scholarly journals WATFLOOD: a Micro-Computer Based Flood Forecasting System Based on Real-Time Weather Radar

1988 ◽  
Vol 13 (1) ◽  
pp. 62-77 ◽  
Author(s):  
Nicholas Kouwen
Keyword(s):  
Real Time ◽  
Weather Radar ◽  
Flood Forecasting ◽  
Computer Based ◽  
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.

scholarly journals A grid-based distributed flood forecasting model for use with weather radar data: Part 1. Formulation

1998 ◽  
Vol 2 (2/3) ◽  
pp. 265-281 ◽  
Author(s):  
V. A. Bell ◽  
R. J. Moore
Keyword(s):  
Real Time ◽  
Weather Radar ◽  
Radar Data ◽  
Flood Forecasting ◽  
Absorption Capacity ◽  
Model Parameters ◽  
Grid Model ◽  
Basic Model ◽  
Digital Terrain ◽  
Runoff Production

Abstract. A practical methodology for distributed rainfall-runoff modelling using grid square weather radar data is developed for use in real-time flood forecasting. The model, called the Grid Model, is configured so as to share the same grid as used by the weather radar, thereby exploiting the distributed rainfall estimates to the full. Each grid square in the catchment is conceptualised as a storage which receives water as precipitation and generates water by overflow and drainage. This water is routed across the catchment using isochrone pathways. These are derived from a digital terrain model assuming two fixed velocities of travel for land and river pathways which are regarded as model parameters to be optimised. Translation of water between isochrones is achieved using a discrete kinematic routing procedure, parameterised through a single dimensionless wave speed parameter, which advects the water and incorporates diffusion effects through the discrete space-time formulation. The basic model routes overflow and drainage separately through a parallel system of kinematic routing reaches, characterised by different wave speeds but using the same isochrone-based space discretisation; these represent fast and slow pathways to the basin outlet, respectively. A variant allows the slow pathway to have separate isochrones calculated using Darcy velocities controlled by the hydraulic gradient as estimated by the local gradient of the terrain. Runoff production within a grid square is controlled by its absorption capacity which is parameterised through a simple linkage function to the mean gradient in the square, as calculated from digital terrain data. This allows absorption capacity to be specified differently for every grid square in the catchment through the use of only two regional parameters and a DTM measurement of mean gradient for each square. An extension of this basic idea to consider the distribution of gradient within the square leads analytically to a Pareto distribution of absorption capacity, given a power distribution of gradient within the square. The probability-distributed model theory (Moore, 1985) can then be used directly to obtain the integrated runoff production for the square for routing to the catchment outlet. justification for the simple linkage function is in part sought through consideration of variants on the basic model where (i) runoff production is based on a topographic index control on saturation and (ii) absorption capacity is related to the Integrated Air Capacity of the soil, as obtained from soil survey. An impervious area fraction is also introduced based on the use of Landsat classified urban areas. The Grid Model and its variants are assessed in Part 2 (Bell and Moore, 1998), first as simulation models and then as forecasting models, following the development of updating procedures to accommodate recent observations of flow so as to improve forecast performance in a real-time context.

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

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

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
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