garrote ◽  
A. Chavez-Jimenez
Keyword(s):  
Data Assimilation ◽  
Genetic Programming ◽  
Real Time ◽  
High Performance ◽  
Forecast Model ◽  
Flood Forecasting ◽  
Distribution Functions ◽  
Model Parameters ◽  
Hydrological Process ◽  
Model Based

Abstract. Opportunities offered by high performance computing provide a significant degree of promise in the enhancement of the performance of real-time flood forecasting systems. In this paper, a real-time framework for probabilistic flood forecasting through data assimilation is presented. The distributed rainfall-runoff real-time interactive basin simulator (RIBS) model is selected to simulate the hydrological process in the basin. Although the RIBS model is deterministic, it is run in a probabilistic way through the results of calibration developed in a previous work performed by the authors that identifies the probability distribution functions that best characterise the most relevant model parameters. Adaptive techniques improve the result of flood forecasts because the model can be adapted to observations in real time as new information is available. The new adaptive forecast model based on genetic programming as a data assimilation technique is compared with the previously developed flood forecast model based on the calibration results. Both models are probabilistic as they generate an ensemble of hydrographs, taking the different uncertainties inherent in any forecast process into account. The Manzanares River basin was selected as a case study, with the process being computationally intensive as it requires simulation of many replicas of the ensemble in real time.

Real-time flood forecasting of the Tiber river in Rome

2008 ◽  
Vol 50 (3) ◽  
pp. 461-477 ◽  
Author(s):  
Benedetto Calvo ◽  
Fabrizio Savi
Keyword(s):  
Real Time ◽  
Flood Forecasting ◽  
Tiber River

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

2010 ◽  
Vol 35 (3-5) ◽  
pp. 187-194 ◽  
Author(s):  
G. Napolitano ◽  
L. See ◽  
B. Calvo ◽  
F. Savi ◽  
A. Heppenstall
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Network Model ◽  
Neural Network Model ◽  
Flood Forecasting ◽  
Tiber River

Real-Time Associative Memory–Based Rear-End Collision Warning System

2017 ◽  
Vol 2621 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Donghoun Lee ◽  
Sehyun Tak ◽  
Sungjin Park ◽  
Hwasoo Yeo
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Associative Memory ◽  
Intelligent Transportation System ◽  
Warning System ◽  
Time Interval ◽  
Associative Memories ◽  
Collision Warning ◽  
Sampling Ratio ◽  
Artificial Neural Network Ann

In the intelligent transportation system field, there has been a growing interest in developing collision warning systems based on artificial neural network (ANN) techniques in an effort to address several issues associated with parametric approaches. Previous ANN-based collision warning algorithms were generally based on predetermined associative memories derived before driving. Because collision risk is highly related to the current traffic situation, such as traffic state transition from free flow to congestion, however, updating associative memory in real time should be considered. To improve further the performance of the warning system, a systemic architecture is proposed to implement the multilayer perceptron neural network–based rear-end collision warning system (MCWS), which updates the associative memory with the vehicle distance sensor and smartphone data in a cloud computing environment. For the practical use of the proposed MCWS, its collision warning accuracy is evaluated with respect to various time intervals for updating the associative memories and market penetration rates. Results show that the MCWS exhibits a steady improvement in its warning performance as the time interval decreases, whereas the MCWS works more efficiently as the sampling ratio increases overall. When the sampling ratio reaches 50%, the MCWS shows a particularly stable warning accuracy, regardless of the time interval. These findings suggest that the MCWS has great potential to provide an acceptable level of warning accuracy for practical use, as it can obtain the well-trained associative memories reflecting current traffic situations by using information from widespread smartphones.

scholarly journals System response curve correction method of runoff error for real-time flood forecast

2020 ◽  
Vol 51 (6) ◽  
pp. 1312-1331
Author(s):  
Qian Li ◽  
Caisong Li ◽  
Huanfei Yu ◽  
Jinglin Qian ◽  
Linlin Hu ◽  
...  
Keyword(s):  
Real Time ◽  
River Basin ◽  
Forecast Model ◽  
Flood Forecasting ◽  
Correction Method ◽  
System Response ◽  
Flood Forecast ◽  
Feedback Correction ◽  
Underlying Surface Conditions ◽  
Source Of Information

Abstract Multiple factors including rainfall and underlying surface conditions make river basin real-time flood forecasting very challenging. It is often necessary to use real-time correction techniques to modify the forecasting results so that they reach satisfactory accuracy. There are many such techniques in use today; however, they tend to have weak physical conceptual basis, relatively short forecast periods, unsatisfactory correction effects, and other problems. The mechanism that affects real-time flood forecasting error is very complicated. The strongest influencing factors corresponding to this mechanism affect the runoff yield of the forecast model. This paper proposes a feedback correction algorithm that traces back to the source of information, namely, modifies the watershed runoff. The runoff yield error is investigated using the principle of least squares estimation. A unit hydrograph is introduced into the real-time flood forecast correction; a feedback correction model that traces back to the source of information. The model is established and verified by comparison with an ideal model. The correction effects of the runoff yield errors are also compared in different ranges. The proposed method shows stronger correction effect and enhanced prediction accuracy than the traditional method. It is also simple in structure and has a clear physical concept without requiring added parameters or forecast period truncation. It is readily applicable in actual river basin flood forecasting scenarios.

Building the Flood Early Warning System in Guyana at the National scale, with real-time forecast of inundated areas for selected flood prone communities.

2020 ◽  
Author(s):  
Alessandro Masoero ◽  
Imra Hodzic ◽  
Colis Allen ◽  
Andrea Libertino ◽  
Andrea Giusti ◽  
...  
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Flood Forecasting ◽  
Local Scale ◽  
Civil Defense ◽  
Data Availability ◽  
Inundation Maps ◽  
Extreme Flood

<p>Within the framework of the project “Strengthening Disaster Management Capacity of Women in Guyana and Dominica”, the National Flood Early Warning System (NFEWS) for Guyana is currently under development. The technical component of the system aims at implementing an operational flood forecasting modelling chain linking meteorological, hydrological and inundation models to provide timely early warnings and predicted flood scenarios, allowing the decision maker to take prevention actions and reduce the impacts of the forecasted event.</p><p>The objective is to implement, together with the Hydromet Service of Guyana, a technical tool able to provide daily forecasts of extreme flood events 1 to seven 7 days in advance, up to the local scale of inundation maps for selected locations.</p><p>The forecasting chain implemented is composed of five (5) main components: i) the weather forecasts, using the limited area WRF model executed twice a day at Hydromet; ii) observational inputs preparation, in particular rain maps through conditional merging between local ground stations and satellite information; iii) rainfall downscaling in several equiprobable scenarios using RAINFARM stochastic model; iv) the distributed hydrological model CONTINUUM, able to estimate river discharge and soil moisture conditions from the meteorological inputs (observation and forecasts), and v)the hydraulic model HYDRA-2D, that using a simplification of the shallow water equations allows fast and reliable inundation mapping.</p><p>At four (4) selected locations, corresponding to relevant flood-prone communities in Guyana, an innovative coupling between the hydrological and the inundation models allows to trigger an operational execution of several hydraulic simulations, resulting in real-time probabilistic forecast of inundation maps. The outflow volumes, derived from CONTINUUM hydrological routing, for different rainfall scenarios are used as inflow inputs for HYDRA-2D. Scalability between hydrological (1.5km) and hydraulic (12m) scales has been achieved through detailed field data collection, that was also used, together with local knowledge, to calibrate the inundation model.</p><p>Through the complete flood forecasting chain set up for Guyana, probability of exceeding significant water depths can be provided in advance to involved stakeholders, triggering early actions and thus enhancing flood resilience at the local scale.</p><p>The hydrological component of the forecasting chain has been implemented at the national level for the whole country, at a feasible spatial and temporal resolution based on a balance between input data availability and expected response time for civil defense activities.</p><p>Being developed using an open source model, as for all the other elements of the forecasting system, the hydraulic modelling component can be, in future, extended and replicated in other areas of interests.</p>

scholarly journals A data based mechanistic real-time flood forecasting module for NFFS FEWS

2012 ◽  
Vol 9 (6) ◽  
pp. 7271-7296 ◽  
Author(s):  
D. Leedal ◽  
A. H. Weerts ◽  
P. J. Smith ◽  
K. J. Beven
Keyword(s):  
Real Time ◽  
Forecast Model ◽  
Open Shell ◽  
Flood Forecasting ◽  
Time Data ◽  
Flood Alert ◽  
Interaction Interface ◽  
Forecast Models ◽  
Forecasting System ◽  
The Uk

Abstract. The data based mechanistic (DBM) approach for identifying and estimating rainfall to level, and level to level models has been shown to perform well for flood forecasting in several studies. The DELFT-FEWS open shell operational flood forecasting system provides a framework linking hydrological/meteorological real-time data, real-time forecast models, and a human/computer interaction interface. This infrastructure is used by the UK National Flood Forecasting System (NFFS) and the European Flood Alert System (EFAS) among others. The open shell nature of the FEWS framework has been specifically designed to make it easy to add new forecasting models written as FEWS modules. This paper shows the development of the DBM forecast model as a FEWS module and presents results for the Eden catchment (Cumbria UK) as a case study.

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