Development and application of a real-time flood forecasting system in the Veneto region of Italy

1991 ◽  
Vol 5 (3-4) ◽  
pp. 209-216 ◽  
Author(s):  
M. Borga ◽  
A. Capovilla ◽  
F. Cazorzi ◽  
S. Fattorelli
Keyword(s):  
Real Time ◽  
Flood Forecasting ◽  
Veneto Region ◽  
Forecasting System

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.

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

2016 ◽  
Vol 21 (4) ◽  
pp. 05015031 ◽  
Author(s):  
Jen-Kuo Huang ◽  
Ya-Hsin Chan ◽  
Kwan Tun Lee
Keyword(s):  
Real Time ◽  
Flood Forecasting ◽  
Forecasting System

scholarly journals Case Study: A Real-Time Flood Forecasting System with Predictive Uncertainty Estimation for the Godavari River, India

Water ◽  
2016 ◽  
Vol 8 (10) ◽  
pp. 463 ◽  
Author(s):  
Silvia Barbetta ◽  
Gabriele Coccia ◽  
Tommaso Moramarco ◽  
Ezio Todini
Keyword(s):  
Real Time ◽  
Flood Forecasting ◽  
Uncertainty Estimation ◽  
Predictive Uncertainty ◽  
Godavari River ◽  
Forecasting System

Development of the Deltares global fluvial flood forecast system

2020 ◽  
Author(s):  
Matthijs den Toom ◽  
Jan Verkade ◽  
Albrecht Weerts ◽  
Gert-Jan Schotmeijer
Keyword(s):  
Real Time ◽  
Flood Forecasting ◽  
Pedotransfer Functions ◽  
Water Runoff ◽  
Multiple Model ◽  
Recent Past ◽  
Seasonal Forecasts ◽  
Hydrological Parameters ◽  
Forecasting System ◽  
Near Future

<p>Deltares operates the Global Fluvial Flood Forecasting System (GLOFFIS) is a real-time fluvial forecasting system with global coverage. At any location in the world, for both the recent past and the near future, the system produces estimates of various hydrological parameters. </p><p>The continued investment in GLOFFIS is justified by various reasons. Primarily, there is an R&D rationale. Any operational system that runs in near real-time poses high requirements to the availability of input data and the runtime of the models used. This problem is augmented when applying the system to the global scale: both model domains and data volumes become significantly larger. Also, data originates from a wide variety of sources. Runtimes, however, cannot be significantly larger hence this poses additional requirements to the efficiency of models used. Solving these issues requires a considerable R&D effort. The resulting developments tend to be useful for the ‘local’ systems we develop and maintain for our clients. An additional rationale is found in the increased demand for global forecasts – notably from a client base that is not able or willing to operate forecasting systems themselves. </p><p>At its core, GLOFFIS operates a set of hydrological models that, jointly, cover the entire earth’s land. The models are forced by meteorological data – pertaining to both the recent past and the near future. The models produce estimates of various hydrological parameters such as soil moisture content, surface water runoff and streamflow rates. Future versions of GLOFFIS will include hydrodynamic models, allowing to produce estimates of water level in addition to streamflow rates. Also, future versions will include seasonal forecasts, i.e. forecasts going out several weeks if not months. In addition to real-time data, the system enables the production of long-term timeseries. </p><p>In terms of the infrastructure of the system, GLOFFIS is based on the wflow framework for hydrological modelling which is embedded within a the Delft-FEWS forecast production system. Neither of these require any licensing fees and the wflow framework is available through an open source license. The Delft-FEWS system is used for many operational flood forecasting systems including those of the US National Weather Service, the English Environment Agency, the Bureau of Meteorology and many other national forecasting agencies. Wflow is a distributed modelling framework specifically designed to accommodate multiple model schematization types and data assimilation techniques. For GLOFFIS, we opted for the physically based wflow_sbm that uses kinematic wave routing for surface and subsurface flow. So-called pedotransfer functions that translate input base maps to model parameter values ensure that the models require little calibration.</p>

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