Atmospheric Model Component of an Atmospheric-Hydrological Model-Based Real-Time Flood Forecasting System for the Kızılırmak River Basin in Turkey

2016 ◽  
Author(s):  
K. Ishida ◽  
A. Ercan ◽  
R. Chen ◽  
N. Ohara ◽  
M. L. Kavvas ◽  
...  
Keyword(s):  
Real Time ◽  
River Basin ◽  
Hydrological Model ◽  
Atmospheric Model ◽  
Flood Forecasting ◽  
Model Based ◽  
Model Component ◽  
Forecasting System

scholarly journals Accuracy assessment of real-time flood forecasting of coupled hydrological and mesoscale meteorological models

2018 ◽  
Author(s):  
Aida Jabbari ◽  
Jae-Min So ◽  
Deg-Hyo Bae
Keyword(s):  
Real Time ◽  
Spatial Resolution ◽  
Lead Time ◽  
Hydrological Model ◽  
Weather Prediction ◽  
Atmospheric Model ◽  
Flood Forecasting ◽  
Coupled Models ◽  
Temporal And Spatial ◽  
Meteorological Models

Abstract. Hydro-meteorological predictions are important for water management plans, which include providing early flood warnings and preventing flood damages. This study evaluates the real-time precipitation of an atmospheric model at the point and catchment scales to select the proper hydrological model to couple with the atmospheric model. Furthermore, a variety of tests were conducted to quantify the accuracy assessments of coupled models to provide details on the maximum spatial and temporal resolutions and lead times in a real-time forecasting system. As a major limitation of previous studies, the temporal and spatial resolutions of the hydrological model are smaller than those of the meteorological model. Here, through ultra-fine scale of temporal (10 min) and spatial resolution (1 km × 1 km), we determined the optimal resolution. A numerical weather prediction model and a rainfall runoff model were employed to evaluate real-time flood forecasting for the Imjin River (South and North Korea). The comparison of the forecasted precipitation and the observed precipitation indicated that the Weather Research and Forecasting (WRF) model underestimated precipitation. The skill of the model was relatively higher for the catchment than for the point scale, as illustrated by the lower RMSE value, which is important for a semi-distributed hydrological model. The variations in temporal and spatial resolutions illustrated a decrease in accuracy; additionally, the optimal spatial resolution obtained at 8 km and the temporal resolution did not affect the inherent inaccuracy of the results. Lead time variation demonstrated that lead time dependency was almost negligible below 36 h. With reference to our case study, comparisons of model performance provided quantitative knowledge for understanding the credibility and restrictions of hydro-meteorological models.

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.

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

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.

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.

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