Developing a Web-based flood forecasting system for reservoirs with J2EE / Développement sur Internet avec J2EE d’un système de prévision de crue pour barrages

2004 ◽  
Vol 49 (6) ◽  
Author(s):  
Chun-Tian Cheng ◽  
K. W. Chau ◽  
Xiang-Yang Li ◽  
Gang Li
Keyword(s):  
Flood Forecasting ◽  
Web Based ◽  
Forecasting System

A Web-based flood forecasting system for Shuangpai region

2006 ◽  
Vol 37 (3) ◽  
pp. 146-158 ◽  
Author(s):  
Xiang-Yang Li ◽  
K.W. Chau ◽  
Chun-Tian Cheng ◽  
Y.S. Li
Keyword(s):  
Flood Forecasting ◽  
Web Based ◽  
Forecasting System

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

2018 ◽  
Vol 10 (3) ◽  
pp. 535-545 ◽  
Author(s):  
Nguyen Kim Loi ◽  
Nguyen Duy Liem ◽  
Le Hoang Tu ◽  
Nguyen Thi Hong ◽  
Cao Duy Truong ◽  
...  
Keyword(s):  
Real Time ◽  
River Basin ◽  
Flood Forecasting ◽  
Hydraulic Modeling ◽  
Statistical Correlation ◽  
Storm Events ◽  
Web Based ◽  
River Hydraulics ◽  
Forecasting System ◽  
Forecasting And Warning

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.

scholarly journals A pan-African medium-range ensemble flood forecast system

2015 ◽  
Vol 19 (8) ◽  
pp. 3365-3385 ◽  
Author(s):  
V. Thiemig ◽  
B. Bisselink ◽  
F. Pappenberger ◽  
J. Thielen
Keyword(s):  
Large Scale ◽  
Practical Implication ◽  
River Basins ◽  
Flood Forecasting ◽  
Small Scale ◽  
Flood Forecast ◽  
Flood Events ◽  
Forecast System ◽  
Pan African ◽  
Forecasting System

Abstract. The African Flood Forecasting System (AFFS) is a probabilistic flood forecast system for medium- to large-scale African river basins, with lead times of up to 15 days. The key components are the hydrological model LISFLOOD, the African GIS database, the meteorological ensemble predictions by the ECMWF (European Centre for Medium-Ranged Weather Forecasts) and critical hydrological thresholds. In this paper, the predictive capability is investigated in a hindcast mode, by reproducing hydrological predictions for the year 2003 when important floods were observed. Results were verified by ground measurements of 36 sub-catchments as well as by reports of various flood archives. Results showed that AFFS detected around 70 % of the reported flood events correctly. In particular, the system showed good performance in predicting riverine flood events of long duration (> 1 week) and large affected areas (> 10 000 km2) well in advance, whereas AFFS showed limitations for small-scale and short duration flood events. The case study for the flood event in March 2003 in the Sabi Basin (Zimbabwe) illustrated the good performance of AFFS in forecasting timing and severity of the floods, gave an example of the clear and concise output products, and showed that the system is capable of producing flood warnings even in ungauged river basins. Hence, from a technical perspective, AFFS shows a large potential as an operational pan-African flood forecasting system, although issues related to the practical implication will still need to be investigated.

Development of a Real-Time Flood Forecasting System by Hydraulic Flood Routing

2001 ◽  
Author(s):  
Joo Heon Lee ◽  
Do Hun Lee ◽  
Sang Man Jeong ◽  
Eun Tae Lee
Keyword(s):  
Real Time ◽  
Flood Forecasting ◽  
Flood Routing ◽  
Forecasting System

Measuring the skill of an operational ice jam flood forecasting system

2021 ◽  
Vol 52 ◽  
pp. 102001
Author(s):  
Brandon S. Williams ◽  
Apurba Das ◽  
Peter Johnston ◽  
Bin Luo ◽  
Karl-Erich Lindenschmidt
Keyword(s):  
Flood Forecasting ◽  
Ice Jam ◽  
Forecasting System

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

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Chao Zhao ◽  
Jinyan Yang
Keyword(s):  
Real Time ◽  
Exponential Function ◽  
Flood Forecasting ◽  
Interquartile Range ◽  
Skewed Data ◽  
Symmetric Distributions ◽  
Contaminated Data ◽  
Forecasting System

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.

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